3 citations · 9 across the 12 of their papers we have counts for
6 papers · 1 filter
Where Did This Sentence Come From? Tracing Provenance in LLM Reasoning Distillation
Kaiyuan Liu, Shaotian Yan, Rui Miao +4
Reasoning distillation has attracted increasing attention. It typically leverages a large teacher model to generate reasoning paths, which are then used to fine-tune a student mode…
SalaMAnder: Shapley-based Mathematical Expression Attribution and Metric for Chain-of-Thought Reasoning
Yue Xin, Chen Shen, Shaotian Yan +5
Chain-of-Thought (CoT) prompting enhances the math reasoning capability of large language models (LLMs) to a large margin. However, the mechanism underlying such improvements remai…
Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning
Chenxi Huang, Shaotian Yan, Liang Xie +6
Representation Fine-tuning (ReFT), a recently proposed Parameter-Efficient Fine-Tuning (PEFT) method, has attracted widespread attention for significantly improving parameter effic…
Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering
Xiao Long, Liansheng Zhuang, Chen Shen +3
Recently, large language models (LLMs) have demonstrated impressive performance in Knowledge Graph Question Answering (KGQA) tasks, which aim to find answers based on knowledge gra…
Improving Complex Reasoning with Dynamic Prompt Corruption: A soft prompt Optimization Approach
Sinan Fan, Liang Xie, Chen Shen +7
Prompt-tuning (PT) for large language models (LLMs) can facilitate the performance on various conventional NLP tasks with significantly fewer trainable parameters. However, our inv…
Don't Take Things Out of Context: Attention Intervention for Enhancing Chain-of-Thought Reasoning in Large Language Models
Shaotian Yan, Chen Shen, Wenxiao Wang +3
Few-shot Chain-of-Thought (CoT) significantly enhances the reasoning capabilities of large language models (LLMs), functioning as a whole to guide these models in generating reason…