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# Who cites «Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation»

68 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/geiger-2020-neural-natural-language-inference-models/)

1. [Multi-component Causal Tracing in Large Language Models](https://aclanthology.org/2026.acl-long.154/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
2. [The Validity of Coreference-based Evaluations of Natural Language Understanding](https://arxiv.org/abs/2602.16200) *— 2026 · arXiv:2602.16200*
3. [Thunder-NUBench: A Benchmark for LLMs'' Sentence-Level Negation Understanding](https://aclanthology.org/2026.findings-eacl.250/) *— 2026 · Findings of the Association for Computational Linguistics: EACL 2026*
4. [When Models Decide and When They Bind: A Two-Stage Computation for Multiple-Choice Question Answering](https://aclanthology.org/2026.findings-acl.1144/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
5. [Characterizing the Role of Similarity in the Property Inferences of Language Models](https://aclanthology.org/2025.naacl-long.574/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
6. [Sneaking Syntax into Transformer Language Models with Tree Regularization](https://aclanthology.org/2025.naacl-long.407/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
7. [Steering off Course: Reliability Challenges in Steering Language Models](https://aclanthology.org/2025.acl-long.974/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
8. [A Primer on the Inner Workings of Transformer-based Language Models](https://arxiv.org/abs/2405.00208) *— 2024 · arXiv:2405.00208*
9. [AtP*: An efficient and scalable method for localizing LLM behaviour to components](https://arxiv.org/abs/2403.00745) *— 2024 · arXiv:2403.00745*
10. [Exploring Continual Learning of Compositional Generalization in NLI](https://aclanthology.org/2024.tacl-1.51/) *— 2024 · Transactions of the Association for Computational Linguistics*
11. [Information Flow Routes: Automatically Interpreting Language Models at Scale](https://aclanthology.org/2024.emnlp-main.965/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
12. [LM Transparency Tool: Interactive Tool for Analyzing Transformer Language Models](https://aclanthology.org/2024.acl-demos.6/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)*
13. [Language Models Linearly Represent Sentiment](https://aclanthology.org/2024.blackboxnlp-1.5/) *— 2024 · Proceedings of the 7th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP*
14. [Mission: Impossible Language Models](https://aclanthology.org/2024.acl-long.787/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
15. [RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations](https://aclanthology.org/2024.acl-long.470/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
16. [Recurrent Neural Networks Learn to Store and Generate Sequences using Non-Linear Representations](https://aclanthology.org/2024.blackboxnlp-1.17/) *— 2024 · Proceedings of the 7th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP*
17. [Representational Analysis of Binding in Language Models](https://aclanthology.org/2024.emnlp-main.967/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
18. [Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI Models](https://aclanthology.org/2024.eacl-long.27/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
19. [Beyond Positive Scaling: How Negation Impacts Scaling Trends of Language Models](https://aclanthology.org/2023.findings-acl.472/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
20. [COMPS: Conceptual Minimal Pair Sentences for testing Robust Property Knowledge and its Inheritance in Pre-trained Language Models](https://aclanthology.org/2023.eacl-main.213/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
21. [Causal Abstraction for Chain-of-Thought Reasoning in Arithmetic Word Problems](https://aclanthology.org/2023.blackboxnlp-1.12/) *— 2023 · Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP*
22. [Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability](https://arxiv.org/abs/2301.04709) *— 2023 · arXiv:2301.04709*
23. [Causal interventions expose implicit situation models for commonsense language understanding](https://aclanthology.org/2023.findings-acl.839/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
24. [Interpretability at Scale: Identifying Causal Mechanisms in Alpaca](https://arxiv.org/abs/2305.08809) *— 2023 · arXiv:2305.08809*
25. [Interventional Probing in High Dimensions: An NLI Case Study](https://aclanthology.org/2023.findings-eacl.188/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
26. [Language models are not naysayers: An analysis of language models on negation benchmarks](https://arxiv.org/abs/2306.08189) *— 2023 · arXiv:2306.08189*
27. [Linear Representations of Sentiment in Large Language Models](https://arxiv.org/abs/2310.15154) *— 2023 · arXiv:2310.15154*
