The LAMBADA dataset: Word prediction requiring a broad discourse context
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Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Ngoc Quan Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, Raquel Fernández. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.
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There is no 'ground-truth' implementation from the original benchmark authors for many popular benchmarks that have been retrofitted for compatibility with autoregressive LMs.
“There is no 'ground-truth' implementation from the original benchmark authors, and many popular benchmarks (Paperno et al., 2016; Clark et al., 2018; Zellers et al., 2019) that have been 'retrofitted' for compatibility with autoregressive LMs.”
From Lessons from the Trenches on Reproducible Evaluation of Language Models (2024) · cites this paper as Paperno et al., 2016
Section 4 (Modeling experiments; page unavailable) establishes that Paperno et al. introduced LAMBADA as a benchmark and evaluated language-model baselines on it. Our copy does not say that LAMBADA was later retrofitted for autoregressive LMs or that its authors provided no ‘ground-truth’ implementation for such use. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe downstream tasks LAMBADA, HellaSwag, PIQA, ARC-challenge, ARC-easy, and WinoGrande measure common sense reasoning.
“for evaluating the following tasks that measure common sense reasoning: LAMBADA (OpenAI version in LM Evaluation Harness) (Paperno et al., 2016), HellaSwag (Zellers et al., 2019), PIQA (Bisk et al., 2020), ARC-challenge, ARC-easy (Clark et al., 2018), WinoGrande (Sakaguchi et al., 2021).”
From xLSTM: Extended Long Short-Term Memory (2024) · cites this paper as Paperno et al., 2016
The Conclusion identifies LAMBADA as a benchmark for predicting a word using broad discourse context, and §3.3 describes items requiring inference about familiar events and general reasoning. This supports a reasoning component, but the paper does not characterize LAMBADA specifically as a measure of common-sense reasoning. The Paperno et al. citation is attached to LAMBADA, not to the other listed tasks. (LLM, conf=0.85)
Experimental — accuracy not yet measuredIn contrast to LAMBADA and GSM8K, which test skills not directly applicable to software engineering, the SWAA set represents actions needed in both software engineering work and the longer tasks studied.
“In contrast to other simple benchmarks like LAMBADA [44] or GSM8K [45], which test skills not directly applicable to software engineering, the SWAA set represents actions that are needed in both software engineering work and the longer tasks we study.”
From Measuring AI Ability to Complete Long Software Tasks (2025) · cites this paper as [44]
On LAMBADA, Chinchilla achieves 77.4% accuracy, compared to 74.5% from Gopher and 76.6% from MT-NLG 530B.
“On the final word prediction dataset LAMBADA (Paperno et al., 2016), Chinchilla achieves 77.4% accuracy, compared to 74.5% accuracy from Gopher and 76.6% from MT-NLG 530B (see Table 7).”
From Training Compute-Optimal Large Language Models (2022) · cites this paper as Paperno et al., 2016
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Zhou, K., Zhu, Y., Chen, Z., Chen, W., Zhao, W. X., Chen, X., Lin, Y., Wen, J.-R., & Han, J. (2023) · arXiv · doi:10.48550/arxiv.2311.01964 · Cited by 25 · see citing works
Kaushik, D., Kiela, D., Lipton, Z. C., & Yih, W.-T. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.517 · Cited by 26 · see citing works
Broad Context Language Modeling as Reading Comprehension
Chu, Z., Wang, H., Gimpel, K., & McAllester, D. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-2009 · Cited by 20 · see citing works
Titans: Learning to Memorize at Test Time
Behrouz, A., Zhong, P., & Mirrokni, V. (2025) · arXiv · doi:10.48550/arxiv.2501.00663 · Cited by 24 · see citing works
“Following recent studies on linear recurrent models [35, 30, 38], we use Wikitext [123], LMB [124], PIQA [125], HellaSwag [126], WinoGrande [127], ARC-easy (ARC-e) and ARC-challenge (ARC-c) [128], SIQA [129], and BoolQ [130].”
