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cs.CL2026
Escaping the Self-Confirmation Trap: An Execute-Distill-Verify Paradigm for Agentic Experience Learning
Shiding Zhu, Yudi Qi, Yajie Wang +6
Experience-driven self-evolution is critical for large language model (LLM) agents to improve through open-world interaction. However, existing experience learning methods mostly r…
cs.CL2024
SAFETY-J: Evaluating Safety with Critique
Yixiu Liu, Yuxiang Zheng, Shijie Xia +4
The deployment of Large Language Models (LLMs) in content generation raises significant safety concerns, particularly regarding the transparency and interpretability of content eva…
cs.CL2024
A Framework to Implement 1+N Multi-task Fine-tuning Pattern in LLMs Using the CGC-LORA Algorithm
Chao Song, Zhihao Ye, Qiqiang Lin +2
With the productive evolution of large language models (LLMs) in the field of natural language processing (NLP), tons of effort has been made to effectively fine-tune common pre-tr…