6 papers · 1 filter
Transforming External Knowledge into Triplets for Enhanced Retrieval in RAG of LLMs
Xudong Wang, Chaoning Zhang, Qigan Sun +7
Retrieval-Augmented Generation (RAG) mitigates hallucination in large language models (LLMs) by incorporating external knowledge during generation. However, the effectiveness of RA…
TDA-RC: Task-Driven Alignment for Knowledge-Based Reasoning Chains in Large Language Models
Jiaquan Zhang, Qigan Sun, Chaoning Zhang +11
Enhancing the reasoning capability of large language models (LLMs) remains a core challenge in natural language processing. The Chain-of-Thought (CoT) paradigm dominates practical…
Optimizing Soft Prompt Tuning via Structural Evolution
Zhenzhen Huang, Chaoning Zhang, Haoyu Bian +8
Soft prompt tuning leverages continuous embeddings to capture task-specific information in large pre-trained language models (LLMs), achieving competitive performance in few-shot s…
Text summarization via global structure awareness
Jiaquan Zhang, Chaoning Zhang, Shuxu Chen +9
Text summarization is a fundamental task in natural language processing (NLP), and the information explosion has made long-document processing increasingly demanding, making summar…
Syzygy of Thoughts: Improving LLM CoT with the Minimal Free Resolution
Chenghao Li, Chaoning Zhang, Yi Lu +7
Chain-of-Thought (CoT) prompting enhances the reasoning of large language models (LLMs) by decomposing problems into sequential steps, mimicking human logic and reducing errors. Ho…
Beyond Performance: Quantifying and Mitigating Label Bias in LLMs
Yuval Reif, Roy Schwartz
Large language models (LLMs) have shown remarkable adaptability to diverse tasks, by leveraging context prompts containing instructions, or minimal input-output examples. However,…