Papers by Kevin Lin
Controllable Fake Document Infilling for Cyber Deception (2022.findings-emnlp)
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| Challenge: | Existing approaches to deter malicious intrusion generate multiple fake versions of a document that are context-agnostic and produce sub-optimal outputs. |
| Approach: | They propose a context-aware model for creating fake documents that masks important concepts of varied lengths and then infills a realistic but fake alternative considering both the previous and future contexts. |
| Outcome: | The proposed model outperforms baseline models in generating highly believable fakes with moderate modification to protect critical information and deceive adversaries. |
Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction (2023.acl-long)
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Ashish Sharma, Kevin Rushton, Inna Lin, David Wadden, Khendra Lucas, Adam Miner, Theresa Nguyen, Tim Althoff
| Challenge: | Psychotherapy can help people overcome negative thoughts by replacing them with a more hopeful "reframed thought" but clinician shortages and mental health stigma often limit access to therapy. |
| Approach: | They propose a framework of seven linguistic attributes that can be used to reframe a thought . they use a retrieval-enhanced in-context learning model to generate reframed thoughts . |
| Outcome: | The proposed model is based on a human-centered study of 600 situations, thoughts and reframes on 2,000 mental health websites. |
Decomposing Complex Queries for Tip-of-the-tongue Retrieval (2023.findings-emnlp)
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| Challenge: | Tip-of-the-tongue retrieval is a retrieval setting in which a user is unable to formulate a precise query that identifies a sought item . a framework that decomposes complex queries into subqueries can improve gold book recall . |
| Approach: | They propose a framework for handling tip-of-the-tongue queries by decomposing queries into individual clues routing them to specialized retrievers. |
| Outcome: | The proposed framework improves gold book recall up to 6% on a new query-book pair . it takes advantage of off-the-shelf retrievers or incorporates retriever-specific logic . |
Empowering Healthcare Practitioners with Language Models: Structuring Speech Transcripts in Two Real-World Clinical Applications (2025.emnlp-industry)
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Jean-Philippe Corbeil, Asma Ben Abacha, George Michalopoulos, Phillip Swazinna, Miguel Del-Agua, Jerome Tremblay, Akila Jeeson Daniel, Cari Bader, Kevin Cho, Pooja Krishnan, Nathan Bodenstab, Thomas Lin, Wenxuan Teng, Francois Beaulieu, Paul Vozila
| Challenge: | Large language models (LLMs) have demonstrated strong performance on clinical natural language processing tasks across multiple medical benchmarks. |
| Approach: | They propose an agentic pipeline for generating realistic, non-sensitive nurse dictations, enabling structured extraction of clinical observations. |
| Outcome: | The proposed pipeline generates realistic, non-sensitive nurse dictations, enabling structured extraction of clinical observations. |
ELISA-EDL: A Cross-lingual Entity Extraction, Linking and Localization System (N18-5)
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| Challenge: | ELISA-EDL is a cross-lingual entity extraction, linking and localization system for Wikipedia languages. |
| Approach: | They propose a cross-lingual entity extraction, linking and localization system for English speakers . it extracts entities from unstructured text in any of 282 Wikipedia languages and links them to English knowledge bases . |
| Outcome: | The proposed system extracts entity mentions from Wikipedia and links them to English knowledge bases and visualizes locations related to disaster topics on a world heatmap. |
An Empirical Study of Multimodal Model Merging (2023.findings-emnlp)
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| Challenge: | Existing studies have shown that model merging can generate a multi-task solution without synchronous training. |
| Approach: | They propose to merge vision, language, and cross-modal transformers of a modality-specific architecture to create a parameter-efficient architecture. |
| Outcome: | The proposed model merging outperforms naive models on various tasks with improvements of 3% on VQA, 7% on COCO retrieval, 25% on NLVR2, 14% on Flickr30k and 3% ADE20k. |
QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions (D19-1)
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| Challenge: | Unlike previous datasets, the general knowledge is textual and not tied to a fixed set of relationships. |