28. [Localizing Model Behavior with Path Patching](https://arxiv.org/abs/2304.05969) *— 2023 · arXiv:2304.05969*
29. [SETI: Systematicity Evaluation of Textual Inference](https://aclanthology.org/2023.findings-acl.252/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
30. [ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning](https://aclanthology.org/2023.acl-short.154/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
31. [The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets](https://arxiv.org/abs/2310.06824) *— 2023 · arXiv:2310.06824*
32. [Towards Best Practices of Activation Patching in Language Models: Metrics and Methods](https://arxiv.org/abs/2309.16042) *— 2023 · arXiv:2309.16042*
33. [ANLIzing the Adversarial Natural Language Inference Dataset](https://aclanthology.org/2022.scil-1.3/) *— 2022 · Proceedings of the Society for Computation in Linguistics 2022*
34. [An Analysis of Negation in Natural Language Understanding Corpora](https://aclanthology.org/2022.acl-short.81/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
35. [CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation](https://aclanthology.org/2022.emnlp-main.598/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
36. [Can Transformers Reason in Fragments of Natural Language?](https://aclanthology.org/2022.emnlp-main.768/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
37. [Causal Distillation for Language Models](https://aclanthology.org/2022.naacl-main.318/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
38. [Decomposing Natural Logic Inferences for Neural NLI](https://aclanthology.org/2022.blackboxnlp-1.33/) *— 2022 · Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
39. [It Is Not Easy To Detect Paraphrases: Analysing Semantic Similarity With Antonyms and Negation Using the New SemAntoNeg Benchmark](https://aclanthology.org/2022.blackboxnlp-1.20/) *— 2022 · Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
40. [Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inference](https://aclanthology.org/2022.tacl-1.14/) *— 2022 · Transactions of the Association for Computational Linguistics*
41. [Simple but Challenging: Natural Language Inference Models Fail on Simple Sentences](https://aclanthology.org/2022.findings-emnlp.252/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
42. [A Multilingual Benchmark for Probing Negation-Awareness with Minimal Pairs](https://aclanthology.org/2021.conll-1.19/) *— 2021 · Proceedings of the 25th Conference on Computational Natural Language Learning*
43. [Causal Abstractions of Neural Networks](https://arxiv.org/abs/2106.02997) *— 2021 · arXiv:2106.02997*
44. [Constructing Taxonomies from Pretrained Language Models](https://aclanthology.org/2021.naacl-main.373/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
45. [Exploring Transitivity in Neural NLI Models through Veridicality](https://aclanthology.org/2021.eacl-main.78/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
46. [Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference](https://aclanthology.org/2021.findings-acl.331/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
47. [Issues with Entailment-based Zero-shot Text Classification](https://aclanthology.org/2021.acl-short.99/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)*
48. [Modeling Event Plausibility with Consistent Conceptual Abstraction](https://aclanthology.org/2021.naacl-main.138/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
49. [Supporting Context Monotonicity Abstractions in Neural NLI Models](https://aclanthology.org/2021.naloma-1.6/) *— 2021 · Proceedings of the 1st and 2nd Workshops on Natural Logic Meets Machine Learning (NALOMA)*
50. [SyGNS: A Systematic Generalization Testbed Based on Natural Language Semantics](https://aclanthology.org/2021.findings-acl.10/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
51. bylinina-2022-transformers-loop-polarity-neural-models
52. canby-2025-how-reliable-are-causal-probing
53. dai-2026-cell-based-representation-relational-binding
54. dutt-2024-investigating-generalizability-pretrained-language-models
55. feucht-2026-arithmetic-wild-llama-uses-base
56. fu-2024-compositional-structured-explanation-generation-dynamic
57. jabbar-2025-distinguishing-fair-unfair-compositional-generalization
58. jiang-2026-immediate-inference-missing-foundation-large
59. junias-2026-logical-commonsenseqa-benchmark-logical-commonsense
60. larraya-2026-assessing-logical-coherence-llms-fine
61. mircea-2023-balaur-language-model-pretraining-lexical
62. rozanova-2024-estimating-causal-effects-natural-logic
63. saphra-2024-mechanistic
64. sineva-2021-negation-instance-based-evaluation-end
65. tao-2024-inference-verbalization-functions-during-context
66. wu-2022-identifying-limits-cross-domain-knowledge
67. wu-2024-textttpyvene-library-understanding-improving-pytorch
68. zheng-2023-natlogattack-framework-attacking-natural-language
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