---
title: "Who cites The Third PASCAL Recognizing — 384 citing papers"
description: "384 papers of the Elamur Research library cite «The Third PASCAL Recognizing Textual Entailment Challenge», found in their bibliographies…"
canonical: "https://research.elamur.ai/free/en/papers/giampiccolo-2007-third-pascal-recognizing-textual-entailment/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/giampiccolo-2007-third-pascal-recognizing-textual-entailment/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [The Third PASCAL Recognizing Textual Entailment…](https://md.research.elamur.ai/free/en/papers/giampiccolo-2007-third-pascal-recognizing-textual-entailment/) / cited by

# Who cites «The Third PASCAL Recognizing Textual Entailment Challenge»

384 papers of our library cite it (of the 149,439 whose bibliographies we read) · 81 with a page here, the most cited first · [← back to «The Third PASCAL Recognizing Textual Entailment Challenge»](https://md.research.elamur.ai/free/en/papers/giampiccolo-2007-third-pascal-recognizing-textual-entailment/)

1. [**Language Models are Few-Shot Learners arXiv:2005.14165**Tom B. Brown, Benjamin Mann, Nick Ryder, et al.*2020 · 14,178 citations*](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
2. [**RoBERTa: A Robustly Optimized BERT Pretraining Approach arXiv:1907.11692**Yinhan Liu, Myle Ott, Naman Goyal, et al.*2019 · 9,837 citations*](https://md.research.elamur.ai/free/en/papers/liu-2019-roberta-robustly-optimized-bert-pretraining/)
3. **[GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/wang-2018-glue-multi-task-benchmark-analysis/) [EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP](https://md.research.elamur.ai/free/en/venues/emnlp-workshop-blackboxnlp-analyzing-and-interpreting-neural-networks-for-nlp/)**Alex Wang, Amanpreet Singh, Julian Michael, et al.*2018 · 7,276 citations*
4. **[A large annotated corpus for learning natural language inference](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Samuel R. Bowman, Gabor Angeli, Christopher Potts, et al.*2015 · 5,299 citations*
5. **[The Power of Scale for Parameter-Efficient Prompt Tuning](https://md.research.elamur.ai/free/en/papers/lester-2021-power-scale-parameter-efficient-prompt/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Brian Lester, Rami Al-Rfou, Noah Constant*2021 · 4,287 citations*
6. [**Self-Consistency Improves Chain of Thought Reasoning in Language Models arXiv:2203.11171**Xuezhi Wang, Jason Wei, Dale Schuurmans, et al.*2022 · 3,121 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-self-consistency-improves-chain-thought/)
7. [**ALBERT: A Lite BERT for Self-supervised Learning of Language Representations arXiv:1909.11942**Zhenzhong Lan, Mingda Chen, Sebastian Goodman, et al.*2019 · 2,806 citations*](https://md.research.elamur.ai/free/en/papers/lan-2019-albert-lite-bert-self-supervised/)
8. **[SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://md.research.elamur.ai/free/en/papers/joshi-2019-spanbert-improving-pre-training-representing/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Mandar Joshi, Danqi Chen, Yinhan Liu, et al.*2019 · 2,616 citations*
9. [**SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems arXiv:1905.00537**Alex Wang, Yada Pruksachatkun, Nikita Nangia, et al.*2019 · 2,227 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-superglue-stickier-benchmark-general-purpose/)
10. **[Making Pre-trained Language Models Better Few-shot Learners](https://md.research.elamur.ai/free/en/papers/gao-2021-making-pre-trained-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyu Gao, Adam Fisch, Danqi Chen*2021 · 1,987 citations*
11. [**Finetuned Language Models Are Zero-Shot Learners arXiv:2109.01652**Jason Wei, Maarten Bosma, Vincent Y. Zhao, et al.*2021 · 1,954 citations*](https://md.research.elamur.ai/free/en/papers/wei-2021-finetuned-language-models-zero-shot/)
