5 papers
SWE-Exp: Experience-Driven Software Issue Resolution
Silin Chen, Shaoxin Lin, Yuling Shi +8
Recent advances in large language model (LLM) agents have shown remarkable progress in software issue resolution, leveraging advanced techniques such as multi-agent collaboration a…
From Biased Chatbots to Biased Agents: Examining Role Assignment Effects on LLM Agent Robustness
Linbo Cao, Lihao Sun, Yang Yue
Large Language Models (LLMs) are increasingly deployed as autonomous agents capable of actions with real-world impacts beyond text generation. While persona-induced biases in text…
Pretraining on the Test Set Is No Longer All You Need: A Debate-Driven Approach to QA Benchmarks
Linbo Cao, Jinman Zhao
As frontier language models increasingly saturate standard QA benchmarks, concerns about data contamination, memorization, and escalating dataset creation costs persist. We propose…
UORA: Uniform Orthogonal Reinitialization Adaptation in Parameter-Efficient Fine-Tuning of Large Models
Xueyan Zhang, Jinman Zhao, Zhifei Yang +4
This paper introduces Uniform Orthogonal Reinitialization Adaptation (UORA), a novel parameter-efficient fine-tuning (PEFT) approach for Large Language Models (LLMs). UORA achieves…
Role-Play Paradox in Large Language Models: Reasoning Performance Gains and Ethical Dilemmas
Jinman Zhao, Zifan Qian, Linbo Cao +5
Role-play in large language models (LLMs) enhances their ability to generate contextually relevant and high-quality responses by simulating diverse cognitive perspectives. However,…