1 citations · 1 across the 8 of their papers we have counts for
9 papers
LLMCodec: Adapting Video Codecs for Efficient Weight Compression of Large Language Models
Rui Wang, Yan Zhao, Li Song +1
The rapid development of large language models(LLMs) has led to remarkable advances in natural language processing. However, the increasing scale of these models introduces substan…
Automated Proving of Shannon-Type Entropy Inequalities via Fine-Tuned Language Models and Guided Tree Search
Shing Yin Wong, Shaocheng Liu, Linqi Song +2
Proving Shannon-type entropy inequalities is a fundamental task in information theory that often requires constructing non-trivial linear combinations of known constraints, which i…
Reasoning in Action: MCTS-Driven Knowledge Retrieval for Large Language Models
Shuqi Liu, Bowei He, Chen Ma +1
Large language models (LLMs) typically enhance their performance through either the retrieval of semantically similar information or the improvement of their reasoning capabilities…
Activation-Guided Consensus Merging for Large Language Models
Yuxuan Yao, Shuqi Liu, Zehua Liu +6
Recent research has increasingly focused on reconciling the reasoning capabilities of System 2 with the efficiency of System 1. While existing training-based and prompt-based appro…
Sens-Merging: Sensitivity-Guided Parameter Balancing for Merging Large Language Models
Shuqi Liu, Han Wu, Bowei He +3
Recent advances in large language models have led to numerous task-specialized fine-tuned variants, creating a need for efficient model merging techniques that preserve specialized…
1bit-Merging: Dynamic Quantized Merging for Large Language Models
Shuqi Liu, Yuxuan Yao, Bowei He +5
Recent advances in large language models have led to specialized models excelling in specific domains, creating a need for efficient model merging techniques. While traditional mer…
Beyond One-Size-Fits-All Pruning via Evolutionary Metric Search for Large Language Models
Shuqi Liu, Bowei He, Han Wu +1
Post-training pruning has emerged as a crucial optimization technique as large language models (LLMs) continue to grow rapidly. However, the significant variations in weight distri…
Learning From Correctness Without Prompting Makes LLM Efficient Reasoner
Yuxuan Yao, Han Wu, Zhijiang Guo +6
Large language models (LLMs) have demonstrated outstanding performance across various tasks, yet they still exhibit limitations such as hallucination, unfaithful reasoning, and tox…
Fine-grained Conversational Decoding via Isotropic and Proximal Search
Yuxuan Yao, Han Wu, Qiling Xu +1
General-purpose text decoding approaches are usually adopted for dialogue response generation. Although the quality of the generated responses can be improved with dialogue-specifi…