12. [**DeBERTa: Decoding-enhanced BERT with Disentangled Attention arXiv:2006.03654**Pengcheng He, Xiaodong Liu, Jianfeng Gao, et al.*2020 · 1,828 citations*](https://md.research.elamur.ai/free/en/papers/he-2020-deberta-decoding-enhanced-bert-disentangled/)
13. [**Multitask Prompted Training Enables Zero-Shot Task Generalization arXiv:2110.08207**Victor Sanh, Albert Webson, Colin Raffel, et al.*2021 · 1,612 citations*](https://md.research.elamur.ai/free/en/papers/sanh-2021-multitask-prompted-training-enables-zero/)
14. **[Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://md.research.elamur.ai/free/en/papers/min-2022-rethinking-role-demonstrations-what-makes/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sewon Min, Xinxi Lyu, Ari Holtzman, et al.*2022 · 1,444 citations*
15. [**Unified Language Model Pre-training for Natural Language Understanding and Generation arXiv:1905.03197**Li Dong, Nan Yang, Wenhui Wang, et al.*2019 · 1,422 citations*](https://md.research.elamur.ai/free/en/papers/dong-2019-unified-language-model-pre-training/)
16. [**DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing arXiv:2111.09543**Pengcheng He, Jianfeng Gao, Weizhu Chen*2021 · 1,320 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-debertav3-improving-deberta-using-electra/)
17. [**The Stanford Typed Dependencies Representation**Marie-Catherine de Marneffe, Christopher D. Manning*2008 · 1,146 citations*](https://md.research.elamur.ai/free/en/papers/marneffe-2008-stanford-typed-dependencies-representation/)
18. **[AdapterHub: A Framework for Adapting Transformers](https://md.research.elamur.ai/free/en/papers/pfeiffer-2020-adapterhub-framework-adapting-transformers/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, et al.*2020 · 754 citations*
19. [**BloombergGPT: A Large Language Model for Finance arXiv:2303.17564**Shijie Wu, Ozan Irsoy, Steven Lu, et al.*2023 · 618 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-bloomberggpt-large-language-model-finance/)
20. [**Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time arXiv:2203.05482**Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, et al.*2022 · 507 citations*](https://md.research.elamur.ai/free/en/papers/wortsman-2022-model-soups-averaging-weights-multiple/)
21. **[LinkBERT: Pretraining Language Models with Document Links](https://md.research.elamur.ai/free/en/papers/yasunaga-2022-linkbert-pretraining-language-models-document/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Michihiro Yasunaga, Jure Leskovec, Percy Liang*2022 · 458 citations*
22. [**FreeLB: Enhanced Adversarial Training for Natural Language Understanding arXiv:1909.11764**Chen Zhu, Yu Cheng, Zhe Gan, et al.*2019 · 452 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2019-freelb-enhanced-adversarial-training-natural/)
23. [**True Few-Shot Learning with Language Models arXiv:2105.11447**Ethan Perez, Douwe Kiela, Kyunghyun Cho*2021 · 412 citations*](https://md.research.elamur.ai/free/en/papers/perez-2021-true-few-shot-learning-language/)
24. [**Editing Models with Task Arithmetic arXiv:2212.04089**Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, et al.*2022 · 402 citations*](https://md.research.elamur.ai/free/en/papers/ilharco-2022-editing-models-task-arithmetic/)
25. [**UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training arXiv:2002.12804**Hangbo Bao, Li Dong, Furu Wei, et al.*2020 · 395 citations*](https://md.research.elamur.ai/free/en/papers/bao-2020-unilmv2-pseudo-masked-language-models/)
26. **[SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization](https://md.research.elamur.ai/free/en/papers/jiang-2020-smart-robust-efficient-fine-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoming Jiang, Pengcheng He, Weizhu Chen, et al.*2020 · 304 citations*
27. **[Finding Contradictions in Text](https://md.research.elamur.ai/free/en/papers/marneffe-2008-finding-contradictions-text/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Marie-Catherine de Marneffe, Anna N. Rafferty, Christopher D. Manning*2008 · 300 citations*
