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Theorem proving in mathematics faces growing challenges due to increasing proof complexity. Formalized systems like Lean, Isabelle, and Coq offer computer-verifiable proofs, but creating these demands substantial human effort. Large language models (LLMs) show promise in solving high-school-level math problems using proof assistants, yet their performance still needs to improve due to data scarcity. Formal languages require significant expertise, resulting in limited corpora. Unlike conventional programming languages, formal proof languages contain hidden intermediate information, making raw language corpora unsuitable for training. This scarcity persists despite the existence of valuable human-written corpora. Auto-formalization efforts, while helpful, cannot fully substitute human-crafted data in quality and diversity.

Existing attempts to address theorem-proving challenges have evolved significantly with modern proof assistants like Coq, Isabelle, and Lean having expanded formal systems beyond first-order logic, increasing interest in automated theorem proving (ATP). The recent integration of large language models has further advanced this field. Early ATP approaches used traditional methods like KNN or GNN, with some employing reinforcement learning. Recent efforts utilize deep transformer-based methods, treating theorems as plain text. Many learning-based systems (e.g., GPT-f, PACT, Llemma) train language models on (proof state, next-tactic) pairs and use tree search for theorem proving. Alternative approaches involve LLMs generating entire proofs independently or based on human-provided proofs. Data extraction tools are crucial for ATP, capturing intermediate states invisible in code but visible during runtime. Tools exist for various proof assistants, but Lean 4 tools face challenges in massive extraction across multiple projects due to single-project design limitations. Some methods also explore incorporating informal proofs into formal proofs, broadening the scope of ATP research.

Researchers from The Chinese University of Hong Kong propose LEAN-GitHub, a large-scale Lean dataset that complements the well-utilized Mathlib dataset. This innovative approach provides an open-source Lean repositories on GitHub, significantly expanding the available data for training theorem-proving models. The researchers developed a scalable pipeline to enhance extraction efficiency and parallelism, enabling the exploitation of valuable data from previously uncompiled and unextracted Lean corpus. Also, they provide a solution to the state duplication problem common in tree-proof search methods.

The LEAN-GitHub dataset construction process involved several key steps and innovations:

  1. Repository Selection: The researchers identified 237 Lean 4 repositories  (GitHub does not differentiate between Lean 3 and Lean 4) on GitHub, estimating approximately 48,091 theorems. After discarding 90 repositories with deprecated Lean 4 versions, 147 remained. Only 61 of these could be compiled without modifications.
  2. Compilation Challenges: The team developed automated scripts to find the closest official releases for projects using non-official Lean 4 versions. They also addressed the issue of isolated files within empty Lean projects.
  3. Source Code Compilation: Instead of using the Lake tool, they called the Leanc compiler directly. This approach allowed for compiling non-compliant Lean projects and isolated files, which Lake couldn’t handle. They extended Lake’s import graph and created a custom compiling script with increased parallelism.
  4. Extraction Process: Building upon LeanDojo, the team implemented data extraction for isolated files and restructured the implementation to increase parallelism. This approach overcame bottlenecks in network connection and computational redundancies.
  5. Results: Out of 8,639 Lean source files, 6,352 and 42,000 theorems were successfully extracted. The final dataset includes 2,133 files and 28,000 theorems with valid tactic information.

The resulting LEAN-GitHub dataset is diverse, covering various mathematical fields including logic, first-order logic, matroid theory, and arithmetic. It contains cutting-edge mathematical topics, data structures, and Olympiad-level problems. Compared to existing datasets, LEAN-GitHub offers a unique combination of human-written content, intermediate states, and diverse complexity levels, making it a valuable resource for advancing automated theorem proving and formal mathematics.

InternLM2-StepProver, trained on the diverse LEAN-GitHub dataset, demonstrates exceptional formal reasoning abilities across various benchmarks. It achieves state-of-the-art performance on miniF2F (63.9% on Valid, 54.5% on Test), surpassing previous models. On ProofNet, it attains an 18.1% Pass@1 rate, outperforming the previous leader. For PutnamBench, it solves 5 problems in a single pass, including the previously unsolved Putnam 1988 B2. These results span high-school to advanced undergraduate-level mathematics, showcasing InternLM2-StepProver’s versatility and the effectiveness of the LEAN-GitHub dataset in training advanced theorem-proving models.

LEAN-GitHub, a large-scale dataset extracted from open Lean 4 repositories, contains 28,597 theorems and 218,866 tactics. This diverse dataset was used to train InternLM2-StepProver, achieving state-of-the-art performance in Lean 4 formal reasoning. Models trained on LEAN-GitHub demonstrate improved performance across various mathematical fields and difficulty levels, highlighting the dataset’s effectiveness in enhancing reasoning capabilities. By open-sourcing LEAN-GitHub, the researchers aim to help the community better utilize under-exploited information in raw corpora and advance mathematical reasoning. This contribution could significantly accelerate progress in automated theorem proving and formal mathematics.

Check out the Paper and Dataset. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup. If you like our work, you will love our newsletter..

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Asjad is an intern consultant at Marktechpost. He is persuing B.Tech in mechanical engineering at the Indian Institute of Technology, Kharagpur. Asjad is a Machine learning and deep learning enthusiast who is always researching the applications of machine learning in healthcare.

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Alibaba Qwen Researchers Introduced ProcessBench: A New AI Benchmark for Measuring the Ability to Identify Process Errors in Mathematical Reasoning

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Best-of-N Jailbreaking: A Multi-Modal AI Approach to Identifying Vulnerabilities in Large Language Models

  • Mohammad Asjad

Latent Functional Maps: A Robust Machine Learning Framework for Analyzing Neural Network Representations

  • Mohammad Asjad

Voyage AI Introduces voyage-code-3: A New Next-Generation Embedding Model Optimized for Code Retrieval

  • Mohammad Asjad

Meet GRAPE: A Plug-and-Play Algorithm to Generalize Robot Policies via Preference Alignment

  • Mohammad Asjad

Top 20 Guardrails to Secure LLM Applications

  • Mohammad Asjad

Allen Institute for AI: Open-Source Innovations with Ethical Commitments and Contributions in 2024

  • Mohammad Asjad

Can You Turn Your Vision-Language Model from a Zero-Shot Model to Any-Shot Generalist? Meet LIxP, the Context-Aware Multimodal Framework

  • Mohammad Asjad

Characterizing and Mitigating Compute Express Link (CXL) Interference in Modern Memory Systems

