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Enhancing Multilingual RAG Systems with Debiased Language Preference-Guided Query Fusion
Jeonghyun Park, Byeongjeong Kim, Seojin Hwang +1
Multilingual Retrieval-Augmented Generation (mRAG) systems often exhibit a perceived preference for high-resource languages, particularly English, resulting in the widespread adopt…
Chronological Passage Assembling in RAG framework for Temporal Question Answering
Byeongjeong Kim, Jeonghyun Park, Joonho Yang +1
Long-context question answering over narrative tasks is challenging because correct answers often hinge on reconstructing a coherent timeline of events while preserving contextual…
Hallucinate at the Last in Long Response Generation: A Case Study on Long Document Summarization
Joonho Yang, Seunghyun Yoon, Hwan Chang +2
Large Language Models (LLMs) have significantly advanced text generation capabilities, including tasks like summarization, often producing coherent and fluent outputs. However, fai…
Personality Editing for Language Models through Adjusting Self-Referential Queries
Seojin Hwang, Yumin Kim, Byeongjeong Kim +2
Large Language Models (LLMs) are integral to applications such as conversational agents and content creation, where precise control over a model's personality is essential for main…
SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example Selection for Text-to-SQL
Jimin Lee, Ingeol Baek, Byeongjeong Kim +2
Text-to-SQL aims to convert natural language questions into executable SQL queries. While previous approaches, such as skeleton-masked selection, have demonstrated strong performan…
Dynamic Label Name Refinement for Few-Shot Dialogue Intent Classification
Gyutae Park, Ingeol Baek, ByeongJeong Kim +2
Dialogue intent classification aims to identify the underlying purpose or intent of a user's input in a conversation. Current intent classification systems encounter considerable c…
Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval
Ingeol Baek, Hwan Chang, Byeongjeong Kim +2
Retrieval-Augmented Generation (RAG) enhances language models by retrieving and incorporating relevant external knowledge. However, traditional retrieve-and-generate processes may…
FIZZ: Factual Inconsistency Detection by Zoom-in Summary and Zoom-out Document
Joonho Yang, Seunghyun Yoon, Byeongjeong Kim +1
Through the advent of pre-trained language models, there have been notable advancements in abstractive summarization systems. Simultaneously, a considerable number of novel methods…