Papers by Kevin Lu
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. |
OmniCode: A Benchmark for Evaluating Software Development Agents (2026.findings-acl)
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Atharv Sonwane, Eng-Shen Tu, Wei-Chung Lu, Claas Beger, Carter Larsen, Debjit Dhar, Simon Alford, Rachel Chen, Ronit Pattanayak, Tuan Anh Dang, Guohao Chen, Gloria Geng, Kevin Ellis, Saikat Dutta
| Challenge: | popular coding benchmarks focus on narrowly scoped tasks such as competition programming and patch generation. |
| Approach: | They propose a software engineering benchmark that aims to provide a broader set of tasks beyond code or patch generation. |
| Outcome: | The proposed framework performs well on bug fixing for Python, test generation, code review fixing, and style fixing with popular agent frameworks such as SWE-Agent. |
Character-Based Models for Adversarial Phone Extraction: Preventing Human Sex Trafficking (D19-55)
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Nathanael Chambers, Timothy Forman, Catherine Griswold, Kevin Lu, Yogaish Khastgir, Stephen Steckler
| Challenge: | Illicit activity on the Web often obscures information between client and seller, such as the seller’s phone number. |
| Approach: | They propose to use a dataset to model adversarial noise in a text extraction system and propose a visual character language model to interpret unseen unicode characters. |
| Outcome: | The proposed model improves number recognition by 89% over a CRF with a CNN and shows that unicode characters can be translated to unicoding. |
PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification Data for Learning Enhanced Generation (2022.coling-1)
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Sedrick Scott Keh, Kevin Lu, Varun Gangal, Steven Y. Feng, Harsh Jhamtani, Malihe Alikhani, Eduard Hovy
| Challenge: | Personifications are figures of speech that endow inanimate entities with properties and actions typically seen as requiring animacy. |
| Approach: | They propose to use personification data to train a parallel corpus of personifications . they propose to combine personification-related literalizations with automatic ones . |
| Outcome: | The proposed personification system can generate diverse and creative personifications . it can generate personification-related qualities such as interestingness and animacy . |
NovelHopQA: Diagnosing Multi-Hop Reasoning Failures in Long Narrative Contexts (2025.emnlp-main)
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| Challenge: | Current large language models struggle to answer questions that span tens of thousands of tokens. |
| Approach: | They evaluate 1–4 hop QA over 64k–128k-token excerpts from 83 novels . they find consistent accuracy drops with increased hops and context length . |
| Outcome: | The novelhopqa benchmark evaluates 1–4 hop QA over 64k–128k-token excerpts from 83 public-domain novels. |
Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation (2025.findings-acl)
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Qiyue Gao, Xinyu Pi, Kevin Liu, Junrong Chen, Ruolan Yang, Xinqi Huang, Xinyu Fang, Lu Sun, Gautham Kishore, Bo Ai, Stone Tao, Mengyang Liu, Jiaxi Yang, Chao-Jung Lai, Chuanyang Jin, Jiannan Xiang, Benhao Huang, Zeming Chen, David Danks, Hao Su, Tianmin Shu, Ziqiao Ma, Lianhui Qin, Zhiting Hu
| Challenge: | Recent studies have evaluated and shown limitations in specific capabilities such as visual understanding, but a systematic evaluation of VLMs’ fundamental WM abilities remains absent. |
| Approach: | They propose a framework that assesses perception and prediction to provide an atomic evaluation of VLMs as WMs. |
| Outcome: | The proposed framework assesses perception and prediction abilities on 15 latest VLMs and compares them to human-level models. |
Rosetta-PL: Propositional Logic as a Benchmark for Large Language Model Reasoning (2025.naacl-srw)
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Shaun Lee Baek, Shaun Esua-Mensah, Cyrus Tsui, Sejan Vigneswaralingam, Abdullah Alali, Michael Lu, Vasu Sharma, Kevin Zhu
| Challenge: | Large Language Models (LLMs) are primarily trained on high-resource natural languages, limiting their effectiveness in low-resourced settings and in tasks requiring deep logical reasoning. |
| Approach: | They propose to use a dataset of logical propositions from Lean into a custom logical language to evaluate LLMs' logical reasoning and generalization capabilities in a controlled environment. |
| Outcome: | The proposed model improves accuracy and accuracy beyond 20,000 training samples. |