  • Mohammad Asjad

ShowUI: A Vision-Language-Action Model for GUI Visual Agents that Addresses Key Challenges in UI Visual and Action Modeling

  • Mohammad Asjad

Geometry Distributions: Advancing Neural 3D Surface Modeling with Diffusion Models

  • Mohammad Asjad

SEALONG: A Self-Improving AI Approach to Long-Context Reasoning in Large Language Models

  • Mohammad Asjad

Quantum Neuromorphic Computing: Implementing Scalable Quantum Perceptrons

  • Mohammad Asjad

Red Teaming for AI: Strengthening Safety and Trust through External Evaluation

  • Mohammad Asjad

KuaiFormer: A Transformer-Based Architecture for Large-Scale Short-Video Recommendation Systems

  • Mohammad Asjad

Artificial Intelligence AI and Quantum Computing: Transforming Computational Frontiers

  • Mohammad Asjad

Deep Learning Meets Cybersecurity: A Hybrid Approach to Detecting DDoS Attacks with Unmatched Accuracy

  • Mohammad Asjad

Stanford Researchers Propose ‘POSR’: A Unique AI Framework for Analyzing Educational Conversations Using Joint Segmentation and Retrieval

  • Mohammad Asjad

H-DPO: Advancing Language Model Alignment through Entropy Control

  • Mohammad Asjad

Meet OpenCoder: A Completely Open-Source Code LLM Built on the Transparent Data Process Pipeline and Reproducible Dataset

  • Mohammad Asjad

Researchers from Snowflake and CMU Introduce SuffixDecoding: A Novel Model-Free Approach to Accelerating Large Language Model (LLM) Inference through Speculative Decoding

  • Mohammad Asjad

The Semantic Hub: A Cognitive Approach to Language Model Representations

  • Mohammad Asjad

Researchers from Stanford and Cornell Introduce APRICOT: A Novel AI Approach that Merges LLM-based Bayesian Active Preference Learning with Constraint-Aware Task Planning

  • Mohammad Asjad

Nearest Neighbor Normalization: A Sublinear Approach to Improving Contrastive Retrieval

  • Mohammad Asjad

Predicting and Interpreting In-Context Learning Curves Through Bayesian Scaling Laws

  • Mohammad Asjad

Multi-Scale Geometric Analysis of Language Model Features: From Atomic Patterns to Galaxy Structures

  • Mohammad Asjad

AUTO-CEI: A Curriculum and Expert Iteration Approach to Elevate LLMs’ Response Precision and Control Refusal Rates Across Diverse Reasoning Domains

  • Mohammad Asjad

CodeFavor: A Machine Learning Framework that Trains Pairwise Preference Models with Synthetic Code Preferences Generated from Code Evolution like Code Commits and Code Critiques

  • Mohammad Asjad

SimpleToM: Evaluating Applied Theory of Mind Capabilities in Large Language Models

  • Mohammad Asjad

LongRAG: A Robust RAG Framework for Long-Context Question Answering

  • Mohammad Asjad

MiniCTX: Advancing Context-Dependent Theorem Proving in Large Language Models

  • Mohammad Asjad

Meta AI Researchers Introduce Token-Level Detective Reward Model (TLDR) to Provide Fine-Grained Annotations for Large Vision Language Models

  • Mohammad Asjad

Multi-Scale Neural Audio Codec (SNAC): An Wxtension of Residual Vector Quantization that Uses Quantizers Operating at Multiple Temporal Resolutions

  • Mohammad Asjad

Scaling Diffusion transformers (DiT): An AI Framework for Optimizing Text-to-Image Models Across Compute Budgets

  • Mohammad Asjad

Model Kinship: The Degree of Similarity or Relatedness between LLMs, Analogous to Biological Evolution

  • Mohammad Asjad

AutoDAN-Turbo: A Black-Box Jailbreak Method for LLMs with a Lifelong Agent

  • Mohammad Asjad

Stochastic Prompt Construction for Effective In-Context Reinforcement Learning in Large Language Models

  • Mohammad Asjad

UNC Chapel Hill Researchers Propose DataEnvGym: A Testbed of Teacher Environments for Data Generation Agents

  • Mohammad Asjad

ScienceAgentBench: A Rigorous AI Evaluation Framework for Language Agents in Scientific Discovery

  • Mohammad Asjad

SQ-LLaVA: A New Visual Instruction Tuning Method that Enhances General-Purpose Vision-Language Understanding and Image-Oriented Question Answering through Visual Self-Questioning

  • Mohammad Asjad

From Prediction to Reasoning: Evaluating o1’s Impact on LLM Probabilistic Biases

  • Mohammad Asjad

NVIDIA AI Releases OpenMathInstruct-2: A Math Instruction Tuning Dataset with 14M Problem-Solution Pairs Generated Using the Llama3.1-405B-Instruct Model

  • Mohammad Asjad

Exploring In-Context Reinforcement Learning in LLMs with Sparse Autoencoders

  • Mohammad Asjad

AI-Assisted Causal Inference: Using LLMs to Revolutionize Instrumental Variable Selection

  • Mohammad Asjad

GemFilter: A Novel AI Approach to Accelerate LLM Inference and Reduce Memory Consumption for Long Context Inputs

  • Mohammad Asjad

The Impact of AI Chatbots on False Memory Formation: A Comprehensive Study

  • Mohammad Asjad

Salesforce AI Research Proposes a Novel Threat Model: Building Secure LLM Applications Against Prompt Leakage Attacks

  • Mohammad Asjad

Evaluating the Vulnerabilities of Unlearning Techniques in Large Language Models: A Comprehensive White-Box Analysis

  • Mohammad Asjad

Logic-of-Thought: Enhancing Logical Reasoning in Large Language Models through Propositional Logic Augmentation

  • Mohammad Asjad

Model Collapse in the Synthetic Data Era: Analytical Insights and Mitigation Strategies

  • Mohammad Asjad

WaveletGPT: Leveraging Wavelet Theory for Speedier LLM Training Across Modalities

  • Mohammad Asjad

Scaling Laws and Model Comparison: New Frontiers in Large-Scale Machine Learning

  • Mohammad Asjad

RxEnvironments.jl: A Reactive Programming Approach to Complex Agent-Environment Simulations in the Julia Language

  • Mohammad Asjad

Bridging Policy and Practice: Transparency Reporting in Foundation Models

  • Mohammad Asjad

Researchers from John Hopkins and Samaya AI Propose Promptriever: A Zero-Shot Promptable Retriever Trained from a New Instruction-based Retrieval Dataset

