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20242026
most citedA Survey of Pre-trained Language Models for Processing Scientific Text

4 citations · 8 across the 19 of their papers we have counts for

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17 papers · 1 filter

cs.CL2026

How Robust Is Multimodal Claim Verification to LLM Rewriting?

Yun-Ang Wu, Xanh Ho, Andre Greiner-Petter +4

LLMs are known to introduce stylistic changes into generated text, yet how these stylistic shifts affect model decisions on scientific tasks remains underexplored. In this paper, w…

cs.CL2026

Encoded but Not Routed: Explaining the Table-Chart Gap in Scientific Claim Verification

Sunisth Kumar, Xanh Ho, Tim Schopf +3

Multimodal LLMs are increasingly used to assist scientific peer review, where a core requirement is verifying whether claims in a paper are supported by its evidence. Prior work ha…

cs.CL2026

EarlySciRev: A Dataset of Early-Stage Scientific Revisions Extracted from LaTeX Writing Traces

Léane Jourdan, Julien Aubert-Béduchaud, Yannis Chupin +2

Scientific writing is an iterative process that generates rich revision traces, yet publicly available resources typically expose only final or near-final versions of papers. This…

cs.CL2026★ 2 cited

SciClaimEval: Cross-modal Claim Verification in Scientific Papers

Xanh Ho, Yun-Ang Wu, Sunisth Kumar +4

We present SciClaimEval, a new scientific dataset for the claim verification task. Unlike existing resources, SciClaimEval features authentic claims, including refuted ones, direct…

cs.CL2026

FC-CONAN: An Exhaustively Paired Dataset for Robust Evaluation of Retrieval Systems

Juan Junqueras, Florian Boudin, May-Myo Zin +5

Hate speech (HS) is a critical issue in online discourse, and one promising strategy to counter it is through the use of counter-narratives (CNs). Datasets linking HS with CNs are…

cs.CL2025

Format Matters: The Robustness of Multimodal LLMs in Reviewing Evidence from Tables and Charts

Xanh Ho, Yun-Ang Wu, Sunisth Kumar +3

With the growing number of submitted scientific papers, there is an increasing demand for systems that can assist reviewers in evaluating research claims. Experimental results are…

cs.CL2025

Repurposing Annotation Guidelines to Instruct LLM Annotators: A Case Study

Kon Woo Kim, Rezarta Islamaj, Jin-Dong Kim +2

This study investigates how existing annotation guidelines can be repurposed to instruct large language model (LLM) annotators for text annotation tasks. Traditional guidelines are…

cs.CL2025

Automatically Suggesting Diverse Example Sentences for L2 Japanese Learners Using Pre-Trained Language Models

Enrico Benedetti, Akiko Aizawa, Florian Boudin

Providing example sentences that are diverse and aligned with learners' proficiency levels is essential for fostering effective language acquisition. This study examines the use of…

cs.CL2025★ 1 cited

Table-Text Alignment: Explaining Claim Verification Against Tables in Scientific Papers

Xanh Ho, Sunisth Kumar, Yun-Ang Wu +3

Scientific claim verification against tables typically requires predicting whether a claim is supported or refuted given a table. However, we argue that predicting the final label…

cs.CL2025

Identifying Reliable Evaluation Metrics for Scientific Text Revision

Léane Jourdan, Florian Boudin, Richard Dufour +1

Evaluating text revision in scientific writing remains a challenge, as traditional metrics such as ROUGE and BERTScore primarily focus on similarity rather than capturing meaningfu…

cs.CL2025

Reassessing Extractive QA Datasets at Scale: LLM-as-a-Judge and In-Depth Analyses

Xanh Ho, Jiahao Huang, Florian Boudin +1

Extractive QA tasks are commonly evaluated using Exact Match (EM) and F1-score, but these metrics often fail to reflect true model performance. Recent studies have proposed using l…

cs.CL2025

ParaRev: Building a dataset for Scientific Paragraph Revision annotated with revision instruction

Léane Jourdan, Nicolas Hernandez, Richard Dufour +2

Revision is a crucial step in scientific writing, where authors refine their work to improve clarity, structure, and academic quality. Existing approaches to automated writing assi…

cs.CL2024

Self-Compositional Data Augmentation for Scientific Keyphrase Generation

Mael Houbre, Florian Boudin, Beatrice Daille +1

State-of-the-art models for keyphrase generation require large amounts of training data to achieve good performance. However, obtaining keyphrase-labeled documents can be challengi…

cs.CL2024★ 1 cited

Unsupervised Domain Adaptation for Keyphrase Generation using Citation Contexts

Florian Boudin, Akiko Aizawa

Adapting keyphrase generation models to new domains typically involves few-shot fine-tuning with in-domain labeled data. However, annotating documents with keyphrases is often proh…

cs.CL2024

MoreHopQA: More Than Multi-hop Reasoning

Julian Schnitzler, Xanh Ho, Jiahao Huang +3

Most existing multi-hop datasets are extractive answer datasets, where the answers to the questions can be extracted directly from the provided context. This often leads models to…

cs.CL2024

CASIMIR: A Corpus of Scientific Articles enhanced with Multiple Author-Integrated Revisions

Leane Jourdan, Florian Boudin, Nicolas Hernandez +1

Writing a scientific article is a challenging task as it is a highly codified and specific genre, consequently proficiency in written communication is essential for effectively con…

cs.CL2024★ 4 cited

A Survey of Pre-trained Language Models for Processing Scientific Text

Xanh Ho, Anh Khoa Duong Nguyen, An Tuan Dao +6

The number of Language Models (LMs) dedicated to processing scientific text is on the rise. Keeping pace with the rapid growth of scientific LMs (SciLMs) has become a daunting task…