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# Who cites «Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks»

363 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/weston-2015-towards-ai-complete-question-answering/)

Pages: **1** · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/weston-2015-towards-ai-complete-question-answering-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/weston-2015-towards-ai-complete-question-answering-3/) · [4](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/weston-2015-towards-ai-complete-question-answering-4/)

1. [Improving Chain-of-Thought for Logical Reasoning via Attention-Aware Intervention](https://aclanthology.org/2026.findings-eacl.152/) *— 2026 · Findings of the Association for Computational Linguistics: EACL 2026*
2. [Logic Haystacks: Probing LLMs'' Long-Context Logical Reasoning (Without Easily Identifiable Unrelated Padding)](https://aclanthology.org/2026.eacl-short.3/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)*
3. [Multi-Constraint State Tracking with Negation: A Diagnostic Benchmark for LLM World Modeling](https://aclanthology.org/2026.acl-srw.119/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)*
4. [NormWorlds-CF: Solver-Verified Counterfactual Normative Reasoning with Metamorphic-Relation GRPO](https://arxiv.org/abs/2607.03957) *— 2026 · arXiv:2607.03957*
5. [PBEBench: A Multi-Step Programming by Examples Reasoning Benchmark inspired by Historical Linguistics](https://aclanthology.org/2026.findings-acl.432/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
6. [QSTRBench: a New Benchmark to Evaluate the Ability of Language Models to Reason with Qualitative Spatial and Temporal Calculi](https://arxiv.org/abs/2605.18380) *— 2026 · arXiv:2605.18380*
7. [Word Surprisal Correlates with Sentential Contradiction in LLMs](https://aclanthology.org/2026.eacl-long.211/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
8. [A Comprehensive Survey on Long Context Language Modeling](https://arxiv.org/abs/2503.17407) *— 2025 · arXiv:2503.17407*
9. [BIG-Bench Extra Hard](https://aclanthology.org/2025.acl-long.1285/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
10. [Elements of World Knowledge ( EWoK ): A Cognition-Inspired Framework for Evaluating Basic World Knowledge in Language Models](https://aclanthology.org/2025.tacl-1.57/) *— 2025*
11. [From Reasoning to Learning: A Survey on Hypothesis Discovery and Rule Learning with Large Language Models](https://arxiv.org/abs/2505.21935) *— 2025 · arXiv:2505.21935*
12. [More is not always better? Enhancing Many-Shot In-Context Learning with Differentiated and Reweighting Objectives](https://aclanthology.org/2025.acl-long.1475/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
13. [Temporal Relation Classification: An XAI Perspective](https://aclanthology.org/2025.nodalida-1.72/) *— 2025 · Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)*
14. [Theory of Mind in Large Language Models: Assessment and Enhancement](https://aclanthology.org/2025.acl-long.1522/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
15. [Towards Rationality in Language and Multimodal Agents: A Survey](https://aclanthology.org/2025.naacl-long.186/) *— 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)*
16. [Advancing Spatial Reasoning in Large Language Models: An In-Depth Evaluation and Enhancement Using the StepGame Benchmark](https://arxiv.org/abs/2401.03991) *— 2024 · arXiv:2401.03991*
17. [Aquila2 Technical Report](https://arxiv.org/abs/2408.07410) *— 2024 · arXiv:2408.07410*
18. [Are self-explanations from Large Language Models faithful?](https://aclanthology.org/2024.findings-acl.19/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
19. [BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack](https://arxiv.org/abs/2406.10149) *— 2024 · arXiv:2406.10149*
20. [Belief Revision: The Adaptability of Large Language Models Reasoning](https://aclanthology.org/2024.emnlp-main.586/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
21. [Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models](https://aclanthology.org/2024.eacl-long.138/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
22. [Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research](https://aclanthology.org/2024.acl-long.840/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
23. [Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic Corpus](https://arxiv.org/abs/2411.12498) *— 2024 · arXiv:2411.12498*
24. [Instruction Pre-Training: Language Models are Supervised Multitask Learners](https://aclanthology.org/2024.emnlp-main.148/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
25. [Interpreting User Requests in the Context of Natural Language Standing Instructions](https://aclanthology.org/2024.findings-naacl.255/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
26. [JFLD: A Japanese Benchmark for Deductive Reasoning Based on Formal Logic](https://aclanthology.org/2024.lrec-main.832/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
27. [LLMs Are Prone to Fallacies in Causal Inference](https://aclanthology.org/2024.emnlp-main.590/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
28. [Mind''s Eye of LLMs: Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models](https://arxiv.org/abs/2404.03622) *— 2024 · arXiv:2404.03622*
29. [OpenPI2.0: An Improved Dataset for Entity Tracking in Texts](https://aclanthology.org/2024.eacl-long.10/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
30. [OpenToM: A Comprehensive Benchmark for Evaluating Theory-of-Mind Reasoning Capabilities of Large Language Models](https://aclanthology.org/2024.acl-long.466/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
