5 papers
Source Preference in the Wild: How LLM Agents Favor Items by Source, and How to Reduce It
Jonghyun Song, Haewon Park, Jeonghoon Shim +2
As LLM agents decide on users' behalf which product to buy, which hotel to book, or which paper to cite, a preference for items from certain sources (the sites or services they com…
AgentHabit: Characterizing Distinct Behaviors of Agents on Everyday Tasks
Woojung Song, Hoyeol Yang, Jeonghoon Shim +4
Large language model (LLM) agents assist users with everyday tasks that can be completed in many reasonable ways. Even when their answers are useful, how agents carry out these tas…
Your Language Model is Its Own Critic: Reinforcement Learning with Value Estimation from Actor's Internal States
Yunho Choi, Jongwon Lim, Woojin Ahn +3
Reinforcement learning with verifiable rewards (RLVR) for Large Reasoning Models rests on variance reduction, which requires both a reliable baseline and high prompt diversity with…
Non-Collaborative User Simulators for Tool Agents
Jeonghoon Shim, Woojung Song, Cheyon Jin +2
Tool agents interact with users through multi-turn dialogues to accomplish various tasks. Recent studies have adopted user simulation methods to develop these agents in multi-turn…
ToolDial: Multi-turn Dialogue Generation Method for Tool-Augmented Language Models
Jeonghoon Shim, Gyuhyeon Seo, Cheongsu Lim +1
Tool-Augmented Language Models (TALMs) leverage external APIs to answer user queries across various domains. However, existing benchmark datasets for TALM research often feature si…