| Approach: | They introduce the first open-domain dataset, called QuaRTz, for reasoning about textual qualitative relationships. |
| Outcome: | The proposed dataset is the first open-domain dataset for reasoning about qualitative relationships. |
AfriCLIRMatrix: Enabling Cross-Lingual Information Retrieval for African Languages (2022.emnlp-main)
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| Challenge: | Existing datasets for cross-lingual information retrieval are limited in many languages, especially those spoken in Africa. |
| Approach: | They propose to build a test collection for cross-lingual information retrieval in 15 diverse African languages. |
| Outcome: | AfriCLIRMatrix contains 6 million queries in English and 23 million relevance judgments automatically mined from Wikipedia inter-language links, covering many more African languages than any existing information retrieval test collection. |
Reasoning Over Paragraph Effects in Situations (D19-58)
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| Challenge: | a key component of reading a passage is the ability to apply knowledge gained from the passage to a new situation. |
| Approach: | They propose a benchmark for reading comprehension targeting Reasoning Over Paragraph Effects in Situations. |
| Outcome: | The proposed model performs slightly better than randomly guessing an answer of the correct type, but is below the human performance of 89.0%. |
Audio-Aware Large Language Models as Judges for Speaking Styles (2025.findings-emnlp)
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Cheng-Han Chiang, Xiaofei Wang, Chung-Ching Lin, Kevin Lin, Linjie Li, Radu Kopetz, Yao Qian, Zhendong Wang, Zhengyuan Yang, Hung-yi Lee, Lijuan Wang
| Challenge: | Audio-aware large language models (ALLMs) can understand textual and non-textual information in the audio input. |
| Approach: | They use audio-aware large language models (ALLMs) to evaluate the speaking styles of SLMs on two tasks: voice style instruction following and role-playing. |
| Outcome: | The proposed models can understand the textual and non-textual information in the audio input and can be used as a judge to assess the speaking styles of SLMs. |
Evaluating Models’ Local Decision Boundaries via Contrast Sets (2020.findings-emnlp)
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Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, Ben Zhou
| Challenge: | Standard test sets for supervised learning evaluate in-distribution generalization but are misleading when a dataset has systematic gaps. |
| Approach: | They propose a more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data. |
| Outcome: | The proposed model performs significantly lower on contrast sets than on the original test sets—up to 25% in some cases. |
VIEWS: Entity-Aware News Video Captioning (2024.emnlp-main)
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Hammad Ayyubi, Tianqi Liu, Arsha Nagrani, Xudong Lin, Mingda Zhang, Anurag Arnab, Feng Han, Yukun Zhu, Xuande Feng, Kevin Zhang, Jialu Liu, Shih-Fu Chang
| Challenge: | Existing video captioning benchmarks and models produce generic captions for videos that lack specific identification of individuals, locations, or organizations. |
| Approach: | They propose a task of directly summarizing news videos into captions that are entity-aware . they validate the effectiveness of their approach across three video captioning models . |
| Outcome: | The proposed approach is effective across three video captioning models. |
Constructing Taxonomies from Pretrained Language Models (2021.naacl-main)
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| Challenge: | A variety of NLP tasks use taxonomic information, including question answering and information retrieval. |
| Approach: | They propose a method for constructing taxonomic trees using pretrained language models by incorporating web-retrieved glosses into the model. |
| Outcome: | The proposed model achieves 66.7 ancestor F1, a 20.0% relative increase over the previous best published model on English WordNet. |
Shanks: Simultaneous Hearing and Thinking for Spoken Language Models (2026.acl-long)
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Cheng-Han Chiang, Xiaofei Wang, Linjie Li, Chung-Ching Lin, Kevin Lin, Shujie Liu, Zhendong Wang, Zhengyuan Yang, Hung-yi Lee, Lijuan Wang
| Challenge: | Existing large language models and spoken language models (SLMs) begin thinking and taking actions only after the user has finished their turn. |
| Approach: | They propose a general inference framework that enables SLMs to generate unspoken chain-of-thought reasoning while listening to user input. |
| Outcome: | The proposed framework enhances real-time user–SLM interaction in two scenarios. |