32 citations · 33 across the 6 of their papers we have counts for
6 papers
JsonTuning: Towards Generalizable, Robust, and Controllable Instruction Tuning
Chang Gao, Wenxuan Zhang, Guizhen Chen +1
Instruction tuning is vital for enhancing the performance of large language models (LLMs), but existing text-to-text methods, referred to as TextTuning, struggle with issues such a…
M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models
Wenxuan Zhang, Sharifah Mahani Aljunied, Chang Gao +2
Despite the existence of various benchmarks for evaluating natural language processing models, we argue that human exams are a more suitable means of evaluating general intelligenc…
Easy-to-Hard Learning for Information Extraction
Chang Gao, Wenxuan Zhang, Wai Lam +1
Information extraction (IE) systems aim to automatically extract structured information, such as named entities, relations between entities, and events, from unstructured texts. Wh…
Prompt Conditioned VAE: Enhancing Generative Replay for Lifelong Learning in Task-Oriented Dialogue
Yingxiu Zhao, Yinhe Zheng, Zhiliang Tian +6
Lifelong learning (LL) is vital for advanced task-oriented dialogue (ToD) systems. To address the catastrophic forgetting issue of LL, generative replay methods are widely employed…
Towards Generalizable and Robust Text-to-SQL Parsing
Chang Gao, Bowen Li, Wenxuan Zhang +5
Text-to-SQL parsing tackles the problem of mapping natural language questions to executable SQL queries. In practice, text-to-SQL parsers often encounter various challenging scenar…
UniGDD: A Unified Generative Framework for Goal-Oriented Document-Grounded Dialogue
Chang Gao, Wenxuan Zhang, Wai Lam
The goal-oriented document-grounded dialogue aims at responding to the user query based on the dialogue context and supporting document. Existing studies tackle this problem by dec…