Top Important LLM Papers for the Week from 01/01 to 07/01
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 first week of January.
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 Fine Tuning
LLM Reasoning
LLM Training & Evaluation
Transformers & Attention Based Models
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1. LLM Progress & Benchmarking
Boosting Large Language Model for Speech Synthesis: An Empirical Study
PanGu-$Ï€$: Enhancing Language Model Architectures via Nonlinearity Compensation
COSMO: COntrastive Streamlined MultimOdal Model with Interleaved Pre-Training
GeoGalactica: A Scientific Large Language Model in Geoscience
LLaMA Beyond English: An Empirical Study on Language Capability Transfer
DocLLM: A layout-aware generative language model for multimodal document understanding
LLM Augmented LLMs: Expanding Capabilities through Composition
LLaVA-$φ$: Efficient Multi-Modal Assistant with Small Language Model
A Comprehensive Study of Knowledge Editing for Large Language Models
2. LLM Fine Tuning
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models
3. LLM Reasoning
Towards Truly Zero-shot Compositional Visual Reasoning with LLMs as Programmers
Gemini in Reasoning: Unveiling Commonsense in Multimodal Large Language Models
4. LLM Training & Evaluation
Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws
Understanding LLMs: A Comprehensive Overview from Training to Inference
5. Transformers & Attention Based Models
Boundary Attention: Learning to Find Faint Boundaries at Any Resolution
ICE-GRT: Instruction Context Enhancement by Generative Reinforcement-based Transformers
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