  • Mohammad Asjad

Iteration of Thought: An AI Framework for Enhancing LLM Responses by Generating “thought”-Provoking Prompts

  • Mohammad Asjad

RetrievalAttention: A Training-Free Machine Learning Approach to both Accelerate Attention Computation and Reduce GPU Memory Consumption

  • Mohammad Asjad

DCMAC: Demand-Aware Customized Communication for Efficient Multi-Agent Reinforcement Learning

  • Mohammad Asjad

CORE-Bench: A Benchmark Consisting of 270 Tasks based on 90 Scientific Papers Across Computer Science, Social Science, and Medicine with Python or R Codebases

  • Mohammad Asjad

Gated Slot Attention: Advancing Linear Attention Models for Efficient and Effective Language Processing

  • Mohammad Asjad

Sketch: An Innovative AI Toolkit Designed to Streamline LLM Operations Across Diverse Fields

  • Mohammad Asjad

This AI Paper from Centre for the Governance of AI Proposes a Grading Rubric for AI Safety Frameworks

  • Mohammad Asjad

Contrastive Twist Learning and Bidirectional SMC Bounds: A New Paradigm for Language Model Control

  • Mohammad Asjad

Rethinking LLM Training: The Promise of Inverse Reinforcement Learning Techniques

  • Mohammad Asjad

Google DeepMind Researchers Propose Human-Centric Alignment for Vision Models to Boost AI Generalization and Interpretation

  • Mohammad Asjad

LLaMA-Omni: A Novel AI Model Architecture Designed for Low-Latency and High-Quality Speech Interaction with LLMs

  • Mohammad Asjad

Small but Mighty: The Enduring Relevance of Small Language Models in the Age of LLMs

  • Mohammad Asjad

Ebay Researchers Introduce GraphEx: A Graph-based Extraction Method for Advertiser Keyphrase Recommendation

  • Mohammad Asjad

Automating Reinforcement Learning Workflows with Vision-Language Models: Towards Autonomous Mastery of Robotic Tasks

  • Mohammad Asjad

FlashSigmoid: A Hardware-Aware and Memory-Efficient Implementation of Sigmoid Attention Yielding a 17% Inference Kernel Speed-Up over FlashAttention-2 on H100 GPUs

  • Mohammad Asjad

Apple Researchers Propose a Novel AI Algorithm to Optimize a Byte-Level Representation for Automatic Speech Recognition ASR and Compare it with UTF-8 Representation

  • Mohammad Asjad

Optimizing Document Understanding with DocOwl2: A Novel High-Resolution Compression Architecture

  • Mohammad Asjad

Language-Guided World Models (LWMs): Enhancing Agent Controllability and Compositional Generalization through Natural Language

  • Mohammad Asjad

Political DEBATE Language Models: Open-Source Solutions for Efficient Text Classification in Political Science

  • Mohammad Asjad

VQ4DiT: A Fast Post-Training Vector Quantization Method for DiTs (Diffusion Transformers Models)

  • Mohammad Asjad

Advancing Cantonese NLP: Bridging Development Gaps in Large Language Models with New Benchmarks and Open-Source Innovations

  • Mohammad Asjad

OpenFGL: A Comprehensive Benchmark for Advancing Federated Graph Learning

  • Mohammad Asjad

SFR-GNN: A Novel Graph Neural Networks (GNN) Model that Employs an ‘Attribute Pre-Training and Structure Fine-Tuning’ Strategy to Achieve Robustness Against Structural Attacks

  • Mohammad Asjad

Comparative Analysis of LLM and Traditional Text Augmentation: Accuracy, Efficiency, and Cost-Effectiveness

  • Mohammad Asjad

Why GPU Utilization Falls Short: Understanding Streaming Multiprocessor (SM) Efficiency for Better LLM Performance

  • Mohammad Asjad

LLaVaOLMoBitnet1B: The First Ternary Multimodal LLM Capable of Accepting Image(s) and Text Inputs to Produce Coherent Textual Response

  • Mohammad Asjad

The Mamba in the Llama: Accelerating Inference with Speculative Decoding

  • Mohammad Asjad

Qwen2-VL Released: The Latest Version of the Vision Language Models based on Qwen2 in the Qwen Model Familities

  • Mohammad Asjad

Microsoft Researchers Combine Small and Large Language Models for Faster, More Accurate Hallucination Detection

  • Mohammad Asjad

Aleph Alpha Researchers Release Pharia-1-LLM-7B: Two Distinct Variants- Pharia-1-LLM-7B-Control and Pharia-1-LLM-7B-Control-Aligned

  • Mohammad Asjad

Jina AI Introduced ‘Late Chunking’: A Simple AI Approach to Embed Short Chunks by Leveraging the Power of Long-Context Embedding Models

  • Mohammad Asjad

Humboldt: A Specification-based System Framework for Generating a Data Discovery UI from Different Metadata Providers

  • Mohammad Asjad

Improving RLHF (Reinforcement Learning from Human Feedback) with Critique-Generated Reward Models

  • Mohammad Asjad

AWS Enhancing Information Retrieval in Large Language Models: A Data-Centric Approach Using Metadata, Synthetic QAs, and Meta Knowledge Summaries for Improved Accuracy and Relevancy

  • Mohammad Asjad

Integrating Graph Structures into Language Models: A Comprehensive Study of GraphRAG

  • Mohammad Asjad

Code as a Catalyst: Improving LLM Capabilities Across Diverse Tasks

  • Mohammad Asjad

DaRec: A Novel Plug-and-Play Alignment Framework for LLMs and Collaborative Models

  • Mohammad Asjad

DataVisT5: A Powerful Pre-Trained Language Model for Seamless Data Visualization Tasks

  • Mohammad Asjad

Improving Robustness Against Bias in Social Science Machine Learning: The Promise of Instruction-Based Models

  • Mohammad Asjad

UniBench: A Python Library to Evaluate Vision-Language Models VLMs Robustness Across Diverse Benchmarks

  • Mohammad Asjad

Meta AI and NYU Researchers Propose E-RLHF to Combat LLM Jailbreaking

  • Mohammad Asjad

Google AI Announces Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

  • Mohammad Asjad

DeepSeek-AI Open-Sources DeepSeek-Prover-V1.5: A Language Model with 7 Billion Parameters that Outperforms all Open-Source Models in Formal Theorem Proving in Lean 4

