@inproceedings{pramanick-etal-2026-claimflow,
title = "{C}laim{F}low: Tracing the Evolution of Scientific Claims in {NLP}",
author = "Pramanick, Aniket and
Hou, Yufang and
Mohammad, Saif M. and
Gurevych, Iryna",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.67/",
pages = "1531--1557",
ISBN = "979-8-89176-459-0",
abstract = "Scientific papers advance claims that later work supports, extends, or sometimes refutes. Yet existing methods for citation and claim analysis capture only fragments of this dialogue. In this work, we make these interactions explicit at the level of scientific claims. We introduce ClaimFlow, a claim-centric view of the NLP literature, built from 1,617 ACL Anthology papers (1979-2025) that are manually annotated with 5,689 claims and 4,871 cross-paper claim relations, indicating whether a citing paper supports, extends, qualifies, refutes, or references a cited claim as background. Building on ClaimFlow, we define a new task {--} Claim Relation Classification {--} which requires models to infer the scientific stance toward a cited claim from the text and citation context. Evaluating neural models and large language models on this task, we report baseline performance of 0.81 macro-F1, suggesting that the task is tractable while leaving room for improvement. We then scale this framework to {\textasciitilde}13k NLP papers to study claim evolution across decades of NLP research. We show that 63.5{\%} of claims are never reused; only 11.1{\%} are ever challenged. Widely propagated claims are more often reshaped through qualification and extension than supported or refuted. Overall, ClaimFlow offers a lens for examining how ideas shift and mature within NLP."
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<abstract>Scientific papers advance claims that later work supports, extends, or sometimes refutes. Yet existing methods for citation and claim analysis capture only fragments of this dialogue. In this work, we make these interactions explicit at the level of scientific claims. We introduce ClaimFlow, a claim-centric view of the NLP literature, built from 1,617 ACL Anthology papers (1979-2025) that are manually annotated with 5,689 claims and 4,871 cross-paper claim relations, indicating whether a citing paper supports, extends, qualifies, refutes, or references a cited claim as background. Building on ClaimFlow, we define a new task – Claim Relation Classification – which requires models to infer the scientific stance toward a cited claim from the text and citation context. Evaluating neural models and large language models on this task, we report baseline performance of 0.81 macro-F1, suggesting that the task is tractable while leaving room for improvement. We then scale this framework to ~13k NLP papers to study claim evolution across decades of NLP research. We show that 63.5% of claims are never reused; only 11.1% are ever challenged. Widely propagated claims are more often reshaped through qualification and extension than supported or refuted. Overall, ClaimFlow offers a lens for examining how ideas shift and mature within NLP.</abstract>
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%0 Conference Proceedings
%T ClaimFlow: Tracing the Evolution of Scientific Claims in NLP
%A Pramanick, Aniket
%A Hou, Yufang
%A Mohammad, Saif M.
%A Gurevych, Iryna
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F pramanick-etal-2026-claimflow
%X Scientific papers advance claims that later work supports, extends, or sometimes refutes. Yet existing methods for citation and claim analysis capture only fragments of this dialogue. In this work, we make these interactions explicit at the level of scientific claims. We introduce ClaimFlow, a claim-centric view of the NLP literature, built from 1,617 ACL Anthology papers (1979-2025) that are manually annotated with 5,689 claims and 4,871 cross-paper claim relations, indicating whether a citing paper supports, extends, qualifies, refutes, or references a cited claim as background. Building on ClaimFlow, we define a new task – Claim Relation Classification – which requires models to infer the scientific stance toward a cited claim from the text and citation context. Evaluating neural models and large language models on this task, we report baseline performance of 0.81 macro-F1, suggesting that the task is tractable while leaving room for improvement. We then scale this framework to ~13k NLP papers to study claim evolution across decades of NLP research. We show that 63.5% of claims are never reused; only 11.1% are ever challenged. Widely propagated claims are more often reshaped through qualification and extension than supported or refuted. Overall, ClaimFlow offers a lens for examining how ideas shift and mature within NLP.
%U https://aclanthology.org/2026.emnlp-main.67/
%P 1531-1557
Markdown (Informal)
[ClaimFlow: Tracing the Evolution of Scientific Claims in NLP](https://aclanthology.org/2026.emnlp-main.67/) (Pramanick et al., EMNLP 2026)
ACL
- Aniket Pramanick, Yufang Hou, Saif M. Mohammad, and Iryna Gurevych. 2026. ClaimFlow: Tracing the Evolution of Scientific Claims in NLP. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 1531–1557, Budapest, Hungary. Association for Computational Linguistics.