Papers by Joseph Chee Chang
Intent-aware Schema Generation and Refinement for Literature Review Tables (2025.findings-emnlp)
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| Challenge: | ambiguity in reference-based evaluations and lack of editing/refinement methods have slow progress on schema generation. |
| Approach: | They propose a method for augmenting unannotated table corpora with synthesized intents . they propose prompted workflows and fine-tuned models to improve schema generation . |
| Outcome: | The proposed approach significantly improves baseline performance in reconstructing reference schemas. |
Ai2 Scholar QA: Organized Literature Synthesis with Attribution (2025.acl-demo)
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Amanpreet Singh, Joseph Chee Chang, Dany Haddad, Aakanksha Naik, Jena D. Hwang, Rodney Kinney, Daniel S Weld, Doug Downey, Sergey Feldman
| Challenge: | Ai2 Scholar QA is a free online scientific question answering application . it uses retrieval-augmented generation to answer complex scientific questions . many of these systems are expensive to use and closed-source . |
| Approach: | They propose a retrieval-augmented generation-based scientific question answering application . it uses a Python package and an interactive web app to make the entire pipeline public . they compare it with other similar question-answering applications . |
| Outcome: | The proposed system outperforms other systems on a recent scientific QA benchmark. |
Personalized Jargon Identification for Enhanced Interdisciplinary Communication (2024.naacl-long)
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| Challenge: | Identifying and translating scientific jargon for individual researchers could speed up research, but current methods of jaron identification rely on corpus-level familiarity indicators rather than modeling researcher-specific needs. |
| Approach: | They collect over 10K term familiarity annotations from 11 computer science researchers and investigate supervised and prompt-based methods to predict individual jargon familiarity. |
| Outcome: | The proposed method improves jargon familiarity prediction by using domain, subdomain, and individual knowledge. |
Language Models Don’t Know What You Want: Evaluating Personalization in Deep Research Needs Real Users (2026.acl-long)
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Nishant Balepur, Malachi Hamada, Varsha Kishore, Sergey Feldman, Amanpreet Singh, Pao Siangliulue, Joseph Chee Chang, Eunsol Choi, Jordan Lee Boyd-Graber, Aakanksha Naik
| Challenge: | Earlier research used real users to push personalization, but easy-to-use judges have been criticized for not adopting online studies. |
| Approach: | They propose a personalized action-following tool that infers a user's research interests and proposes personalized actions for a query. |
| Outcome: | The proposed tool beats baselines in citation metrics and personalized action-following with an online version of MySQA. |
ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models (2024.emnlp-main)
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Benjamin Newman, Yoonjoo Lee, Aakanksha Naik, Pao Siangliulue, Raymond Fok, Juho Kim, Daniel Weld, Joseph Chee Chang, Kyle Lo
| Challenge: | Using language models (LMs) can generate literature review tables by decomposing it into separate schema and value generation steps. |
| Approach: | They propose a framework that leverages language models to perform literature review table generation by decomposing it into separate schema and value generation steps. |
| Outcome: | The proposed framework decomposes the task into two sub-tasks: schema generation and value generation. |