  • Mohammad Asjad

Answer.AI Releases answerai-colbert-small: A Proof of Concept for Smaller, Faster, Modern ColBERT Models

  • Mohammad Asjad

Self-play muTuAl Reasoning (rStar): A Novel AI Approach that Boosts Small Language Models SLMs’ Reasoning Capability during Inference without Fine-Tuning

  • Mohammad Asjad

NACL: A Robust KV Cache Eviction Framework for Efficient Long-Text Processing in LLMs

  • Mohammad Asjad

Apple Researchers Present KGLens: A Novel AI Method Tailored for Visualizing and Evaluating the Factual Knowledge Embedded in LLMs

  • Mohammad Asjad

Parler-TTS Released: A Fully Open-Sourced Text-to-Speech Model with Advanced Speech Synthesis for Complex and Lightweight Applications

  • Mohammad Asjad

RadGraph2: A New Dataset for Tracking Disease Progression in Radiology Reports

  • Mohammad Asjad

NYU Researchers Open-Sourced GPUDrive: A GPU-Accelerated Multi-Agent Driving Simulation at 1 Million FPS

  • Mohammad Asjad

Allen Institute for AI (AI2) Released a New Bundle of OLMo 1B and 7B Assets

  • Mohammad Asjad

This AI Paper Introduces a Verbalized Way to Perform Machine Learning and Conducts Several Case Studies on Regression and Classification Tasks

  • Mohammad Asjad

Magpie-Ultra Dataset Released: Harnessing Llama 3.1 405B for Diverse AI Instruction-Response Pairs

  • Mohammad Asjad

Character AI Releases Prompt Poet: A New Low Code Python Libary that Streamlines Prompt Design for both Developers and Non-Technical Users

  • Mohammad Asjad

MLPs vs KANs: Evaluating Performance in Machine Learning, Computer Vision, NLP, and Symbolic Tasks

  • Mohammad Asjad

Lyzr Automata: A Low-Code Multi-Agent Framework for Advanced Process Automation

  • Mohammad Asjad

Black Forest Labs Open-Source FLUX.1: A 12 Billion Parameter Rectified Flow Transformer Capable of Generating Images from Text Descriptions

  • Mohammad Asjad

Google AI Introduces ShieldGemma: A Comprehensive Suite of LLM-based Safety Content Moderation Models Built on Gemma2

  • Mohammad Asjad

PersonaGym: A Dynamic AI Framework for Comprehensive Evaluation of LLM Persona Agents

  • Mohammad Asjad

Salesforce AI Introduces ‘ThinK’: A New AI Method that Exploits Substantial Redundancy Across the Channel Dimension of the KV Cache

  • Mohammad Asjad

rLLM (relationLLM): A PyTorch Library Designed for Relational Table Learning (RTL) with Large Language Models (LLMs)

  • Mohammad Asjad

Recursive IntroSpEction (RISE): A Machine Learning Approach for Fine-Tuning LLMs to Improve Their Own Responses Over Multiple Turns Sequentially

  • Mohammad Asjad

This AI Paper from Stanford Provides New Insights on AI Model Collapse and Data Accumulation

  • Mohammad Asjad

CompeteAI: An Artificial Intelligence AI Framework that Understands the Competition Dynamics of Large Language Model-based Agents

  • Mohammad Asjad

FLUTE: A CUDA Kernel Designed for Fused Quantized Matrix Multiplications to Accelerate LLM Inference

  • Mohammad Asjad

SF-LLaVA: A Training-Free Video LLM that is Built Upon LLaVA-NeXT and Requires No Additional Fine-Tuning to Work Effectively for Various Video Tasks

  • Mohammad Asjad

Apple Researchers Propose LazyLLM: A Novel AI Technique for Efficient LLM Inference in Particular under Long Context Scenarios

  • Mohammad Asjad

WTU-Eval: A New Standard Benchmark Tool for Evaluating Large Language Models LLMs Usage Capabilities

  • Mohammad Asjad

Open Artificial Knowledge (OAK) Dataset: A Large-Scale Resource for AI Research Derived from Wikipedia’s Main Categories

  • Mohammad Asjad

From RAG to ReST: A Survey of Advanced Techniques in Large Language Model Development

  • Mohammad Asjad

Athene-Llama3-70B Released: An Open-Weight LLM Trained through RLHF based on Llama-3-70B-Instruct

  • Mohammad Asjad

Agent Symbolic Learning: An Artificial Intelligence AI Framework for Agent Learning that Jointly Optimizes All Symbolic Components within an Agent System

  • Mohammad Asjad

ZebraLogic: A Logical Reasoning AI Benchmark Designed for Evaluating LLMs with Logic Puzzles

  • Mohammad Asjad

MUSE: A Comprehensive AI Framework for Evaluating Machine Unlearning in Language Models

  • Mohammad Asjad

EM-LLM: A Novel and Flexible Architecture that Integrates Key Aspects of Human Episodic Memory and Event Cognition into Transformer-based Language Models

  • Mohammad Asjad

From Diagrams to Solutions: MAVIS’s Three-Stage Framework for Mathematical AI

  • Mohammad Asjad

DotaMath: Advancing LLMs’ Mathematical Reasoning Through Decomposition and Self-Correction

  • Mohammad Asjad

G-Retriever: Advancing Real-World Graph Question Answering with RAG and LLMs

  • Mohammad Asjad

MELLE: A Novel Continuous-Valued Tokens-based Language Modeling Approach for Text-to-Speech Synthesis (TTS)

  • Mohammad Asjad

UCSD Researchers Propose a General Variational Inference-based Framework (MCD) to Infer the Underlying Causal Models as well as the Mixing Probability of Each Sample

  • Mohammad Asjad

Planetarium: A New Benchmark to Evaluate LLMs on Translating Natural Language Descriptions of Planning Problems into Planning Domain Definition Language PDDL

  • Mohammad Asjad

Samsung Researchers Introduce LoRA-Guard: A Parameter-Efficient Guardrail Adaptation Method that Relies on Knowledge Sharing between LLMs and Guardrail Models

  • Mohammad Asjad

InternLM-XComposer-2.5 (IXC-2.5): A Versatile Large-Vision Language Model that Supports Long-Contextual Input and Output

  • Mohammad Asjad

Can LLMs Help Accelerate the Discovery of Data-Driven Scientific Hypotheses? Meet DiscoveryBench: A Comprehensive LLM Benchmark that Formalizes the Multi-Step Process of Data-Driven Discovery

