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Chia-Chien Hung

6 accepted papers

2025

MEDDxAgent: A Unified Modular Agent Framework for Explainable Automatic Differential Diagnosis

Daniel Philip Rose, Chia-Chien Hung, Marco Lepri, Israa Alqassem, Kiril Gashteovski, Carolin Lawrence
ACL 2025long

Differential Diagnosis (DDx) is a fundamental yet complex aspect of clinical decision-making, in which physicians iteratively refine a ranked list of possible diseases based on symptoms, antecedents, and medical knowledge. While recent advances in large language models (LLMs) have shown promise in s…

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2025

On Synthesizing Data for Context Attribution in Question Answering

Gorjan Radevski, Kiril Gashteovski, Shahbaz Syed, Christopher Malon, Sebastien Nicolas, Chia-Chien Hung +8
ACL 2025long

Question Answering (QA) accounts for a significant portion of LLM usage in the wild”. However, LLMs sometimes produce false or misleading responses, also known as hallucinations”. Therefore, grounding the generated answers in contextually provided information—i.e., providing evidence for the generat…

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2023

Linking Surface Facts to Large-Scale Knowledge Graphs

Gorjan Radevski, Kiril Gashteovski, Chia-Chien Hung, Carolin Lawrence, Goran Glavaš
EMNLP 2023long main

Open Information Extraction (OIE) methods extract facts from natural language text in the form of ("subject"; "relation"; "object") triples. These facts are, however, merely surface forms, the ambiguity of which impedes their downstream usage; e.g., the surface phrase "Michael Jordan" may refer to e…

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2023

TADA: Efficient Task-Agnostic Domain Adaptation for Transformers

Chia-Chien Hung, Lukas Lange, Jannik Strötgen
ACL 2023findings

Intermediate training of pre-trained transformer-based language models on domain-specific data leads to substantial gains for downstream tasks. To increase efficiency and prevent catastrophic forgetting alleviated from full domain-adaptive pre-training, approaches such as adapters have been develope…

Cited by 7Source ↗PDF ↗code ↗Scholar ↗
2022

DS-TOD: Efficient Domain Specialization for Task-Oriented Dialog

Chia-Chien Hung, Anne Lauscher, Simone Ponzetto, Goran Glavaš
ACL 2022findings

Recent work has shown that self-supervised dialog-specific pretraining on large conversational datasets yields substantial gains over traditional language modeling (LM) pretraining in downstream task-oriented dialog (TOD). These approaches, however, exploit general dialogic corpora (e.g., Reddit) an…

Cited by 21Source ↗PDF ↗code ↗Scholar ↗
2022

Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining for Task-Oriented Dialog

Chia-Chien Hung, Anne Lauscher, Ivan Vulić, Simone Ponzetto, Goran Glavaš
NAACL 2022long

Research on (multi-domain) task-oriented dialog (TOD) has predominantly focused on the English language, primarily due to the shortage of robust TOD datasets in other languages, preventing the systematic investigation of cross-lingual transfer for this crucial NLP application area. In this work, we…

Cited by 35Source ↗PDF ↗code ↗Scholar ↗