Top Important LLM Papers for the Week from 04/12 to 10/12

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 second week of December.

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 Fine Tuning

3. LLM Reasoning

4. LLM Training & Optimization

5. Transformers & Attention Models

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1. LLM Progress & Benchmarking

1. Large Language Models for Mathematicians

2. SeaLLMs — Large Language Models for Southeast Asia

3. Pearl: A Production-ready Reinforcement Learning Agent

4. Using Large Language Models to Accelerate Communication for Users with Severe Motor Impairments

5. TextGenSHAP: Scalable Post-hoc Explanations in Text Generation with Long Documents

6. Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized Model Responses

7. Axiomatic Preference Modeling for Longform Question Answering

8. WhisBERT: Multimodal Text-Audio Language Modeling on 100M Words

9. Generating Fine-Grained Human Motions Using ChatGPT-Refined Descriptions

10. Magicoder: Source Code Is All You Need

11. The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning

12. Nash Learning from Human Feedback

13. RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback

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2. LLM Fine Tuning

1. Instruction-tuning Aligns LLMs to the Human Brain

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3. LLM Reasoning

1. Chain of Code: Reasoning with a Language Model-Augmented Code Emulator

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4. LLM Training & Optimization

1. OneLLM: One Framework to Align All Modalities with Language

2. Rank-without-GPT: Building GPT-Independent Listwise Rerankers on Open-Source Large Language Models

3. Beyond Surface: Probing LLaMA Across Scales and Layers

4. Training Chain-of-Thought via Latent-Variable Inference

5. Towards Accurate Differential Diagnosis with Large Language Models

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5. Transformers & Attention Models 

1. Efficient Monotonic Multihead Attention

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