4 papers
Eliciting Chain-of-Thought in Base LLMs via Gradient-Based Representation Optimization
Zijian Wang, Yanxiang Ma, Chang Xu
Chain-of-Thought (CoT) reasoning is a critical capability for large language models (LLMs), enabling them to tackle com- plex multi-step tasks. While base LLMs, pre-trained on gene…
ThoughtProbe: Classifier-Guided LLM Thought Space Exploration via Probing Representations
Zijian Wang, Chang Xu
This paper introduces ThoughtProbe, a novel inference time framework that leverages the hidden reasoning features of Large Language Models (LLMs) to improve their reasoning perform…
Functional Abstraction of Knowledge Recall in Large Language Models
Zijian Wang, Chang Xu
Pre-trained transformer large language models (LLMs) demonstrate strong knowledge recall capabilities. This paper investigates the knowledge recall mechanism in LLMs by abstracting…
ThoughtProbe: Classifier-Guided Thought Space Exploration Leveraging LLM Intrinsic Reasoning
Zijian Wang, Chang Xu
Pre-trained large language models (LLMs) have been demonstrated to possess intrinsic reasoning capabilities that can emerge naturally when expanding the response space. However, th…