28. **[Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little](https://md.research.elamur.ai/free/en/papers/sinha-2021-masked-language-modeling-distributional-hypothesis/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Koustuv Sinha, Robin Jia, Dieuwke Hupkes, et al.*2021 · 297 citations*
29. **[BAM! Born-Again Multi-Task Networks for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/clark-2019-bam-born-again-multi-task/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal, et al.*2019 · 285 citations*
30. **[A Survey of Paraphrasing and Textual Entailment Methods](https://md.research.elamur.ai/free/en/papers/androutsopoulos-2009-survey-paraphrasing-textual-entailment-methods/) [Journal of Artificial Intelligence Research](https://md.research.elamur.ai/free/en/venues/journal-of-artificial-intelligence-research/)**Ion Androutsopoulos, Prodromos Malakasiotis*2009 · 284 citations*
31. **[Paraphrase Identification as Probabilistic Quasi-Synchronous Recognition](https://md.research.elamur.ai/free/en/papers/das-2009-paraphrase-identification-as-probabilistic-quasi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dipanjan Das, Noah A. Smith*2009 · 278 citations*
32. **[ZeroGen: Efficient Zero-shot Learning via Dataset Generation](https://md.research.elamur.ai/free/en/papers/ye-2022-zerogen-efficient-zero-shot-learning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiacheng Ye, Jiahui Gao, Qintong Li, et al.*2022 · 245 citations*
33. **[Coreferential Reasoning Learning for Language Representation](https://md.research.elamur.ai/free/en/papers/ye-2020-coreferential-reasoning-learning-language-representation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Deming Ye, Yankai Lin, Jiaju Du, et al.*2020 · 228 citations*
34. **[Visualizing and Understanding the Effectiveness of BERT](https://md.research.elamur.ai/free/en/papers/hao-2019-visualizing-understanding-effectiveness-bert/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yaru Hao, Li Dong, Furu Wei, et al.*2019 · 222 citations*
35. [**Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch arXiv:2311.03099**Le Yu, Bowen Yu, Haiyang Yu, et al.*2023 · 217 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-language-models-super-mario-absorbing/)
36. [**Generating Training Data with Language Models: Towards Zero-Shot Language Understanding arXiv:2202.04538**Yu Meng, Jiaxin Huang, Yu Zhang, et al.*2022 · 210 citations*](https://md.research.elamur.ai/free/en/papers/meng-2022-generating-training-data-language-models/)
37. **[Modality and Negation: An Introduction to the Special Issue](https://md.research.elamur.ai/free/en/papers/morante-2012-modality-negation-introduction-special-issue/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Roser Morante, Caroline Sporleder*2012 · 205 citations*
38. **[A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://md.research.elamur.ai/free/en/papers/laskar-2023-systematic-study-comprehensive-evaluation-chatgpt/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, et al.*2023 · 200 citations*
39. [**COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining arXiv:2102.08473**Yu Meng, Chenyan Xiong, Payal Bajaj, et al.*2021 · 198 citations*](https://md.research.elamur.ai/free/en/papers/meng-2021-coco-lm-correcting-contrasting-text/)
40. [**Paraphrasing for Style**Wei Xu, Alan Ritter, Bill Dolan, et al.*2012 · 196 citations*](https://md.research.elamur.ai/free/en/papers/xu-2012-paraphrasing-style/)
41. **[Debiasing Pre-trained Contextualised Embeddings](https://md.research.elamur.ai/free/en/papers/kaneko-2021-debiasing-pre-trained-contextualised-embeddings/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Masahiro Kaneko, Danushka Bollegala*2021 · 175 citations*
42. **[Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?](https://md.research.elamur.ai/free/en/papers/ravichander-2021-probing-probing-paradigm-does-probing/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Abhilasha Ravichander, Yonatan Belinkov, Eduard Hovy*2021 · 144 citations*
