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cs.CL2025
Investigating the Impact of Rationales for LLMs on Natural Language Understanding
Wenhang Shi, Shuqing Bian, Yiren Chen +5
Chain-of-thought (CoT) rationales, which provide step-by-step reasoning to derive final answers, benefit LLMs in both inference and training. Incorporating rationales, either by ge…
cs.CL2025
No Loss, No Gain: Gated Refinement and Adaptive Compression for Prompt Optimization
Wenhang Shi, Yiren Chen, Shuqing Bian +6
Prompt engineering is crucial for leveraging the full potential of large language models (LLMs). While automatic prompt optimization offers a scalable alternative to costly manual…
cs.CL2024
Joint Knowledge Editing for Information Enrichment and Probability Promotion
Wenhang Shi, Yiren Chen, Shuqing Bian +5
Knowledge stored in large language models requires timely updates to reflect the dynamic nature of real-world information. To update the knowledge, most knowledge editing methods f…