Papers by Hanhua Hong
How to Determine the Most Powerful Pre-trained Language Model without Brute Force Fine-tuning? An Empirical Survey (2023.findings-emnlp)
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| Challenge: | Transferability estimation has been a topic of great interest in computer vision fields . a lack of a comprehensive comparison between these estimation methods is a problem . |
| Approach: | They conduct a thorough survey of existing methods to find the most suitable model . they also outline difficulties of consideration of training details and applicability to text generation . |
| Outcome: | The proposed methods perform well with superiorities in effectiveness and efficiency. |
HiRAS: A Hierarchical Multi-Agent Framework for Paper-to-Code Generation and Execution (2026.findings-acl)
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| Challenge: | Existing approaches to automate computational research use fixed sequential agent pipelines with weak global coordination, which limits their robustness and overall performance. |
| Approach: | They propose a hierarchical multi-agent framework for end-to-end paper reproduction that employs supervisory manager agents to coordinate specialised agents across fine-grained stages. |
| Outcome: | The proposed framework improves the paper2code benchmark and significantly reduces hallucination in the evaluation. |
Leveraging Estimated Transferability Over Human Intuition for Model Selection in Text Ranking (2024.emnlp-main)
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| Challenge: | Existing methods for text ranking are based on intuition, but their estimated transferability may not align well with the objectives of text ranking. |
| Approach: | They propose to compute expected rank as transferability, explicitly reflecting the model’s ranking capability. |
| Outcome: | The proposed method shows significant improvements over previous classification-oriented TE methods, human intuition, and ChatGPT with minor time consumption. |
Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG Evaluation Prompts (2026.tacl-1)
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| Challenge: | Evaluating natural language generation systems is challenging due to the diversity of valid outputs. |
| Approach: | They propose an inversion learning method that learns effective reverse mappings from model outputs back to their input instructions. |
| Outcome: | The proposed method requires only a single evaluation sample and eliminates manual prompt engineering. |