Benchmarking Machine Reading Comprehension: A Psychological Perspective
Sugawara, S., Stenetorp, P., & Aizawa, A. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.137 · Cited by 21 · see citing works
RWKV: Reinventing RNNs for the Transformer Era
Peng, B., Alcaide, E., Anthony, Q., Albalak, A., Arcadinho, S., Biderman, S., Cao, H., Cheng, X., Chung, M., Grella, M., et al. (2023) · arXiv · doi:10.48550/arxiv.2305.13048 · Cited by 17 · see citing works
“Tasks: • LAMBADA (Paperno et al., 2016).”
Sugawara, S., Yokono, H., & Aizawa, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10957 · Cited by 17 · see citing works
Chess as a Testbed for Language Model State Tracking
Toshniwal, S., Wiseman, S., Livescu, K., & Gimpel, K. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21390 · Cited by 22 · see citing works
Mitigating harm in language models with conditional-likelihood filtrationPreprint
Ngo, H., Raterink, C., Araújo, J. G. M., Zhang, I., Chen, C., Morisot, A., & Frosst, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.07790 · Cited by 16 · see citing works
Mori, Y., Yamane, H., Ushiku, Y., & Harada, T. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.03.006 · Cited by 15 · see citing works
Understanding and Controlling Memory in Recurrent Neural NetworksPreprint
Haviv, D., Rivkind, A., & Barak, O. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.07275 · Cited by 15 · see citing works
Liu, N. F., Lee, T., Jia, R., & Liang, P. (2021) · arXiv (Cornell University) · Cited by 17 · see citing works
Rationales for Sequential Predictions
Vafa, K., Deng, Y., Blei, D., & Rush, A. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.807 · Cited by 14 · see citing works
Measuring AI Ability to Complete Long Software Tasks
Kwa, T., West, B., Becker, J., Deng, A., Garcia, K., Hasin, M., Jawhar, S., Kinniment, M., Rush, N., Von Arx, S., et al. (2025) · arXiv · doi:10.48550/arxiv.2503.14499 · Cited by 14 · see citing works
The Non-native Speaker Aspect: Indian English in Social Media
Sarkar, R., Mahinder, S., & KhudaBukhsh, A. R. (2020) · Workshop on Noisy User-generated Text (W-NUT) · doi:10.18653/v1/2020.wnut-1.9 · Cited by 14 · see citing works
Entity Tracking Improves Cloze-style Reading Comprehension
Hoang, L., Wiseman, S., & Rush, A. M. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1130 · Cited by 12 · see citing works
A Gated Dilated Convolution with Attention Model for Clinical Cloze-Style Reading Comprehension
Wang, B., Zhang, X., Zhou, X., & Li, J. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17041323 · Cited by 12 · see citing works
Validating Large Language Models with ReLM
Kuchnik, M., Smith, V., & Amvrosiadis, G. (2022) · arXiv · doi:10.48550/arxiv.2211.15458 · Cited by 12 · see citing works
Hardware Accelerator for Approximation-Based Softmax and Layer Normalization in Transformers
Kim, R., Lee, D., Kim, J., Park, J., & Lee, S. E. (2025) · Electronics · doi:10.3390/electronics14122337 · Cited by 10 · see citing works
Emergent Predication Structure in Hidden State Vectors of Neural Readers
Wang, H., Onishi, T., Gimpel, K., & McAllester, D. (2017) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w17-2604 · Cited by 10 · see citing works
Revisiting Simple Neural Probabilistic Language Models
Sun, S., & Iyyer, M. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.407 · Cited by 9 · see citing works
Gong, Z., Liu, J., Wang, J., Cai, X., Zhao, D., & Yan, R. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i16.29765 · Cited by 7 · see citing works
Residual Shuffle-Exchange Networks for Fast Processing of Long Sequences
Draguns, A., Ozoliņš, E., Šostaks, A., Apinis, M., & Freivalds, K. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i8.16890 · Cited by 8 · see citing works
Probing Pre-Trained Language Models for Disease Knowledge
Alghanmi, I., Espinosa-Anke, L., & Schockaert, S. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.266 · Cited by 7 · see citing works
Learning Associative Inference Using Fast Weight MemoryPreprint
Schlag, I., Munkhdalai, T., & Schmidhuber, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.07831 · Cited by 7 · see citing works
Measuring text readability with machine comprehension: a pilot study
Benzahra, M., & Yvon, F. (2019) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/w19-4443 · Cited by 6 · see citing works
Lessons from the Trenches on Reproducible Evaluation of Language Models
Biderman, S., Schoelkopf, H., Sutawika, L., Gao, L., Tow, J., Abbasi, B., Aji, A. F., Ammanamanchi, P. S., Black, S., Clive, J., et al. (2024) · arXiv · doi:10.48550/arxiv.2405.14782 · Cited by 9 · see citing works
“There is no “ground-truth” implementation from the original benchmark authors, and many popular benchmarks (Paperno et al., 2016; Clark et al., 2018; Zellers et al., 2019) that have been “retrofitted” for compatibility with autoregressive LMs.”
