4 papers
Dual-Scale World Models for LLM Agents Towards Hard-Exploration Problems
Minsoo Kim, Seung-won Hwang
LLM-based agents have seen promising advances, yet they are still limited in "hard-exploration" tasks requiring learning new knowledge through exploration. We present GLoW, a novel…
CoEx -- Co-evolving World-model and Exploration
Minsoo Kim, Seung-won Hwang
Planning in modern LLM agents relies on the utilization of LLM as an internal world model, acquired during pretraining. However, existing agent designs fail to effectively assimila…
Agent-as-Judge for Factual Summarization of Long Narratives
Yeonseok Jeong, Minsoo Kim, Seung-won Hwang +1
Large Language Models (LLMs) have demonstrated near-human performance in summarization tasks based on traditional metrics such as ROUGE and BERTScore. However, these metrics do not…
Disentangling Questions from Query Generation for Task-Adaptive Retrieval
Yoonsang Lee, Minsoo Kim, Seung-won Hwang
This paper studies the problem of information retrieval, to adapt to unseen tasks. Existing work generates synthetic queries from domain-specific documents to jointly train the ret…