  • Mohammad Asjad

Google DeepMind Unveils PaliGemma: A Versatile 3B Vision-Language Model VLM with Large-Scale Ambitions

  • Mohammad Asjad

This AI Paper from the National University of Singapore Introduces a Defense Against Adversarial Attacks on LLMs Utilizing Self-Evaluation

  • Mohammad Asjad

NVIDIA Introduces RankRAG: A Novel RAG Framework that Instruction-Tunes a Single LLM for the Dual Purposes of Top-k Context Ranking and Answer Generation in RAG

  • Mohammad Asjad

This AI Research from Tenyx Explore the Reasoning Abilities of Large Language Models (LLMs) Through Their Geometrical Understanding

  • Mohammad Asjad

WorldBench: A Dynamic and Flexible LLM Benchmark Composed of Per-Country Data from the World Bank

  • Mohammad Asjad

Exploring the Influence of AI-Based Recommenders on Human Behavior: Methodologies, Outcomes, and Future Research Directions

  • Mohammad Asjad

Safeguarding Healthcare AI: Exposing and Addressing LLM Manipulation Risks

  • Mohammad Asjad

A Concurrent Programming Framework for Quantitative Analysis of Efficiency Issues When Serving Multiple Long-Context Requests Under Limited GPU High-Bandwidth Memory (HBM) Regime

  • Mohammad Asjad

Rethinking QA Dataset Design: How Popular Knowledge Enhances LLM Accuracy?

  • Mohammad Asjad

EvoAgent: A Generic Method to Automatically Extend Expert Agents to Multi-Agent Systems via the Evolutionary Algorithm

  • Mohammad Asjad

Privacy Meets Performance: GPT4All 3.0 Redefines Local AI Interaction

  • Mohammad Asjad

45 Shades of AI Safety: SORRY-Bench’s Innovative Taxonomy for LLM Refusal Behavior Analysis

  • Mohammad Asjad

Researchers at Princeton University Proposes Edge Pruning: An Effective and Scalable Method for Automated Circuit Finding

  • Mohammad Asjad

Fal AI Introduces AuraSR: A 600M Parameter Upsampler Model Derived from the GigaGAN

  • Mohammad Asjad

Researchers at Brown University Explore Zero-Shot Cross-Lingual Generalization of Preference Tuning in Detoxifying LLMs

  • Mohammad Asjad

This AI Paper from CMU and Google DeepMind Studies the Role of Synthetic Data for Improving Math Reasoning Capabilities of LLMs

  • Mohammad Asjad

MuxServe: A Flexible and Efficient Spatial-Temporal Multiplexing System to Serve Multiple LLMs Concurrently

  • Mohammad Asjad

Q*: A Versatile Artificial Intelligence AI Approach to Improve LLM Performance in Reasoning Tasks

  • Mohammad Asjad

GraphReader: A Graph-based AI Agent System Designed to Handle Long Texts by Structuring them into a Graph and Employing an Agent to Explore this Graph Autonomously

  • Mohammad Asjad

Camb AI Releases MARS5 TTS: A Novel Open Source Text to Speech Model for Insane Prosody

  • Mohammad Asjad

Whiteboard-of-Thought (WoT) Prompting: A Simple AI Approach to Enhance the Visual Reasoning Abilities of MLLMs Across Modalities

  • Mohammad Asjad

MIPRO: A Novel Optimizer that Outperforms Baselines on Five of Six Diverse Language Model LM Programs Using a Best-in-Class Open-Source Model (Llama-3-8B) by 12.9% accuracy

  • Mohammad Asjad

Cephalo: A Series of Open-Source Multimodal Vision Large Language Models (V-LLMs) Specifically in the Context of Bio-Inspired Design

  • Mohammad Asjad

LOFT: A Comprehensive AI Benchmark for Evaluating Long-Context Language Models

  • Mohammad Asjad

The Rise of Diffusion-Based Language Models: Comparing SEDD and GPT-2

  • Mohammad Asjad

PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers

  • Mohammad Asjad

CS-Bench: A Bilingual (Chinese-English) Benchmark Dedicated to Evaluating the Performance of LLMs in Computer Science

  • Mohammad Asjad

StreamSpeech: A Direct Simul-S2ST Speech-to-Speech Translation Model that Jointly Learns Translation and Simultaneous Policy in a Unified Framework of Multi-Task Learning

  • Mohammad Asjad

Apple Releases 4M-21: A Very Effective Multimodal AI Model that Solves Tens of Tasks and Modalities

  • Mohammad Asjad

Pixel Transformer: Challenging Locality Bias in Vision Models

  • Mohammad Asjad

Neural Algorithmic Reasoning for Transformers: The TransNAR Framework

  • Mohammad Asjad

Microsoft Researchers Introduce Samba 3.8B: A Simple Mamba+Sliding Window Attention Architecture that Outperforms Phi3-mini on Major Benchmarks

  • Mohammad Asjad

Enhancing Trust in Large Language Models: Fine-Tuning for Calibrated Uncertainties in High-Stakes Applications

  • Mohammad Asjad

SelfGoal: An Artificial Intelligence AI Framework to Enhance an LLM-based Agent’s Capabilities to Achieve High-Level Goals

  • Mohammad Asjad

GenAI-Arena: An Open Platform for Community-Based Evaluation of Generative AI Models

  • Mohammad Asjad

Benchmarking Federated Learning for Large Language Models with FedLLM-Bench

  • Mohammad Asjad

Advancing Reliable Question Answering with the CRAG Benchmark

  • Mohammad Asjad

From Low-Level to High-Level Tasks: Scaling Fine-Tuning with the ANDROIDCONTROL Dataset

  • Mohammad Asjad

The Missing Piece: Combining Foundation Models and Open-Endedness for Artificial Superhuman Intelligence ASI

  • Mohammad Asjad

Researchers at UC Berkeley Propose a Neural Diffusion Model that Operates on Syntax Trees for Program Synthesis

  • Mohammad Asjad

Modeling Cultural Accumulation in Artificial Reinforcement Learning Agents

  • Mohammad Asjad

Quantized Eigenvector Matrices for 4-bit Second-Order Optimization of Deep Neural Networks

  • Mohammad Asjad

Meet Tsinghua University’s GLM-4-9B-Chat-1M: An Outstanding Language Model Challenging GPT 4V, Gemini Pro (on vision), Mistral and Llama 3 8B

  • Mohammad Asjad

Parrot: Optimizing End-to-End Performance in LLM Applications Through Semantic Variables