43. **[AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning](https://md.research.elamur.ai/free/en/papers/wang-2022-adamix-mixture-adaptations-parameter-efficient/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yaqing Wang, Sahaj Agarwal, Subhabrata Mukherjee, et al.*2022 · 135 citations*
44. [**Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches arXiv:1904.01172**Shane Storks, Qiaozi Gao, Joyce Y. Chai*2019 · 135 citations*](https://md.research.elamur.ai/free/en/papers/storks-2019-recent-advances-natural-language-inference/)
45. [**The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations**Lasha Abzianidze, Johannes Bjerva, Kilian Evang, et al.*2017 · 134 citations*](https://md.research.elamur.ai/free/en/papers/abzianidze-2017-parallel-meaning-bank-multilingual-corpus/)
46. [**An Analysis of Natural Language Inference Benchmarks through the Lens of Negation**Md Mosharaf Hossain, Venelin Kovatchev, Pranoy Dutta, et al.*2020 · 130 citations*](https://md.research.elamur.ai/free/en/papers/hossain-2020-analysis-natural-language-inference-benchmarks/)
47. [**Time Travel in LLMs: Tracing Data Contamination in Large Language Models arXiv:2308.08493**Shahriar Golchin, Mihai Surdeanu*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/golchin-2023-time-travel-llms-tracing-data/)
48. [**EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference**Abhilasha Ravichander, Aakanksha Naik, Carolyn Rose, et al.*2019 · 123 citations*](https://md.research.elamur.ai/free/en/papers/ravichander-2019-equate-benchmark-evaluation-framework-quantitative/)
49. **[ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts](https://md.research.elamur.ai/free/en/papers/asai-2022-attempt-parameter-efficient-multi-task/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Akari Asai, Mohammadreza Salehi, Matthew Peters, et al.*2022 · 122 citations*
50. [**Pretraining Language Models with Human Preferences arXiv:2302.08582**Tomasz Korbak, Kejian Shi, Angelica Chen, et al.*2023 · 121 citations*](https://md.research.elamur.ai/free/en/papers/korbak-2023-pretraining-language-models-human-preferences/)
51. [**Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework**Aaron Steven White, Pushpendre Rastogi, Kevin Duh, et al.*2017 · 110 citations*](https://md.research.elamur.ai/free/en/papers/white-2017-inference-is-everything-recasting-semantic/)
52. **[“Ask Not What Textual Entailment Can Do for You...”](https://md.research.elamur.ai/free/en/papers/sammons-2010-ask-not-what-textual-entailment/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mark Sammons, V.G.Vinod Vydiswaran, Dan Roth*2010 · 108 citations*
53. [**MMTEB: Massive Multilingual Text Embedding Benchmark arXiv:2502.13595**Kenneth Enevoldsen, Isaac Chung, Imene Kerboua, et al.*2025 · 103 citations*](https://md.research.elamur.ai/free/en/papers/enevoldsen-2025-mmteb-massive-multilingual-text-embedding/)
54. **[Understanding by Understanding Not: Modeling Negation in Language Models](https://md.research.elamur.ai/free/en/papers/hosseini-2021-understanding-understanding-not-modeling-negation/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Arian Hosseini, Siva Reddy, Dzmitry Bahdanau, et al.*2021 · 98 citations*
55. [**Tree Edit Models for Recognizing Textual Entailments, Paraphrases, and Answers to Questions**Michael Heilman, Noah A. Smith*2010 · 87 citations*](https://md.research.elamur.ai/free/en/papers/heilman-2010-tree-edit-models-recognizing-textual/)
56. **[Generating Entailment Rules from FrameNet](https://md.research.elamur.ai/free/en/papers/aharon-2010-generating-entailment-rules-framenet/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Roni Ben Aharon, Idan Szpektor, Ido Dagan*2010 · 71 citations*
