최은성 석박통합과정, 박준 석사과정, 이혜리 석사과정 EMNLP 2025 국제 학술대회 논문 채택

21 Aug 2025

DIAL 연구실 소속 인공지능학과 최은성(석박통합과정, 제1저자) 학생, 박준(석사과정, 제2저자) 학생, 이혜리(석사과정, 제3저자) 학생, 이종욱(교신저자) 교수가 참여한 논문 "Conflict-Aware Soft Prompting for Retrieval-Augmented Generation"이 자연어처리 분야 최우수 국제 학술대회인 The 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025)에 게재 승인되었으며 오는 11월에 발표될 예정입니다. 

Abstract

Retrieval-augmented generation (RAG) enhances the capabilities of large language models (LLMs) by incorporating external knowledge into their input prompts. However, when the retrieved context contradicts the LLM’s parametric knowledge, it often fails to resolve the conflict between incorrect external context and correct parametric knowledge, known as context-memory conflict. To tackle this problem, we introduce Conflict-Aware REtrieval-Augmented Generation (CARE), consisting of a context assessor and a base LLM. The context assessor encodes compact memory token embeddings from raw context tokens. Through grounded/adversarial soft prompting, the context assessor is trained to discern unreliable context and capture a guidance signal that directs reasoning toward the more reliable knowledge source. Extensive experiments show that CARE effectively mitigates context-memory conflicts, leading to an average performance gain of 5.0% on QA and fact-checking benchmarks, establishing a promising direction for trustworthy and adaptive RAG systems.