47 citations · 50 across the 4 of their papers we have counts for
6 papers
After Retrieval, Before Generation: Enhancing the Trustworthiness of Large Language Models in Retrieval-Augmented Generation
Xinbang Dai, Huikang Hu, Yuncheng Hua +5
Retrieval-augmented generation (RAG) is a promising paradigm, yet its trustworthiness remains a critical concern. A major vulnerability arises prior to generation: models often fai…
CoTKR: Chain-of-Thought Enhanced Knowledge Rewriting for Complex Knowledge Graph Question Answering
Yike Wu, Yi Huang, Nan Hu +4
Recent studies have explored the use of Large Language Models (LLMs) with Retrieval Augmented Generation (RAG) for Knowledge Graph Question Answering (KGQA). They typically require…
A Low-Cost, Controllable and Interpretable Task-Oriented Chatbot: With Real-World After-Sale Services as Example
Xiangyu Xi, Chenxu Lv, Yuncheng Hua +5
Though widely used in industry, traditional task-oriented dialogue systems suffer from three bottlenecks: (i) difficult ontology construction (e.g., intents and slots); (ii) poor c…
Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge Base
Yongrui Chen, Huiying Li, Yuncheng Hua +1
Formal query building is an important part of complex question answering over knowledge bases. It aims to build correct executable queries for questions. Recent methods try to rank…
Less is More: Data-Efficient Complex Question Answering over Knowledge Bases
Yuncheng Hua, Yuan-Fang Li, Guilin Qi +3
Question answering is an effective method for obtaining information from knowledge bases (KB). In this paper, we propose the Neural-Symbolic Complex Question Answering (NS-CQA) mod…
Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning
Yuncheng Hua, Yuan-Fang Li, Gholamreza Haffari +2
Complex question-answering (CQA) involves answering complex natural-language questions on a knowledge base (KB). However, the conventional neural program induction (NPI) approach e…