31. [P-FOLIO: Evaluating and Improving Logical Reasoning with Abundant Human-Written Reasoning Chains](https://aclanthology.org/2024.findings-emnlp.966/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
32. [PIXAR: Auto-Regressive Language Modeling in Pixel Space](https://aclanthology.org/2024.findings-acl.874/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
33. [Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models](https://aclanthology.org/2024.acl-long.818/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
34. [SpaRC and SpaRP: Spatial Reasoning Characterization and Path Generation for Understanding Spatial Reasoning Capability of Large Language Models](https://aclanthology.org/2024.acl-long.261/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
35. [Teaching Small Language Models Reasoning through Counterfactual Distillation](https://aclanthology.org/2024.emnlp-main.333/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
36. [TopViewRS: Vision-Language Models as Top-View Spatial Reasoners](https://aclanthology.org/2024.emnlp-main.106/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
37. [Which Programming Language and What Features at Pre-training Stage Affect Downstream Logical Inference Performance?](https://aclanthology.org/2024.emnlp-main.1008/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
38. [tasksource: A Large Collection of NLP tasks with a Structured Dataset Preprocessing Framework](https://aclanthology.org/2024.lrec-main.1361/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
39. [基于参数高效微调与半监督学习的空间语义理解](https://aclanthology.org/2024.ccl-3.10/) *— 2024 · Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)*
40. [A Benchmark for Reasoning with Spatial Prepositions](https://aclanthology.org/2023.emnlp-main.1015/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
41. [A Memory Model for Question Answering from Streaming Data Supported by Rehearsal and Anticipation of Coreference Information](https://aclanthology.org/2023.findings-acl.830/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
42. [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://arxiv.org/abs/2302.04023) *— 2023 · arXiv:2302.04023*
43. [AI Alignment: A Comprehensive Survey](https://arxiv.org/abs/2310.19852) *— 2023 · arXiv:2310.19852*
44. [ALERT: Adapt Language Models to Reasoning Tasks](https://aclanthology.org/2023.acl-long.60/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
45. [Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond](https://arxiv.org/abs/2306.09841) *— 2023 · arXiv:2306.09841*
46. [COLLIE: Systematic Construction of Constrained Text Generation Tasks](https://arxiv.org/abs/2307.08689) *— 2023 · arXiv:2307.08689*
47. [Causal Reasoning of Entities and Events in Procedural Texts](https://aclanthology.org/2023.findings-eacl.31/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
48. [Certified Deductive Reasoning with Language Models](https://arxiv.org/abs/2306.04031) *— 2023 · arXiv:2306.04031*
49. [CoRRPUS: Code-based Structured Prompting for Neurosymbolic Story Understanding](https://aclanthology.org/2023.findings-acl.832/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
50. [Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text](https://aclanthology.org/2023.findings-acl.321/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
51. [Do Question Answering Modeling Improvements Hold Across Benchmarks?](https://aclanthology.org/2023.acl-long.736/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
52. [Entity Tracking in Language Models](https://arxiv.org/abs/2305.02363) *— 2023 · arXiv:2305.02363*
53. [Exploring the Capacity of Pretrained Language Models for Reasoning about Actions and Change](https://aclanthology.org/2023.acl-long.255/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
54. [Exploring the State of the Art in Legal QA Systems](https://arxiv.org/abs/2304.06623) *— 2023 · arXiv:2304.06623 · J Big Data 10, 127 (2023)*
55. [How Many Data Samples is an Additional Instruction Worth?](https://aclanthology.org/2023.findings-eacl.77/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
56. [Identifying the limits of transformers when performing model-checking with natural language](https://aclanthology.org/2023.eacl-main.257/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
57. [LMentry: A Language Model Benchmark of Elementary Language Tasks](https://aclanthology.org/2023.findings-acl.666/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
58. [Language Models Meet World Models: Embodied Experiences Enhance Language Models](https://arxiv.org/abs/2305.10626) *— 2023 · arXiv:2305.10626*
59. [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://arxiv.org/abs/2302.00093) *— 2023 · arXiv:2302.00093*
60. [Learning to Reason and Memorize with Self-Notes](https://arxiv.org/abs/2305.00833) *— 2023 · arXiv:2305.00833*
61. [Legal Question-Answering in the Indian Context: Efficacy, Challenges, and Potential of Modern AI Models](https://arxiv.org/abs/2309.14735) *— 2023 · arXiv:2309.14735*
62. [MindGames: Targeting Theory of Mind in Large Language Models with Dynamic Epistemic Modal Logic](https://aclanthology.org/2023.findings-emnlp.303/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
63. [MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning](https://arxiv.org/abs/2310.16049) *— 2023 · arXiv:2310.16049 · ICLR 2024 (Spotlight)*
64. [NarrativeXL: a Large-scale Dataset for Long-Term Memory Models](https://aclanthology.org/2023.findings-emnlp.1005/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
65. [Natural Language Reasoning, A Survey](https://arxiv.org/abs/2303.14725) *— 2023 · arXiv:2303.14725*
66. [ORCA: A Challenging Benchmark for Arabic Language Understanding](https://aclanthology.org/2023.findings-acl.609/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
