Papers by Biaoyan Fang
Controlling Distributional Bias in Multi-Round LLM Generation via KL-Optimized Fine-Tuning (2026.acl-long)
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| Challenge: | Existing evaluation methods focus on single-round inference, but this view is problematic in real-world applications. |
| Approach: | They propose a framework that couples Steering Token Calibration with Semantic Alignment to ensure that LLMs are correctly aligned across gender, race, and sentiment. |
| Outcome: | The proposed framework outperforms baseline methods in achieving precise distributional control in attribute generation tasks. |
What does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural text (2022.findings-acl)
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| Challenge: | Current research on anaphora resolution is mostly based on declarative text, such as chemical patents or instruction manuals. |
| Approach: | They propose a framework for anaphora annotation for the chemical domain for modeling anamorphic phenomena in recipes and chemical patents. |
| Outcome: | The proposed framework improves resolution of anaphora in recipes, suggesting transferability of general procedural knowledge. |
Understanding Faithfulness and Reasoning of Large Language Models on Plain Biomedical Summaries (2024.findings-emnlp)
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| Challenge: | Generating plain biomedical summaries with Large Language Models (LLMs) can enhance access to biomedically knowledge. |
| Approach: | They propose a benchmark dataset with expert-annotated Faithfulness and Reasoning on plain biomedical summaries. |
| Outcome: | The proposed dataset shows that LLMs perform poorly in generating faithful biomedical summaries and that abstractiveness and faithfulness are negatively correlated. |
Born Differently Makes a Difference: Counterfactual Study of Bias in Biography Generation from a Data-to-Text Perspective (2024.acl-short)
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| Challenge: | Current research shows that biographies reflect bias from society such as gender and religions. |
| Approach: | They propose a method that manipulates the personal attributes of interest while keeping the co-occurring attributes unchanged. |
| Outcome: | The proposed method expands the analysis of gender-centered bias in text generation. |
ChEMU-Ref: A Corpus for Modeling Anaphora Resolution in the Chemical Domain (2021.eacl-main)
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| Challenge: | Using a novel annotation scheme, we identify anaphoric references in chemical patents and determine the chemical relation between linked entities. |
| Approach: | They propose a neural approach to anaphora resolution based on coreference and bridging links in chemical patents. |
| Outcome: | The proposed framework can be used to identify anaphoric references in chemical patents and determine the chemical relation between linked entities. |
A Critical Look at Meta-evaluating Summarisation Evaluation Metrics (2024.findings-emnlp)
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| Challenge: | Effective summarisation evaluation metrics enable researchers and practitioners to compare different summarization systems efficiently. |
| Approach: | They argue that evaluation metrics are primarily meta-evaluated on news summarisation datasets and that there has been a noticeable shift in research focus towards evaluating the faithfulness of generated summaries. |
| Outcome: | The evaluation metrics are primarily meta-evaluated on news summarisation datasets and there has been a noticeable shift in research focus towards evaluating the faithfulness of generated summaries. |
Can VLMs Actually See and Read? A Survey on Modality Collapse in Vision-Language Models (2025.findings-acl)
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| Challenge: | Vision-language models integrate textual and visual information, enabling them to process visual inputs and generate predictions. |
| Approach: | They review work on modality collapse analysis to provide insights into the reason for this unintended behavior and review probing studies for fine-grained vision-language understanding. |
| Outcome: | The proposed models can achieve competitive performance in vision-language tasks despite relying heavily on textual information and ignoring visual information. |
More than Votes? Voting and Language based Partisanship in the US Supreme Court (2023.findings-emnlp)
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| Challenge: | partisanship and ideology have been a key topic in legal studies of the US Supreme Court . most research quantifies partisan behavior based on voting behavior, and oral arguments have not been well studied for this purpose. |
| Approach: | They propose a framework for analyzing justices' oral arguments for partisan signals and how they align with voting patterns. |
| Outcome: | The proposed framework shows that the affiliated party of justices can be predicted reliably from their oral contributions. |
The More, The Better? A Critical Study of Multimodal Context in Radiology Report Summarization (2025.findings-emnlp)
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Mong Yuan Sim, Wei Emma Zhang, Xiang Dai, Biaoyan Fang, Sarbin Ranjitkar, Arjun Burlakoti, Jamie Taylor, Haojie Zhuang
| Challenge: | Current multimodal summarization models often fail to utilize radiology images in summarizing Findings section. |
| Approach: | They conduct a thorough analysis to determine whether current multimodal summarization models can utilize radiology images in summarizing Findings section. |
| Outcome: | The Impression section plays a crucial role in communication between radiologists and physicians. |