  • Mohammad Asjad

Researchers at Microsoft Introduce Aurora: A Large-Scale Foundation Model of the Atmosphere Trained on Over a Million Hours of Diverse Weather and Climate Data

  • Mohammad Asjad

Contextual Position Encoding (CoPE): A New Position Encoding Method that Allows Positions to be Conditioned on Context by Incrementing Position only on Certain Tokens Determined by the Model

  • Mohammad Asjad

GNN-RAG: A Novel AI Method for Combining Language Understanding Abilities of LLMs with the Reasoning Abilities of GNNs in a Retrieval-Augmented Generation (RAG) Style

  • Mohammad Asjad

This AI Paper from Princeton and the University of Warwick Proposes a Novel Artificial Intelligence Approach to Enhance the Utility of LLMs as Cognitive Models

  • Mohammad Asjad

MoEUT: A Robust Machine Learning Approach to Addressing Universal Transformers’ Efficiency Challenges

  • Mohammad Asjad

Llama3-V: A SOTA Open-Source VLM Model Comparable performance to GPT4-V, Gemini Ultra, Claude Opus with a 100x Smaller Model

  • Mohammad Asjad

In-Context Learning Capabilities of Multi-Layer Perceptrons MLPs: A Comparative Study with Transformers

  • Mohammad Asjad

Question-Answer Cross Attention Networks (QAN): Advancing Answer Selection in Community Question Answering

  • Mohammad Asjad

Inductive Biases in Deep Learning: Understanding Feature Representation

  • Mohammad Asjad

Optimizing Agent Planning: A Parametric AI Approach to World Knowledge

  • Mohammad Asjad

Unlocking the Potential of SirLLM: Advancements in Memory Retention and Attention Mechanisms

  • Mohammad Asjad

Achieving Balance in Lifelong Learning: The WISE Memory Approach

  • Mohammad Asjad

A Paradigm Shift: MoRA’s Role in Advancing Parameter-Efficient Fine-Tuning Techniques

  • Mohammad Asjad

Transparency in Foundation Models: The Next Step in Foundation Model Transparency Index FMTI

  • Mohammad Asjad

An Efficient AI Approach to Memory Reduction and Throughput Enhancement in LLMs

  • Mohammad Asjad

Apple Researchers Propose KV-Runahead: An Efficient Parallel LLM Inference Technique to Minimize the Time-to-First-Token

  • Mohammad Asjad

Toward Responsible Innovation: Evaluating Risks and Opportunities in Open Generative AI

  • Mohammad Asjad

TII Releases Falcon 2-11B: The First AI Model of the Falcon 2 Family Trained on 5.5T Tokens with a Vision Language Model

  • Mohammad Asjad

This AI Paper from Stanford University Evaluates the Performance of Multimodal Foundation Models Scaling from Few-Shot to Many-Shot-In-Context Learning ICL

  • Mohammad Asjad

Meta AI Introduces Chameleon: A New Family of Early-Fusion Token-based Foundation Models that Set a New Bar for Multimodal Machine Learning

  • Mohammad Asjad

SpeechVerse: A Multimodal AI Framework that Enables LLMs to Follow Natural Language Instructions for Performing Diverse Speech-Processing Tasks

  • Mohammad Asjad

Unveiling the Potential of Large Language Models: Enhancing Feedback Generation in Computing Education

  • Mohammad Asjad

CMU Researchers Propose MOMENT: A Family of Open-Source Machine Learning Foundation Models for General-Purpose Time Series Analysis

  • Mohammad Asjad

Advancements in Knowledge Distillation and Multi-Teacher Learning: Introducing AM-RADIO Framework

  • Mohammad Asjad

RadOnc-GPT: Leveraging Meta Llama for a Pioneering Radiation Oncology Model

  • Mohammad Asjad

Enhancing Anomaly Detection with Adaptive Noise: A Pseudo Anomaly Approach

  • Mohammad Asjad

UC Berkeley Researchers Introduce Learnable Latent Codes as Bridges (LCB): A Novel AI Approach that Combines the Abstract Reasoning Capabilities of Large Language Models with Low-Level Action Policies

  • Mohammad Asjad

Towards Autonomous Software Development: The SWE-agent Revolution

  • Mohammad Asjad

Exploring Sharpness-Aware Minimization (SAM): Insights into Label Noise Robustness and Generalization

  • Mohammad Asjad

Top AI-Powered Cartoonizer Tools

  • Mohammad Asjad

TRAMBA: A Novel Hybrid Transformer and Mamba-based Architecture for Speech Super Resolution and Enhancement for Mobile and Wearable Platforms

  • Mohammad Asjad

MaRDIFlow: Automating Metadata Abstraction for Enhanced Reproducibility in Computational Workflows

  • Mohammad Asjad

Self-Play Preference Optimization (SPPO): An Innovative Machine Learning Approach to Finetuning Large Language Models (LLMs) from Human/AI Feedback

  • Mohammad Asjad

CMU Researchers Propose a Distributed Data Scoping Method: Revealing the Incompatibility between the Deep Learning Architecture and the Generic Transport PDEs

  • Mohammad Asjad

Deciphering Transformer Language Models: Advances in Interpretability Research

  • Mohammad Asjad

Researchers at Stanford Introduce SUQL: A Formal Query Language for Integrating Structured and Unstructured Data

  • Mohammad Asjad

Evaluating LLM Trustworthiness: Insights from Harmoniticity Analysis Research from VISA Team

  • Mohammad Asjad

Iterative Preference Optimization for Improving Reasoning Tasks in Language Models

  • Mohammad Asjad

Bridging the Binary Gap: Challenges in Training Neural Networks to Decode and Summarize Code

  • Mohammad Asjad

Meta AI Introduces CyberSecEval 2: A Novel Machine Learning Benchmark to Quantify LLM Security Risks and Capabilities

  • Mohammad Asjad

Exploring Parameter-Efficient Fine-Tuning Strategies for Large Language Models

  • Mohammad Asjad

REBEL: A Reinforcement Learning RL Algorithm that Reduces the Problem of RL to Solving a Sequence of Relative Reward Regression Problems on Iteratively Collected Datasets

  • Mohammad Asjad

Meet Electric Atlas: A New Era of Robotics by Boston Dynamics

  • Mohammad Asjad

Researchers at UC San Diego Propose DrS: A Novel Machine Learning Approach for Learning Reusable Dense Rewards for Multi-Stage Tasks in a Data-Driven Manner

