2 citations · 7 across the 21 of their papers we have counts for
10 papers · 1 filter
From Awareness to Adherence: Bridging the Context Gap in Spoken Dialogue Systems via Context-Aware Decoding
Che Hyun Lee, Heeseung Kim, Sungroh Yoon
Despite the success of end-to-end (E2E) spoken dialogue systems, maintaining strict context adherence in multi-round conversations remains a challenge. While prior works attribute…
Knowledge Integration Decay in Search-Augmented Reasoning of Large Language Models
Sangwon Yu, Ik-hwan Kim, Donghun Kang +6
Modern Large Language Models (LLMs) have demonstrated remarkable capabilities in complex tasks by employing search-augmented reasoning to incorporate external knowledge into long c…
A Multifaceted Analysis of Negative Bias in Large Language Models through the Lens of Parametric Knowledge
Jongyoon Song, Sangwon Yu, Sungroh Yoon
Negative bias refers to the tendency of large language models (LLMs) to excessively generate negative responses in binary decision tasks (e.g., yes-no question answering). Previous…
Unleashing Multi-Hop Reasoning Potential in Large Language Models through Repetition of Misordered Context
Sangwon Yu, Ik-hwan Kim, Jongyoon Song +3
Multi-hop reasoning, which requires multi-step reasoning based on the supporting documents within a given context, remains challenging for large language models (LLMs). LLMs often…
Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment
Sangwon Yu, Jongyoon Song, Bongkyu Hwang +7
A binary decision task, like yes-no questions or answer verification, reflects a significant real-world scenario such as where users look for confirmation about the correctness of…
Large Language Models are Skeptics: False Negative Problem of Input-conflicting Hallucination
Jongyoon Song, Sangwon Yu, Sungroh Yoon
In this paper, we identify a new category of bias that induces input-conflicting hallucinations, where large language models (LLMs) generate responses inconsistent with the content…
Entity-level Factual Adaptiveness of Fine-tuning based Abstractive Summarization Models
Jongyoon Song, Nohil Park, Bongkyu Hwang +4
Abstractive summarization models often generate factually inconsistent content particularly when the parametric knowledge of the model conflicts with the knowledge in the input doc…
Paralinguistics-Aware Speech-Empowered Large Language Models for Natural Conversation
Heeseung Kim, Soonshin Seo, Kyeongseok Jeong +9
Recent work shows promising results in expanding the capabilities of large language models (LLM) to directly understand and synthesize speech. However, an LLM-based strategy for mo…
On the Analysis of Cross-Lingual Prompt Tuning for Decoder-based Multilingual Model
Nohil Park, Joonsuk Park, Kang Min Yoo +1
An exciting advancement in the field of multilingual models is the emergence of autoregressive models with zero- and few-shot capabilities, a phenomenon widely reported in large-sc…
Controlled Text Generation for Black-box Language Models via Score-based Progressive Editor
Sangwon Yu, Changmin Lee, Hojin Lee +1
Controlled text generation is very important for the practical use of language models because it ensures that the produced text includes only the desired attributes from a specific…