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20242026
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cs.CL2025

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…

cs.CL2025

SciPIP: An LLM-based Scientific Paper Idea Proposer

Wenxiao Wang, Lihui Gu, Liye Zhang +7

The rapid advancement of large language models (LLMs) has opened new possibilities for automating the proposal of innovative scientific ideas. This process involves two key phases:…

cs.CL2025

From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning

Wei Chen, Zhen Huang, Liang Xie +9

Large Language Models (LLMs) tend to prioritize adherence to user prompts over providing veracious responses, leading to the sycophancy issue. When challenged by users, LLMs tend t…

cs.CL2025

InsQABench: Benchmarking Chinese Insurance Domain Question Answering with Large Language Models

Jing Ding, Kai Feng, Binbin Lin +6

The application of large language models (LLMs) has achieved remarkable success in various fields, but their effectiveness in specialized domains like the Chinese insurance industr…

cs.CL2024

Delving into the Reversal Curse: How Far Can Large Language Models Generalize?

Zhengkai Lin, Zhihang Fu, Kai Liu +6

While large language models (LLMs) showcase unprecedented capabilities, they also exhibit certain inherent limitations when facing seemingly trivial tasks. A prime example is the r…

cs.CL2024

Enhancing Multiple Dimensions of Trustworthiness in LLMs via Sparse Activation Control

Yuxin Xiao, Chaoqun Wan, Yonggang Zhang +5

As the development and application of Large Language Models (LLMs) continue to advance rapidly, enhancing their trustworthiness and aligning them with human preferences has become…