  • Mohammad Asjad

From Lost to Found: INformation-INtensive (IN2) Training Revolutionizes Long-Context Language Understanding

  • Mohammad Asjad

Integrating Large Language Models with Graph Machine Learning: A Comprehensive Review

  • Mohammad Asjad

Enhancing AI Model’s Scalability and Performance: A Study on Multi-Head Mixture-of-Experts

  • Mohammad Asjad

Microsoft AI Releases Phi-3 Family of Models: A 3.8B Parameter Language Model Trained on 3.3T Tokens Locally on Your Phone

  • Mohammad Asjad

Interpretable Deep Learning for Biodiversity Monitoring: Introducing AudioProtoPNet

  • Mohammad Asjad

Privacy-Preserving Training-as-a-Service (PTaaS): A Novel Service Computing Paradigm that Provides Privacy-Friendly and Customized Machine Learning Model Training for End Devices

  • Mohammad Asjad

NVIDIA AI Researchers Introduce ScaleFold: A Leap in High-Performance Computing for Protein Structure Prediction

  • Mohammad Asjad

Unveiling Challenges in Language Model Performance: A Study of Saturation and Representation Degeneration

  • Mohammad Asjad

Researchers at CMU Introduce TriForce: A Hierarchical Speculative Decoding AI System that is Scalable to Long Sequence Generation

  • Mohammad Asjad

Researchers at Microsoft Introduces VASA-1: Transforming Realism in Talking Face Generation with Audio-Driven Innovation

  • Mohammad Asjad

Unlocking the Recall Power of Large Language Models: Insights from Needle-in-a-Haystack Testing

  • Mohammad Asjad

Navigating the Landscape of CLIP: Investigating Data, Architecture, and Training Strategies

  • Mohammad Asjad

This AI Paper Introduces Pipeline Forward-Forward Algorithm (PFF): A Novel Machine Learning Approach to Training Distributed Neural Networks using Forward-Forward Algorithm

  • Mohammad Asjad

Researchers at Oxford Presented Policy-Guided Diffusion: A Machine Learning Method for Controllable Generation of Synthetic Trajectories in Offline Reinforcement Learning RL

  • Mohammad Asjad

Researchers at UC Berkeley Introduce GOEX: A Runtime for LLMs with an Intuitive Undo and Damage Confinement Abstractions, Enabling the Safer Deployment of LLM Agents in Practice

  • Mohammad Asjad

LM-Guided CoT: A Novel Machine Learning Framework that Leverages a Lightweight (<1B) Language Model (LM) for guiding a black-box large (>10B) LM in Reasoning Tasks

  • Mohammad Asjad

The Future of Neural Network Training: Empirical Insights into μ-Transfer for Hyperparameter Scaling

  • Mohammad Asjad

This AI Paper from China Introduces MiniCPM: Introducing Innovative Small Language Models Through Scalable Training Approaches

  • Mohammad Asjad

Meta AI Presents MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding

  • Mohammad Asjad

This AI Paper Introduces ReasonEval: A New Machine Learning Method to Evaluate Mathematical Reasoning Beyond Accuracy

  • Mohammad Asjad

This Machine Learning Paper Introduce PISSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models

  • Mohammad Asjad

Microsoft Researchers Propose Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models

  • Mohammad Asjad

This Machine Learning Paper Introduces JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models

  • Mohammad Asjad

Linear Attention Sequence Parallel (LASP): An Efficient Machine Learning Method Tailored to Linear Attention-Based Language Models

  • Mohammad Asjad

Researchers at Intel Labs Introduce LLaVA-Gemma: A Compact Vision-Language Model Leveraging the Gemma Large Language Model in Two Variants (Gemma-2B and Gemma-7B)

  • Mohammad Asjad

AutoTRIZ: An Artificial Ideation Tool that Leverages Large Language Models (LLMs) to Automate and Enhance the TRIZ (Theory of Inventive Problem Solving) Methodology

  • Mohammad Asjad

UniLLMRec: An End-to-End LLM-Centered Recommendation Framework to Execute Multi-Stage Recommendation Tasks Through Chain-of-Recommendations

  • Mohammad Asjad

Can Benign Data Undermine AI Safety? This Paper from Princeton University Explores the Paradox of Machine Learning Fine-Tuning

  • Mohammad Asjad

Are We on the Right Way for Evaluating Large Vision-Language Models? This AI Paper from China Introduces MMStar: An Elite Vision-Dependent Multi-Modal Benchmark

  • Mohammad Asjad

Evolution of RAGs: Naive RAG, Advanced RAG, and Modular RAG Architectures

  • Mohammad Asjad

NVIDIA AI Research Proposes Language Instructed Temporal-Localization Assistant (LITA), which Enables Accurate Temporal Localization Using Video LLMs

  • Mohammad Asjad

Researchers from the University of Washington and Meta AI Present a Simple Context-Aware Decoding (CAD) Method to Encourage the Language Model to Attend to Its Context During Generation

  • Mohammad Asjad

This Paper Reveals Insights from Reproducing OpenAI’s RLHF (Reinforcement Learning from Human Feedback) Work: Implementation and Scaling Explored

  • Mohammad Asjad

Do LLM Agents Have Regret? This Machine Learning Research from MIT and the University of Maryland Presents a Case Study on Online Learning and Games

  • Mohammad Asjad

This AI Paper from Microsoft Present SiMBA: A Simplified Mamba-based Architecture for Vision and Multivariate Time Series

  • Mohammad Asjad

Stability AI Introduces Stable Code: A General Purpose Base Code Language Model

  • Mohammad Asjad

The Idea of Compiler-Generated Feedback for Large Language Models

  • Mohammad Asjad

LlamaFactory: A Unified Machine Learning Framework that Integrates a Suite of Cutting-Edge Efficient Training Methods, Allowing Users to Customize the Fine-Tuning of 100+ LLMs Flexibly

  • Mohammad Asjad

Sakana AI Introduces Evolutionary Model Merge: A New Machine Learning Approach Automating Foundation Model Development

  • Mohammad Asjad

Researchers from Alibaba and the Renmin University of China Present mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding

  • Mohammad Asjad

IBM’s Alignment Studio to Optimize AI Compliance for Contextual Regulations

  • Mohammad Asjad

This AI Paper Introduces the Lightweight Mamba UNet (LightM-UNet) that Integrates Mamba and UNet in a Lightweight Framework for Medical Image Segmentation

  • Mohammad Asjad

This Machine Learning Research Presents ScatterMoE: An Implementation of Sparse Mixture-of-Experts (SMoE) on GPUs

