Top Important LLM Papers for the Week from 19/02 to 25/02
Stay Updated with Recent Large Language Models Research
Large language models (LLMs) have advanced rapidly in recent years. As new generations of models are developed, researchers and engineers need to stay informed on the latest progress. This article summarizes some of the most important LLM papers published during the Fourth Week of February 2024.
The papers cover various topics shaping the next generation of language models, from model optimization and scaling to reasoning, benchmarking, and enhancing performance. Keeping up with novel LLM research across these domains will help guide continued progress toward models that are more capable, robust, and aligned with human values.
Table of Contents:
LLM Progress & Benchmarking
LLM Reasoning
LLM Training & Evaluation
Transformers & Attention Based Models
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1. LLM Progress & Benchmarking
OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement
Copilot Evaluation Harness: Evaluating LLM-Guided Software Programming
LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens
User-LLM: Efficient LLM Contextualization with User Embeddings
FinTral: A Family of GPT-4 Level Multimodal Financial Large Language Models
AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling
Large Language Models as Zero-shot Dialogue State Tracker through Function Calling
DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows
LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration
In Search of Needles in a 10M Haystack: Recurrent Memory Finds What LLMs Miss
2. LLM Reasoning &Â Planning
3. LLM Training, Evaluation & InferenceÂ
Speculative Streaming: Fast LLM Inference without Auxiliary Models
RLVF: Learning from Verbal Feedback without Overgeneralization
LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Models
4. LLM Fine Tuning
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Instruction-tuned Language Models are Better Knowledge Learners
How Easy Is It to Fool Your Multimodal LLMs? An Empirical Analysis of Deceptive Prompts
5. Transformers & Attention Based Models
BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay
Linear Transformers with Learnable Kernel Functions are Better In-Context Models
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