5 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…
Interventional Speech Noise Injection for ASR Generalizable Spoken Language Understanding
Yeonjoon Jung, Jaeseong Lee, Seungtaek Choi +3
Recently, pre-trained language models (PLMs) have been increasingly adopted in spoken language understanding (SLU). However, automatic speech recognition (ASR) systems frequently p…
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…