5 papers
Online Learning with LLM Experts from Limited Feedback
Wang Wei, Soumyabrata Pal, Koyel Mukherjee +4
We study adaptive routing of prompts to large language model (LLM) experts to maximize response quality in an online setting with limited feedback. We formulate it as a bandit prob…
SeqRoute: Global Budget-Aware Sequential LLM Routing via Offline Reinforcement Learning
Zhongling Xu, Shunan Zheng, Wei Wang
Existing LLM routing frameworks treat queries as independent events, neglecting the sequential nature of real-world user sessions constrained by global computational budgets. This…
Structure-Aware RAG: Structured Retrieval Augmented Generation from Noisy Data for Conversational Agents
Kaiqiao Han, LuAn Tang, Renliang Sun +6
Large Language Models (LLMs) have been widely adopted in conversational applications. However, their reliance on parametric knowledge limits reliability in real-world scenarios tha…
Stop When Enough: Adaptive Early-Stopping for Chain-of-Thought Reasoning
Renliang Sun, Wei Cheng, Dawei Li +2
Chain-of-Thought (CoT) reasoning has driven recent gains of large language models (LLMs) on reasoning-intensive tasks by externalizing intermediate steps. However, excessive or red…
Preference Leakage: A Contamination Problem in LLM-as-a-judge
Dawei Li, Renliang Sun, Yue Huang +6
Large Language Models (LLMs) as judges and LLM-based data synthesis have emerged as two fundamental LLM-driven data annotation methods in model development. While their combination…