57. **[Contradictions and Justifications: Extensions to the Textual Entailment Task](https://md.research.elamur.ai/free/en/papers/voorhees-2008-contradictions-justifications-extensions-textual-entailment/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ellen M. Voorhees*2008 · 54 citations*
58. **[Syntactic/Semantic Structures for Textual Entailment Recognition](https://md.research.elamur.ai/free/en/papers/mehdad-2010-syntactic-semantic-structures-textual-entailment/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yashar Mehdad, Alessandro Moschitti, Fabio Massimo Zanzotto*2010 · 43 citations*
59. **[Inference Rules and their Application to Recognizing Textual Entailment](https://md.research.elamur.ai/free/en/papers/dinu-2009-inference-rules-their-application-recognizing/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Georgiana Dinu, Rui Wang*2009 · 34 citations*
60. **[Expanding textual entailment corpora fromWikipedia using co-training](https://md.research.elamur.ai/free/en/papers/zanzotto-2010-expanding-textual-entailment-corpora-fromwikipedia/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Fabio Massimo Zanzotto, Marco Pennacchiotti*2010 · 33 citations*
61. **[New Protocols and Negative Results for Textual Entailment Data Collection](https://md.research.elamur.ai/free/en/papers/bowman-2020-new-protocols-negative-results-textual/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Samuel R. Bowman, Jennimaria Palomaki, Livio Baldini Soares, et al.*2020 · 27 citations*
62. **[Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal Negation](https://md.research.elamur.ai/free/en/papers/truong-2022-not-another-negation-benchmark-nan/) [Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL)](https://md.research.elamur.ai/free/en/venues/aacl/)**Hung Thinh Truong, Yulia Otmakhova, Timothy Baldwin, et al.*2022 · 24 citations*
63. [**Better to Ask in English: Evaluation of Large Language Models on English, Low-resource and Cross-Lingual Settings arXiv:2410.13153**Krishno Dey, Prerona Tarannum, Md. Arid Hasan, et al.*2024 · 16 citations*](https://md.research.elamur.ai/free/en/papers/dey-2024-better-ask-english-evaluation-large/)
64. **[Curing the SICK and Other NLI Maladies](https://md.research.elamur.ai/free/en/papers/kalouli-2023-curing-sick-other-nli-maladies/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Aikaterini-Lida Kalouli, Hai Hu, Alexander F. Webb, et al.*2023 · 14 citations*
65. **[EnCBP: A New Benchmark Dataset for Finer-Grained Cultural Background Prediction in English](https://md.research.elamur.ai/free/en/papers/ma-2022-encbp-new-benchmark-dataset-finer/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Weicheng Ma, Samiha Datta, Lili Wang, et al.*2022 · 13 citations*
66. **[Partial-input baselines show that NLI models can ignore context, but they don't.](https://md.research.elamur.ai/free/en/papers/srikanth-2022-partial-input-baselines-show-that/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Neha Srikanth, Rachel Rudinger*2022 · 13 citations*
67. **[MEraser: An Effective Fingerprint Erasure Approach for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2025-meraser-effective-fingerprint-erasure-approach/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingxuan Zhang, Zhenhua Xu, Rui Hu, et al.*2025 · 11 citations*
68. **[How Often Are Errors in Natural Language Reasoning Due to Paraphrastic Variability?](https://md.research.elamur.ai/free/en/papers/srikanth-2024-how-often-are-errors-natural/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Neha Srikanth, Marine Carpuat, Rachel Rudinger*2024 · 10 citations*
69. **[Semantic Annotation of Textual Entailment](https://md.research.elamur.ai/free/en/papers/toledo-2013-semantic-annotation-textual-entailment/) [International Conference on Computational Semantics – Long Papers](https://md.research.elamur.ai/free/en/venues/international-conference-on-computational-semantics-long-papers/)**Assaf Toledo, Stavroula Alexandropoulou, Sophia Katrenko, et al.*2013 · 7 citations*
