Papers by Chia-Chien Hung
Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of Transformers (2023.findings-eacl)
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| Challenge: | Existing studies show that incorporating demographic factors in language representations improves performance on downstream NLP tasks. |
| Approach: | They use continuous language modeling and dynamic multi-task learning to adapt pre-trained Transformers to incorporate demographic information into their representations. |
| Outcome: | The proposed model shows that the results are consistent with previous studies. |
Linking Surface Facts to Large-Scale Knowledge Graphs (2023.emnlp-main)
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| Challenge: | Open Information Extraction (OIE) methods extract facts in the form of triples . ambiguity of these triples hinders their downstream usage . |
| Approach: | They propose a benchmark that measures fact linking performance on a granular triple slot level . they propose to use a system that can detect out-of-KG entities and predicates . |
| Outcome: | The proposed benchmark can measure fact linking performance on a granular triple slot level while also measuring if a system can recognize that a surface form has no match in the existing KG. |
Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining for Task-Oriented Dialog (2022.naacl-main)
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| Challenge: | Task-oriented dialog (TOD) is arguably one of the most popular natural language processing (NLP) application areas. |
| Approach: | They propose a multilingual multi-domain TOD dataset that spans four languages . they use a framework for multilingual conversational specialization of pretrained language models . |
| Outcome: | The proposed datasets show that they perform better than existing datasets in English . the proposed framework allows for sample-efficient few-shot transfer for TOD tasks . |
On Synthesizing Data for Context Attribution in Question Answering (2025.acl-long)
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Gorjan Radevski, Kiril Gashteovski, Shahbaz Syed, Christopher Malon, Sebastien Nicolas, Chia-Chien Hung, Timo Sztyler, Verena Heußer, Wiem Ben Rim, Masafumi Enomoto, Kunihiro Takeoka, Masafumi Oyamada, Goran Glavaš, Carolin Lawrence
| Challenge: | Large Language Models (LLMs) have a tendency to hallucinate, resulting in false or misleading answers. |
| Approach: | They propose a novel generative strategy for synthesizing context attribution data. |
| Outcome: | The proposed approach is highly effective for fine-tuning small LMs for context attribution in different QA tasks and domains. |
MEDDxAgent: A Unified Modular Agent Framework for Explainable Automatic Differential Diagnosis (2025.acl-long)
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Daniel Philip Rose, Chia-Chien Hung, Marco Lepri, Israa Alqassem, Kiril Gashteovski, Carolin Lawrence
| Challenge: | Existing approaches to DDx are limited by single-dataset evaluations, isolated optimization of components, unrealistic assumptions about complete patient profiles, and single-attempt diagnosis. |
| Approach: | They propose a Modular Explainable DDx Agent framework that allows physicians to iteratively refine a ranked list of possible diseases based on symptoms, antecedents, and medical knowledge. |
| Outcome: | The proposed framework achieves over 10% accuracy improvements in interactive DDx across large and small LLMs while offering critical explainability into its diagnostic reasoning process. |
ANHALTEN: Cross-Lingual Transfer for German Token-Level Reference-Free Hallucination Detection (2024.acl-srw)
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| Challenge: | ANHALTEN is a new evaluation dataset that extends the English hallucination detection dataset to German. |
| Approach: | They propose a dataset that extends the English hallucination detection dataset to German . they show that larger context length leads to better halluciation detection in german . |
| Outcome: | ANHALTEN is the first evaluation dataset that extends the English hallucination detection dataset to German. |
TADA: Efficient Task-Agnostic Domain Adaptation for Transformers (2023.findings-acl)
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| Challenge: | Pre-trained transformer-based language models are limited in their expressiveness and domain knowledge. |
| Approach: | They propose a task-agnostic domain adaptation method which is modular, parameter-efficient, and data-efficient. |
| Outcome: | The proposed method is efficient and modular, parameter-efficient, and data-efficient. |
DS-TOD: Efficient Domain Specialization for Task-Oriented Dialog (2022.findings-acl)
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| Challenge: | Recent work shows that self-supervised dialog-specific pretraining on large conversational datasets yields substantial gains over traditional language modeling (LM) pretraining. |
| Approach: | They propose a resource-efficient and modular domain specialization by means of domain adapters in which domain knowledge is encoded. |
| Outcome: | The proposed framework extracts domain-specific terms and then uses them to build DomainCC and DomainReddit resources based on masked language modeling and response selection objectives. |