6 papers
ClarifyMT-Bench: Benchmarking and Improving Multi-Turn Clarification for Conversational Large Language Models
Sichun Luo, Yi Huang, Mukai Li +5
Large language models (LLMs) are increasingly deployed as conversational assistants in open-domain, multi-turn settings, where users often provide incomplete or ambiguous informati…
Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems
Yucheng Cai, Yuxuan Wu, Yi Huang +2
Large language models (LLMs) have recently been applied to dialog systems. Despite making progress, LLMs are prone to errors in knowledge-intensive scenarios. Recently, approaches…
Entriever: Energy-based Retriever for Knowledge-Grounded Dialog Systems
Yucheng Cai, Ke Li, Yi Huang +2
A retriever, which retrieves relevant knowledge pieces from a knowledge base given a context, is an important component in many natural language processing (NLP) tasks. Retrievers…
From Superficial to Deep: Integrating External Knowledge for Follow-up Question Generation Using Knowledge Graph and LLM
Jianyu Liu, Yi Huang, Sheng Bi +2
In a conversational system, dynamically generating follow-up questions based on context can help users explore information and provide a better user experience. Humans are usually…
Palette of Language Models: A Solver for Controlled Text Generation
Zhe Yang, Yi Huang, Yaqin Chen +3
Recent advancements in large language models have revolutionized text generation with their remarkable capabilities. These models can produce controlled texts that closely adhere t…
Harnessing Diverse Perspectives: A Multi-Agent Framework for Enhanced Error Detection in Knowledge Graphs
Yu Li, Yi Huang, Guilin Qi +7
Knowledge graphs are widely used in industrial applications, making error detection crucial for ensuring the reliability of downstream applications. Existing error detection method…