70. **[CTCC: A Robust and Stealthy Fingerprinting Framework for Large Language Models via Cross-Turn Contextual Correlation Backdoor](https://md.research.elamur.ai/free/en/papers/xu-2025-ctcc-robust-stealthy-fingerprinting-framework/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhenhua Xu, Xixiang Zhao, Xubin Yue, et al.*2025 · 4 citations*
71. **[Conditional Natural Language Inference](https://md.research.elamur.ai/free/en/papers/kim-2023-conditional-natural-language-inference/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Youngwoo Kim, Razieh Rahimi, James Allan*2023 · 4 citations*
72. **[NLI under the Microscope: What Atomic Hypothesis Decomposition Reveals](https://md.research.elamur.ai/free/en/papers/srikanth-2025-nli-under-microscope-what-atomic/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Neha Srikanth, Rachel Rudinger*2025 · 3 citations*
73. **[Corpora for Learning the Mutual Relationship between Semantic Relatedness and Textual Entailment](https://md.research.elamur.ai/free/en/papers/vo-2016-corpora-learning-mutual-relationship-between/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Ngoc Phuoc An Vo, Octavian Popescu*2016 · 3 citations*
74. **[Why Do More Experts Fail? A Theoretical Analysis of Model Merging](https://md.research.elamur.ai/free/en/papers/wang-2026-why-do-more-experts-fail/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zijing Wang, Xingle Xu, YongKang Liu, et al.*2026 · 2 citations*
75. **[Towards a Semantic Model for Textual Entailment Annotation](https://md.research.elamur.ai/free/en/papers/toledo-2014-semantic-model-textual-entailment-annotation/) [Linguistic Issues in Language Technology](https://md.research.elamur.ai/free/en/venues/lilt/)**Assaf Toledo, Stavroula Alexandropoulou, Sophie Chesney, et al.*2014 · 2 citations*
76. **[Inference Rules for Recognizing Textual Entailment](https://md.research.elamur.ai/free/en/papers/dinu-2009-inference-rules-recognizing-textual-entailment/) [International Conference on Computational Semantics](https://md.research.elamur.ai/free/en/venues/iwcs/)**Georgiana Dinu, Rui Wang*2009 · 2 citations*
77. **[Charting the European LLM Benchmarking Landscape: A New Taxonomy and Registry](https://md.research.elamur.ai/free/en/papers/vintar-2026-charting-european-llm-benchmarking-landscape/) [Shaping Multilingual, Multimodal AI for the Social Sciences and Humanities @ LREC](https://md.research.elamur.ai/free/en/venues/llms4ssh/)**Spela Vintar, Mojca Brglez, Taja Kuzman Pungeršek, et al.*2026*
78. **[E-CONAN (Entailment, CONtradition And Neutral) Benchmarks: Arabic Textual Entailment and Natural Inference Datasets](https://md.research.elamur.ai/free/en/papers/jallad-2026-e-conan-entailment-contradition-neutral-benchmarks/) [IEEE Access](https://md.research.elamur.ai/free/en/venues/ieee-access/)**Khloud AL Jallad, Nada Ghneim, Ghaida Rebdawi*2026*
79. [**E-CONAN (Entailment, CONtradition And Neutral) Diagnostics Dataset Investigating Linguistic Phenomena in Arabic Natural Language Understanding arXiv:2609.33530**Khloud AL Jallad, Nada Ghneim, Ghaida Rebdawi*2026*](https://md.research.elamur.ai/free/en/papers/jallad-2026-e-conan-entailment-contradition-neutral/)
80. **[Understanding In-Context Learning Beyond Transformers: An Investigation of State Space and Hybrid Architectures](https://md.research.elamur.ai/free/en/papers/wang-2026-understanding-context-learning-beyond-transformers/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shenran Wang, Timothy Tin-Long Tse, Jian Zhu*2026*
81. **[A MISMATCHED Benchmark for Scientific Natural Language Inference](https://md.research.elamur.ai/free/en/papers/shaik-2025-mismatched-benchmark-scientific-natural-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Firoz Shaik, Mobashir Sadat, Nikita Gautam, et al.*2025*

303 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/giampiccolo-2007-third-pascal-recognizing-textual-entailment/) — The Third PASCAL Recognizing Textual Entailment Challenge