3 citations · 8 across the 41 of their papers we have counts for
6 papers · 1 filter
Model-based Preference Optimization in Abstractive Summarization without Human Feedback
Jaepill Choi, Kyubyung Chae, Jiwoo Song +2
In abstractive summarization, the challenge of producing concise and accurate summaries arises from the vast amount of information contained in the source document. Consequently, a…
Towards Lifelong Dialogue Agents via Timeline-based Memory Management
Kai Tzu-iunn Ong, Namyoung Kim, Minju Gwak +6
To achieve lifelong human-agent interaction, dialogue agents need to constantly memorize perceived information and properly retrieve it for response generation (RG). While prior st…
Mitigating Hallucination in Abstractive Summarization with Domain-Conditional Mutual Information
Kyubyung Chae, Jaepill Choi, Yohan Jo +1
A primary challenge in abstractive summarization is hallucination -- the phenomenon where a model generates plausible text that is absent in the source text. We hypothesize that th…
Ever-Evolving Memory by Blending and Refining the Past
Seo Hyun Kim, Keummin Ka, Yohan Jo +3
For a human-like chatbot, constructing a long-term memory is crucial. However, current large language models often lack this capability, leading to instances of missing important u…
Ask Optimal Questions: Aligning Large Language Models with Retriever's Preference in Conversation
Chanwoong Yoon, Gangwoo Kim, Byeongguk Jeon +3
Conversational search, unlike single-turn retrieval tasks, requires understanding the current question within a dialogue context. The common approach of rewrite-then-retrieve aims…
Generalizing Visual Question Answering from Synthetic to Human-Written Questions via a Chain of QA with a Large Language Model
Taehee Kim, Yeongjae Cho, Heejun Shin +2
Visual question answering (VQA) is a task where an image is given, and a series of questions are asked about the image. To build an efficient VQA algorithm, a large amount of QA da…