67. [OpenPI-C: A Better Benchmark and Stronger Baseline for Open-Vocabulary State Tracking](https://aclanthology.org/2023.findings-acl.452/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
68. [Probing neural language models for understanding of words of estimative probability](https://aclanthology.org/2023.starsem-1.41/) *— 2023 · Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)*
69. [Scaling Data-Constrained Language Models](https://arxiv.org/abs/2305.16264) *— 2023 · arXiv:2305.16264*
70. [SpaCE2022中文空间语义理解评测任务数据集分析报告(A Quality Assessment Report of the Chinese Spatial Cognition Evaluation Benchmark)](https://aclanthology.org/2023.ccl-1.48/) *— 2023 · Proceedings of the 22nd Chinese National Conference on Computational Linguistics*
71. [The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants](https://arxiv.org/abs/2308.16884) *— 2023 · arXiv:2308.16884 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics 749-775 2024*
72. [The Role of Semantic Parsing in Understanding Procedural Text](https://aclanthology.org/2023.findings-eacl.137/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
73. [Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models](https://arxiv.org/abs/2310.00836) *— 2023 · arXiv:2310.00836*
74. [WorldSense: A Synthetic Benchmark for Grounded Reasoning in Large Language Models](https://arxiv.org/abs/2311.15930) *— 2023 · arXiv:2311.15930*
75. [事前学習済み言語モデルの知識に基づく演繹推論能力の調査](https://www.anlp.jp/proceedings/annual_meeting/2023/#B3-1) *— 2023 · 言語処理学会 第29回年次大会 発表論文集 (NLP2023)*
76. [ANLIzing the Adversarial Natural Language Inference Dataset](https://aclanthology.org/2022.scil-1.3/) *— 2022 · Proceedings of the Society for Computation in Linguistics 2022*
77. [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://arxiv.org/abs/2206.04615) *— 2022 · arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj*
78. [Breakpoint Transformers for Modeling and Tracking Intermediate Beliefs](https://aclanthology.org/2022.emnlp-main.658/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
79. [Coalescing Global and Local Information for Procedural Text Understanding](https://aclanthology.org/2022.coling-1.132/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
80. [Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding](https://aclanthology.org/2022.naacl-main.234/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
81. [Discovering Language Model Behaviors with Model-Written Evaluations](https://arxiv.org/abs/2212.09251) *— 2022 · arXiv:2212.09251*
82. [Dyna-bAbI: unlocking bAbI''s potential with dynamic synthetic benchmarking](https://aclanthology.org/2022.starsem-1.9/) *— 2022 · Proceedings of the 11th Joint Conference on Lexical and Computational Semantics*
83. [Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task](https://arxiv.org/abs/2210.13382) *— 2022 · arXiv:2210.13382*
84. [EvEntS ReaLM: Event Reasoning of Entity States via Language Models](https://aclanthology.org/2022.emnlp-main.129/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
85. [Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining](https://aclanthology.org/2022.findings-emnlp.125/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
86. [FaiRR: Faithful and Robust Deductive Reasoning over Natural Language](https://aclanthology.org/2022.acl-long.77/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
87. [Good Night at 4 pm?! Time Expressions in Different Cultures](https://aclanthology.org/2022.findings-acl.224/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
88. [Holistic Evaluation of Language Models](https://arxiv.org/abs/2211.09110) *— 2022 · arXiv:2211.09110 · Published in Transactions on Machine Learning Research (TMLR), 2023*
89. [Interpretable Proof Generation via Iterative Backward Reasoning](https://aclanthology.org/2022.naacl-main.216/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
90. [LILA: A Unified Benchmark for Mathematical Reasoning](https://aclanthology.org/2022.emnlp-main.392/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
91. [Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought](https://arxiv.org/abs/2210.01240) *— 2022 · arXiv:2210.01240*
92. [Leveraging Relaxed Equilibrium by Lazy Transition for Sequence Modeling](https://aclanthology.org/2022.acl-long.208/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
93. [Logical Reasoning for Task Oriented Dialogue Systems](https://aclanthology.org/2022.ecnlp-1.10/) *— 2022 · Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)*
94. [NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks](https://aclanthology.org/2022.acl-long.246/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
95. [Quantifying Adaptability in Pre-trained Language Models with 500 Tasks](https://aclanthology.org/2022.naacl-main.346/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
96. [Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning](https://arxiv.org/abs/2205.09712) *— 2022 · arXiv:2205.09712*
97. [StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts](https://md.mova.elamur.ai/free/en/docs/papers/shi-2022-stepgame-new-benchmark-robust-multi/) *— our card · 2022 · arXiv:2204.08292*
98. [The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning](https://arxiv.org/abs/2205.03401) *— 2022 · arXiv:2205.03401*
99. [Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation](https://aclanthology.org/2022.emnlp-main.20/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
100. [Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems](https://aclanthology.org/2022.findings-acl.33/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*

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