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:

1. LLM Progress & Benchmarking

2. LLM Reasoning

3. LLM Training & Evaluation

4. Transformers & Attention Based Models

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1. LLM Progress & Benchmarking

1. OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement

2. PALO: A Polyglot Large Multimodal Model for 5B People

3. OmniPred: Language Models as Universal Regressors

4. Copilot Evaluation Harness: Evaluating LLM-Guided Software Programming

5. Scaling Up LLM Reviews for Google Ads Content Moderation

6. LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens

7. User-LLM: Efficient LLM Contextualization with User Embeddings

8. FinTral: A Family of GPT-4 Level Multimodal Financial Large Language Models

9. AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling

10. OneBit: Towards Extremely Low-bit Large Language Models

11. Coercing LLMs to do and reveal (almost) anything

12. Large Language Models as Zero-shot Dialogue State Tracker through Function Calling

13. DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows

14. LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration

15. In Search of Needles in a 10M Haystack: Recurrent Memory Finds What LLMs Miss

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2. LLM Reasoning & Planning

1. Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping

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3. LLM Training, Evaluation & Inference 

1. Speculative Streaming: Fast LLM Inference without Auxiliary Models

2. RLVF: Learning from Verbal Feedback without Overgeneralization

3. LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Models

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4. LLM Fine Tuning

1. Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models

2. Instruction-tuned Language Models are Better Knowledge Learners

3. How Easy Is It to Fool Your Multimodal LLMs? An Empirical Analysis of Deceptive Prompts

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5. Transformers & Attention Based Models

1. Linear Transformers are Versatile In-Context Learners

2. BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay

3. Linear Transformers with Learnable Kernel Functions are Better In-Context Models

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