Papers by Kyle Lo
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. |
Discourse Understanding and Factual Consistency in Abstractive Summarization (2021.eacl-main)
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Saadia Gabriel, Antoine Bosselut, Jeff Da, Ari Holtzman, Jan Buys, Kyle Lo, Asli Celikyilmaz, Yejin Choi
| Challenge: | Existing abstractive summarization models often hallucinate information or generate factually incorrect summaries. |
| Approach: | They propose a general framework for abstractive summarization with factual consistency and distinct modeling of the narrative flow in an output summary. |
| Outcome: | The proposed framework generates abstracts with factual consistency and coherence significantly better than baselines. |
S2ORC: The Semantic Scholar Open Research Corpus (2020.acl-main)
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| Challenge: | Academic papers are an increasingly important textual domain for natural language processing (NLP) research. |
| Approach: | They propose to aggregate 81.1M English-language academic papers into a unified source . they hope this resource will facilitate research and development of tools for text mining over academic text. |
| Outcome: | The proposed corpus includes metadata, abstracts, bibliographic references, and structured full text for 8.1M open access papers. |
SciBERT: A Pretrained Language Model for Scientific Text (D19-1)
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| Challenge: | SciBERT is a pretrained language model based on BERT to improve performance on scientific NLP tasks. |
| Approach: | They propose a pretrained language model based on BERT to improve NLP performance . they evaluate on sequence tagging, sentence classification and dependency parsing . |
| Outcome: | The proposed model improves on sequence tagging, sentence classification and dependency parsing tasks with datasets from a variety of scientific domains. |
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. |
InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification (2024.acl-long)
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Jan Trienes, Sebastian Joseph, Jörg Schlötterer, Christin Seifert, Kyle Lo, Wei Xu, Byron Wallace, Junyi Jessy Li
| Challenge: | Text simplification aims to make technical texts more accessible to laypeople but often results in deletion of information and vagueness. |
| Approach: | They propose a framework to characterize and recover simplification-induced information loss in form of question-and-answer (QA) pairs. |
| Outcome: | The proposed framework characterizes and recovers simplification-induced information loss in form of question-and-answer (QA) pairs. |
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. |
PaperMage: A Unified Toolkit for Processing, Representing, and Manipulating Visually-Rich Scientific Documents (2023.emnlp-demo)
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Kyle Lo, Zejiang Shen, Benjamin Newman, Joseph Chang, Russell Authur, Erin Bransom, Stefan Candra, Yoganand Chandrasekhar, Regan Huff, Bailey Kuehl, Amanpreet Singh, Chris Wilhelm, Angele Zamarron, Marti A. Hearst, Daniel Weld, Doug Downey, Luca Soldaini
| Challenge: | Existing tools for working with scientific documents are limited and documents are often in difficult-to-use PDF formats. |
| Approach: | They propose an open-source Python toolkit for analyzing and processing visually-rich scientific documents. |
| Outcome: | PaperMage provides turn-key recipes for common scientific document processing use-cases. |
LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization (2023.eacl-main)
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| Challenge: | Human evaluation is labor-intensive, expensive to scale, and difficult to design. |
| Approach: | They propose a set of guidelines for human evaluation of faithfulness in long-form summaries that address the following challenges: (1) How can we achieve high inter-annotator agreement on faithfulness scores? (2) How can our annotator minimize workload while maintaining accurate faithfulness? |
| Outcome: | The proposed framework reduces inter-annotator variance in faithfulness scores while minimizing annotator workload while maintaining accuracy. |
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 . |
Construction of the Literature Graph in Semantic Scholar (N18-3)
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Waleed Ammar, Dirk Groeneveld, Chandra Bhagavatula, Iz Beltagy, Miles Crawford, Doug Downey, Jason Dunkelberger, Ahmed Elgohary, Sergey Feldman, Vu Ha, Rodney Kinney, Sebastian Kohlmeier, Kyle Lo, Tyler Murray, Hsu-Han Ooi, Matthew Peters, Joanna Power, Sam Skjonsberg, Lucy Lu Wang, Chris Wilhelm, Zheng Yuan, Madeleine van Zuylen, Oren Etzioni
| Challenge: | Fig. 1 summarizes a scalable system for organizing published scientific literature into a heterogeneous graph . authors describe methods used to enable semantic features in www.semanticscholar.org . |
| Approach: | They describe a scalable system for organizing published scientific literature into a heterogeneous graph to facilitate algorithmic manipulation and discovery. |
