44 citations · 125 across the 19 of their papers we have counts for
8 papers · 1 filter
Constructive Large Language Models Alignment with Diverse Feedback
Tianshu Yu, Ting-En Lin, Yuchuan Wu +3
In recent research on large language models (LLMs), there has been a growing emphasis on aligning these models with human values to reduce the impact of harmful content. However, c…
Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models
Haoyu Gao, Ting-En Lin, Hangyu Li +4
Task-oriented dialogue (TOD) systems facilitate users in executing various activities via multi-turn dialogues, but Large Language Models (LLMs) often struggle to comprehend these…
VDialogUE: A Unified Evaluation Benchmark for Visually-grounded Dialogue
Yunshui Li, Binyuan Hui, Zhaochao Yin +6
Visually-grounded dialog systems, which integrate multiple modes of communication such as text and visual inputs, have become an increasingly popular area of investigation. However…
PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional Experts
Yunshui Li, Binyuan Hui, ZhiChao Yin +3
Perceiving multi-modal information and fulfilling dialogues with humans is a long-term goal of artificial intelligence. Pre-training is commonly regarded as an effective approach f…
Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment
Tianshu Yu, Haoyu Gao, Ting-En Lin +6
Recently, speech-text pre-training methods have shown remarkable success in many speech and natural language processing tasks. However, most previous pre-trained models are usually…
Unsupervised Dialogue Topic Segmentation with Topic-aware Utterance Representation
Haoyu Gao, Rui Wang, Ting-En Lin +4
Dialogue Topic Segmentation (DTS) plays an essential role in a variety of dialogue modeling tasks. Previous DTS methods either focus on semantic similarity or dialogue coherence to…
Iterative Forward Tuning Boosts In-Context Learning in Language Models
Jiaxi Yang, Binyuan Hui, Min Yang +5
Despite the advancements in in-context learning (ICL) for large language models (LLMs), current research centers on specific prompt engineering, such as demonstration selection, wi…
Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning
Yunhu Ye, Binyuan Hui, Min Yang +3
Table-based reasoning has shown remarkable progress in combining deep models with discrete reasoning, which requires reasoning over both free-form natural language (NL) questions a…