8 papers
SpanUQ: Span-Level Uncertainty Quantification for Large Language Model Generation
Yimeng Zhang, Yingying Zhuang, Ziyi Wang +12
Uncertainty estimation is essential not only for the trustworthy deployment of large language models (LLMs) but also as a foundation for self-refinement in LLM generation. However,…
PACE: Two-Timescale Self-Evolution for Small Language Model Agents
Chen Ling, Pei Chen, Albert Guan +4
Deploying language-model agents in production often requires substantial compute and human effort to tune prompts, parsers, validators, and other components of the agent pipeline.…
END: Early Noise Dropping for Efficient and Effective Context Denoising
Hongye Jin, Pei Chen, Jingfeng Yang +11
Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks. However, they are often distracted by irrelevant or…
Mitigating Lost in Multi-turn Conversation via Curriculum RL with Verifiable Accuracy and Abstention Rewards
Ming Li, Pei Chen, Zhenhao Zhang +10
Large Language Models demonstrate strong capabilities in single-turn instruction following but suffer from Lost-in-Conversation (LiC), a degradation in performance as information i…
Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templates
Hy Dang, Tianyi Liu, Zhuofeng Wu +9
Large language models (LLMs) have demonstrated strong reasoning and tool-use capabilities, yet they often fail in real-world tool-interactions due to incorrect parameterization, po…
UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations
Fengran Mo, Yifan Gao, Chuan Meng +9
The rapid advancement of conversational search systems revolutionizes how information is accessed by enabling the multi-turn interaction between the user and the system. Existing c…