  • Mohammad Asjad

Synth2: Boosting Visual-Language Models with Synthetic Captions and Image Embeddings by Researchers from Google DeepMind

  • Mohammad Asjad

Google DeepMind Researchers Unveil Multistep Consistency Models: A Machine Learning Approach that Balances Speed and Quality in AI Sampling

  • Mohammad Asjad

Training Value Functions via Classification for Scalable Deep Reinforcement Learning: Study by Google DeepMind Researchers and Others

  • Mohammad Asjad

This AI Paper from China Introduces ShortGPT: A Novel Artificial Intelligence Approach to Pruning Large Language Models (LLMs) based on Layer Redundancy

  • Mohammad Asjad

Unlocking the Best Tokenization Strategies: How Greedy Inference and SaGe Lead the Way in NLP Models

  • Mohammad Asjad

Unlocking the ‘Wisdom of the Silicon Crowd’: How LLM Ensembles Are Redefining Forecasting Accuracy to Match Human Expertise

  • Mohammad Asjad

Microsoft AI Researchers Developed a New Improved Framework ResLoRA for Low-Rank Adaptation (LoRA)

  • Mohammad Asjad

Maximizing Efficiency in AI Training: A Deep Dive into Data Selection Practices and Future Directions

  • Mohammad Asjad

Researchers from Tsinghua University and Microsoft AI Unveil a Breakthrough in Language Model Training: The Path to Optimal Learning Efficiency

  • Mohammad Asjad

This AI Paper from the University of Michigan and Netflix Proposes CLoVe: A Machine Learning Framework to Improve the Compositionality of Pre-Trained Contrastive Vision-Language Models

  • Mohammad Asjad

Researchers from Mohamed bin Zayed University of AI Developed ‘PALO’: A Polyglot Large Multimodal Model for 5B People

  • Mohammad Asjad

Google AI Proposes USER-LLM: A Novel Artificial Intelligence Framework that Leverages User Embeddings to Contextualize LLMs

  • Mohammad Asjad

Brown University Researchers Propose LexC-Gen: A New Artificial Intelligence Method that Generates Low-Resource-Language Classification Task Data at Scale

  • Mohammad Asjad

Neural Network Diffusion: Generating High-Performing Neural Network Parameters

  • Mohammad Asjad

Microsoft Present AI Controller Interface: Generative AI with a Lightweight, LLM-Integrated Virtual Machine (VM)

  • Mohammad Asjad

Can We Drastically Reduce AI Training Costs? This AI Paper from MIT, Princeton, and Together AI Unveils How BitDelta Achieves Groundbreaking Efficiency in Machine Learning

  • Mohammad Asjad

Can Machine Learning Teach Robots to Understand Us Better? This Microsoft Research Introduces Language Feedback Models for Advanced Imitation Learning

  • Mohammad Asjad

This Machine Learning Research from Yale and Google AI Introduce SubGen: An Efficient Key-Value Cache Compression Algorithm via Stream Clustering

  • Mohammad Asjad

Apple Researchers Introduce Keyframer: An LLM-Powered Animation Prototyping Tool that can Generate Animations from Static Images (SVGs)

  • Mohammad Asjad

Meet BiLLM: A Novel Post-Training Binary Quantization Method Specifically Tailored for Compressing Pre-Trained LLMs

  • Mohammad Asjad

This AI Paper Proposes an Interactive Agent Foundation Model that Uses a Novel Multi-Task Agent Training Paradigm for Training AI Agents Across a Wide Range of Domains, Datasets, and Tasks

  • Mohammad Asjad

Meet EscherNet: A Multi-View Conditioned Diffusion Model for View Synthesis

  • Mohammad Asjad

Can Large Language Models be Trusted for Evaluation? Meet SCALEEVAL: An Agent-Debate-Assisted Meta-Evaluation Framework that Leverages the Capabilities of Multiple Communicative LLM Agents

  • Mohammad Asjad

Stanford Researchers Introduce RAPTOR: A Novel Tree-based Retrieval System that Augments the Parametric Knowledge of LLMs with Contextual Information

  • Mohammad Asjad

Researchers from McGill University Present the Pythia 70M Model for Distilling Transformers into Long Convolution Models

  • Mohammad Asjad

Alibaba Researchers Introduce Mobile-Agent: An Autonomous Multi-Modal Mobile Device Agent

  • Mohammad Asjad

Researchers from the Chinese University of Hong Kong and Tencent AI Lab Propose a Multimodal Pathway to Improve Transformers with Irrelevant Data from Other Modalities

  • Mohammad Asjad

This AI Paper from China Introduces DREditor: A Time-Efficient AI Approach for Building a Domain-Specific Dense Retrieval Model

  • Mohammad Asjad

This AI Paper from ETH Zurich, Google, and Max Plank Proposes an Effective AI Strategy to Boost the Performance of Reward Models for RLHF (Reinforcement Learning from Human Feedback)

  • Mohammad Asjad

Google AI Presents Lumiere: A Space-Time Diffusion Model for Video Generation

  • Mohammad Asjad

Researchers from ByteDance and Sun Yat-Sen University Introduce DiffusionGPT: LLM-Driven Text-to-Image Generation System

  • Mohammad Asjad

This AI Paper from Meta and NYU Introduces Self-Rewarding Language Models that are Capable of Self-Alignment via Judging and Training on their Own Generations

  • Mohammad Asjad

Researchers from the University of Washington and Allen Institute for AI Present Proxy-Tuning: An Efficient Alternative to Finetuning Large Language Models

  • Mohammad Asjad

This AI Paper Introduces XAI-AGE: A Groundbreaking Deep Neural Network for Biological Age Prediction and Insight into Epigenetic Mechanisms

  • Mohammad Asjad

Stanford Researchers Introduce Clover: Closed-Loop Verifiable Code Generation that Checks Consistencies Among Code, Doc Strings and Annotations and Enforces Correctness in AI-Generated Code

  • Mohammad Asjad

This AI Paper from China Unveils ‘Activation Beacon’: A Groundbreaking AI Technique to Expand Context Understanding in Large Language Models

  • Mohammad Asjad

AWS Researchers Propose Panda: A New Machine Learning Framework to Provide Context Grounding to Pre-Trained LLMs

  • Mohammad Asjad

NTU and Meta Researchers Introduce URHand: A Universal Relightable Hand AI Model that Generalizes Across Viewpoints, Poses, Illuminations, and Identities

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