| Outcome: | The proposed system can be deployed on a scalable platform and report empirical results for each task. |
VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups (2022.tacl-1)
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| Challenge: | Recent work has improved extraction accuracy by incorporating elementary layout information, for example, each token’s 2D position on the page, into language model pretraining. |
| Approach: | They propose a method that explicitly models VIsual LAyout (VILA) groups, that is, text lines or text blocks, to further improve extraction accuracy. |
| Outcome: | The proposed methods show that inserting special tokens denoting layout group boundaries can lead to a 1.9% Macro F1 improvement in token classification. |
MathFish: Evaluating Language Model Math Reasoning via Grounding in Educational Curricula (2024.findings-emnlp)
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| Challenge: | pedagogical experts spend months reviewing published math problems to ensure that they align with critical skills or concepts. |
| Approach: | They propose a novel approach for evaluating language models' mathematical abilities by combining a dataset of 385 fine-grained descriptions of K-12 math skills and concepts with 9.9K math problems labeled with these standards. |
| Outcome: | The proposed model can discern skills and concepts enabled by math content, and it can be used to assess language models' mathematical abilities. |
ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts (2022.emnlp-demos)
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Sonia Murthy, Kyle Lo, Daniel King, Chandra Bhagavatula, Bailey Kuehl, Sophie Johnson, Jonathan Borchardt, Daniel Weld, Tom Hope, Doug Downey
| Challenge: | Current systems that automatically define unfamiliar terms only surface a single "best" description for all users, which may not be accessible for all readers, given varying background knowledge. |
| Approach: | They propose an end-to-end system that generates sets of descriptions of scientific concepts . ACCoRD corpus includes 1,275 labeled contexts and 1,787 expert-authored concept descriptions . |
| Outcome: | The proposed system produces diverse descriptions of concepts in terms of reference concepts. |
Combining Distant and Direct Supervision for Neural Relation Extraction (N19-1)
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| Challenge: | Existing methods to train relation extraction with distant supervision use noisy labels and implicitly assumes that all the KB facts are mentioned in the text. |
| Approach: | They propose to combine distant supervision data with additional directly-supervised data to train relation extraction models by using sigmoidal attention weights with max pooling. |
| Outcome: | The proposed method achieves state-of-the-art on the widely used FB-NYT dataset. |
One Thousand and One Pairs: A “novel” challenge for long-context language models (2024.emnlp-main)
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| Challenge: | Existing long-context evaluation methods measure surface-level retrieval capabilities, but do not assess performance on the more challenging task of synthesizing distant and underlying information. |
| Approach: | They propose a dataset of 1,001 minimally different pairs of true and false claims about 67 recently-published English fictional books. |
| Outcome: | The proposed model performs better on pairs that require only sentence-level retrieval vs. global reasoning . the proposed model also performs worse on speculative fiction books with extensive world-building . |
DrawEduMath: Evaluating Vision Language Models with Expert-Annotated Students’ Hand-Drawn Math Images (2025.naacl-long)
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| Challenge: | DrawEduMath examines the ability of vision language models to handle real-world math problems, such as those encountered in classrooms and tutoring sessions. |
| Approach: | They present DrawEduMath, an English-language dataset of 2,030 images of students’ handwritten responses to math problems. |
| Outcome: | The proposed model can be used to evaluate teachers' QA pairs and 44,362 synthetic QAs derived from teachers' descriptions. |
Are Layout-Infused Language Models Robust to Layout Distribution Shifts? A Case Study with Scientific Documents (2023.findings-acl)
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| Challenge: | Recent work has shown that infusing layout features into language models improves processing of visually-rich documents such as scientific papers. |
| Approach: | They propose a method to evaluate layout-infused language models that incorporate layout features into their models to emulate layout distribution shifts. |
| Outcome: | The proposed model performs better under layout distribution shifts than in-distribution conditions. |
TLDR: Extreme Summarization of Scientific Documents (2020.findings-emnlp)
