Papers by David Lo
MultiCite: Modeling realistic citations requires moving beyond the single-sentence single-label setting (2022.naacl-main)
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| Challenge: | Citation context analysis (CCA) is an important task in natural language processing that studies how and why scholars discuss each other’s work. |
| Approach: | They propose to use a dataset of 12.6K citation contexts from 1.2K computational linguistics papers to model three important CCA phenomena. |
| Outcome: | The proposed dataset contains 12.6K citation contexts from 1.2K computational linguistics papers and can model these phenomena. |
Generating Scientific Claims for Zero-Shot Scientific Fact Checking (2022.acl-long)
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| Challenge: | Existing methods for scientific fact checking require domain expertise and time consuming. |
| Approach: | They propose a new supervised method for generating claims from scientific sentences and a novel method for negating claims. |
| Outcome: | The proposed method improves on existing methods on biomedical claims and negations. |
TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification (2025.acl-long)
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Yindu Su, Huike Zou, Lin Sun, Ting Zhang, Haiyang Yang, Chen Li Yu, David Lo, Qingheng Zhang, Shuguang Han, Jufeng Chen
| Challenge: | Existing methods for product attribute value identification face critical challenges . seller-provided attribute values are often incomplete or inaccurate . |
| Approach: | They propose a retrieval-based method that uses taxonomy-aware contrastive learning . they use product profiles and candidate values to encode and retrieve attributes based on similarity . |
| Outcome: | The proposed method is based on a taxonomy-aware, hard negative sampling and adaptive inference with dynamic thresholds. |
MultiCodeAttack: Iterative Jailbreak Attacking on LLMs with Multi-Code Prompt Injection (2026.findings-acl)
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| Challenge: | Existing approaches to jailbreak rely on fixed template design and a single programming language . however, existing approaches do not consider language diversity or adaptive template evolution . |
| Approach: | They propose a structured jailbreak framework that explores and optimizes multi-language code templates. |
| Outcome: | The proposed framework outperforms existing jailbreak baselines and produces higher harmful outputs than baseline methods. |
SeCuRepair: Semantics-Aligned, Curriculum-Driven, and Reasoning-Enhanced Vulnerability Repair Framework (2026.acl-long)
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Chengran Yang, Ting Zhang, Jinfeng Jiang, Xin Zhou, Haoye Tian, Mingzhe Du, Jieke Shi, Junkai Chen, Yikun Li, Eng Lieh Ouh, Lwin Khin Shar, David Lo
| Challenge: | Existing methods for automating vulnerability repair suffer from syntactic overfitting . nvd published 49,230 Common Vulnerabilities and Exposures (CVE) records in 2025 alone . |
| Approach: | They propose a semantic-aware reward framework that optimizes for code semantic equivalence rather than lexical mimicry. |
| Outcome: | The proposed framework outperforms state-of-the-art frameworks on repository-level splits . it incorporates expert-aligned reasoning mechanism that grounds patch generation in structured diagnosis. |
Fact or Fiction: Verifying Scientific Claims (2020.emnlp-main)
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David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, Hannaneh Hajishirzi
| Challenge: | SciFact is a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts annotated with labels and rationales. |
| Approach: | They construct a dataset of 1.4K scientific claims paired with evidence-containing abstracts annotated with labels and rationales to test their system. |
| Outcome: | The proposed system can verify claims related to COVID-19 by identifying evidence from the CORD-19 corpus. |
When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets (2024.findings-eacl)
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| Challenge: | Using large language models (LMs) for query or document expansion can improve generalization in information retrieval. |
| Approach: | They conduct the first comprehensive analysis of large language models (LMs) for query or document expansion. |
| Outcome: | The proposed expansions improve retrieval performance for weaker models but harm stronger models. |
SciFact-Open: Towards open-domain scientific claim verification (2022.findings-emnlp)
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| Challenge: | Current scientific claim verification systems can achieve very strong performance on limited contexts, in some cases approaching human agreement. |
| Approach: | They propose to pool and annotate top predictions from four state-of-the-art scientific claim verification models to evaluate their performance against large corpora. |
| Outcome: | The proposed system performs well on a corpus of 500K scientific abstracts. |
MultiVerS: Improving scientific claim verification with weak supervision and full-document context (2022.findings-naacl)
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| Challenge: | a new approach to scientific claim verification uses a document-level fact-checking label to label scientific documents . a multitask approach combines a shared encoding of the claim and document context . |
| Approach: | They propose a system which predicts a fact-checking label and identifies rationales in a multitask fashion based on a shared encoding of the claim and full document context. |
| Outcome: | The proposed approach outperforms baselines on three scientific claim verification datasets . it can learn from instances annotated with a document-level fact-checking label, but lacking sentence-level rationales based on the datasets. |
KIWI: A Dataset of Knowledge-Intensive Writing Instructions for Answering Research Questions (2024.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly used as conversational agents. |
| Approach: | They construct a dataset of knowledge-intensive writing instructions to evaluate LLMs' ability to follow user instructions. |
| Outcome: | The proposed model fails to integrate new information into an existing answer and perform precise and unambiguous edits. |
SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios (2026.acl-long)
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Junkai Chen, Huihui Huang, Yunbo Lyu, Junwen An, Jieke Shi, Chengran Yang, Ting Zhang, Haoye Tian, Yikun Li, Zhenhao Li, Xin Zhou, Xing Hu, David Lo
| Challenge: | Existing benchmarks fail to capture scenarios in which vulnerabilities are introduced by humans . we evaluate 5 popular code agents supported by 5 LLMs on SecureVibeBench . |
| Approach: | They propose a benchmarking tool that compares 105 C/C++ secure coding tasks . they use real-world open-source vulnerabilities and a comprehensive evaluation tool . |
| Outcome: | The proposed benchmarks show that code agents struggle to produce correct and secure code . the best performing agent produces merely 23.8% correct and secured solutions . |
SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature (2025.emnlp-main)
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David Wadden, Kejian Shi, Jacob Morrison, Alan Li, Aakanksha Naik, Shruti Singh, Nitzan Barzilay, Kyle Lo, Tom Hope, Luca Soldaini, Shannon Zejiang Shen, Doug Downey, Hannaneh Hajishirzi, Arman Cohan
| Challenge: | ScIRIFF is the only entirely expert-written instruction-following dataset for scientific literature understanding . it features complex instructions with long input contexts, detailed task descriptions, and structured outputs. |
| Approach: | They present a dataset of 137K instruction-following instances for training and evaluation . they finetuned large language models using a mix of general domain and ScIRIFF instructions . |
| Outcome: | The proposed dataset shows that on nine out-of-distribution held-out tasks, the model performs better than baselines trained on general domain instructions. |