Papers by Chieh-Yen Lin
None of the Above, Less of the Right Parallel Patterns in Human and LLM Performance on Multi-Choice Questions Answering (2025.findings-acl)
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| Challenge: | Multiple-choice exam questions with “None of the above” (NA) options have been extensively studied in educational testing . however, their impact on Large Language Models (LLMs) evaluation remains underexplored . |
| Approach: | They conduct systematic experiments with 28 LLMs on the MMLU benchmark to examine how NA options affect model performance and confidence calibration. |
| Outcome: | The results highlight important implications for benchmark design and raise questions about LLMs’ ability to handle uncertainty in real-world applications. |
Let Me Speak Freely? A Study On The Impact Of Format Restrictions On Large Language Model Performance. (2024.emnlp-industry)
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| Challenge: | Structured generation is used to extract key output information from large language models (LLMs). |
| Approach: | They examine whether constraints on generation space impact LLMs’ abilities, including reasoning and domain knowledge comprehension. |
| Outcome: | The proposed model is based on a few-shot in-context learning and instruction-following capabilities. |
Adaptive Helpfulness–Harmlessness Alignment with Preference Vectors (2026.eacl-long)
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Ren-Wei Liang, Chin Ting Hsu, Chan-Hung Yu, Saransh Agrawal, Shih-Cheng Huang, Chieh-Yen Lin, Shang-Tse Chen, Kuan-Hao Huang, Shao-Hua Sun
| Challenge: | Existing approaches to balancing helpfulness and harmlessness suffer from performance conflicts, limited controllability, and poor extendability. |
| Approach: | They propose a framework that allows users to control their own preferences and dynamically merge them at test time. |
| Outcome: | The proposed framework improves helpfulness without conservatism and smooth control over preference trade-offs. |
I Need Help! Evaluating LLM’s Ability to Ask for Users’ Support: A Case Study on Text-to-SQL Generation (2024.emnlp-main)
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| Challenge: | a new study examines the proactive ability of large language models to seek user support . without external feedback, many LLMs struggle to recognize their need for user support. |
| Approach: | They propose metrics to evaluate the trade-off between performance improvements and user burden . they also investigate whether LLMs can determine when to request user support . |
| Outcome: | The proposed metrics show that without external feedback, many LLMs struggle to recognize their need for user support. |