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| Challenge: | TLDR generation requires expert background knowledge and understanding of complex domain-specific language. |
| Approach: | They propose a learning strategy that exploits titles as an auxiliary training signal. |
| Outcome: | The proposed method improves upon strong baselines under both automated metrics and human evaluations. |
Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks (2020.acl-main)
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Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, Noah A. Smith
| Challenge: | Language models prerained on text from a wide variety of sources form the foundation of today’s NLP. |
| Approach: | They propose to tailor a pretrained model to the domain of a target task by using domain-adaptive pretraining in-domain. |
| Outcome: | The proposed model can be tailored to the domain of a target task and perform well under both high- and low-resource settings. |
FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions (2025.naacl-long)
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Orion Weller, Benjamin Chang, Sean MacAvaney, Kyle Lo, Arman Cohan, Benjamin Van Durme, Dawn Lawrie, Luca Soldaini
| Challenge: | Modern language models (LMs) are capable of following long and complex instructions that enable a large and diverse set of user requests. |
| Approach: | They propose a dataset that contains an instruction evaluation benchmark and a training set to help IR models learn to follow instructions. |
| Outcome: | The proposed model improves after fine-tuning on a training set and rigorous instruction evaluation benchmark. |
Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval (2023.findings-emnlp)
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| Challenge: | Multi-document summarization (MDS) assumes a set of topic-related documents is provided as input. |
| Approach: | They formalize the task and bootstrap it using existing datasets, retrievers and summarizers. |
| Outcome: | The proposed method reduces the sensitivity of summarizers to imperfect retrieval, but is highly sensitive to other errors. |
The olmOCR Project: Building Fully Open OCR using VLMs (2026.acl-demo)
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| Challenge: | olmOCR is a fully open OCR system developed through iterative public releases and community feedback. |
| Approach: | They propose an open OCR system that combines a 7B vision-language model trained in two stages: finetuning and reinforcement learning with visual unit tests. |
| Outcome: | The proposed system achieves state-of-the-art performance among open systems and proprietary APIs at a fraction of the cost. |
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. |
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. |
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. |
A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers (2021.naacl-main)
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| Challenge: | Existing information-seeking question answering datasets do not perform well on answering these questions . existing models that do well on other QA tasks do not do well answering these tasks . |
| Approach: | They present a dataset of 5049 questions over 1585 NLP papers . they use a question-seeking QA model that seeks information in the full text . |
| Outcome: | The proposed dataset underperforms existing models on other QA tasks by 27 F1 points . the focus is on document-grounded, information-seeking QA . |
A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents (2023.emnlp-main)
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| Challenge: | snippets are not meant to be read outside their original document. |
| Approach: | They propose a framework that decomposes the task into three stages: question generation, question answering, and rewriting. |
| Outcome: | The proposed framework decomposes the task into three stages: question generation, question answering, and rewriting. |
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. |
Explaining Relationships Between Scientific Documents (2021.acl-long)
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| Challenge: | Existing approaches to explain relationships between scientific documents using natural language text can be useful for research efficiency. |
| Approach: | They propose a task of explaining relationships between scientific documents using natural language text. |
| Outcome: | The proposed models can be automated and humanely evaluated. |
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. |
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. |
Human-AI Collaboration: How AIs Augment Human Teammates (2025.acl-tutorials)
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| Challenge: | Despite the potential of general-purpose models, they are far from perfect, excelling at certain tasks while struggling with others. |
| Approach: | This tutorial will review recent developments related to human-AI teaming and collaboration. |
| Outcome: | This tutorial will review recent developments related to human-AI teaming and collaboration. |