Limitations in learning an interpreted language with recurrent models
Paperno, D. (2018) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/w18-5456 · Cited by 5 · see citing works
Self-citationSome of Them Can be Guessed! Exploring the Effect of Linguistic Context in Predicting Quantifiers
Pezzelle, S., Steinert‐Threlkeld, S., Raffaella Bernardi, B., & Szymanik, J. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-2019 · Cited by 5 · see citing works
Self-citationSCDE: Sentence Cloze Dataset with High Quality Distractors From Examinations
Kong, X., Gangal, V., & Hovy, E. H. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.502 · Cited by 5 · see citing works
On-The-Fly Information Retrieval Augmentation for Language Models
Wang, H., & McAllester, D. (2020) · doi:10.18653/v1/2020.nuse-1.14 · Cited by 4 · see citing works
Emergent Logical Structure in Vector Representations of Neural Readers.Preprint
Wang, H., Onishi, T., Gimpel, K., & McAllester, D. (2016) · arXiv (Cornell University) · Cited by 4 · see citing works
Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading ComprehensionPreprint
Wang, L., Li, S., Zhao, W., Shen, K., Sun, M., Jia, R., & Liu, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.06289 · Cited by 4 · see citing works
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IRepair: An Intent-Aware Approach to Repair Data-Driven Errors in Large Language Models
Imtiaz, S. M., Singh, A., Batole, F., & Rajan, H. (2025) · Proceedings of the ACM on software engineering. · doi:10.1145/3715775 · Cited by 3 · see citing works
Stance Detection Using Transformer Architectures and Temporal Convolutional Networks
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Disentangling Societal Inequality from Model Biases: Gender Inequality in Divorce Court Proceedings
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Think before you speak: Training Language Models With Pause Tokens
Goyal, S., Ji, Z., Rawat, A. S., Menon, A. K., Kumar, S., & Nagarajan, V. (2023) · arXiv · doi:10.48550/arxiv.2310.02226 · Cited by 2
“, 2020)), (d) long term context recall (LAMBADA (Paperno et al., 2016)), (e) natural language inference (HellaSwag (Zellers et al.”
BackgroundMethodAI SoC Design Challenges in the Foundation Model Era
Chen, Z.-Y., Huang, D.-W., Wang, M., Yang, B., Shin, J. L., Hu, C., Li, B., Prabhakar, R., Deng, G., Sheng, Y.-N., et al. (2023) · IEEE Custom Integrated Circuits Conference · doi:10.1109/cicc57935.2023.10121242 · Cited by 3 · see citing works
Task-Agnostic Language Model Watermarking via High Entropy Passthrough Layers
Masrani, V., Akbari, M. A., Yue, D. M. X., Rezaei, A., & Zhang, Y. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i23.34667 · Cited by 2 · see citing works
CoreLM: Coreference-aware Language Model Fine-Tuning
Stylianou, N., & Vlahavas, I. P. (2021) · Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC) · doi:10.18653/v1/2021.crac-1.8 · Cited by 2 · see citing works
- Authors
- Paperno, D., Kruszewski, G., Lazaridou, A., Pham, N. Q., Bernardi, R., Pezzelle, S., Baroni, M., Boleda, G., & Fernandez, R.
- Affiliations
- University of Trento (IT)
- Venue
- Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2016
- Pages
- 1525-1534
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2473344385
- Cited by
- 87
- References
- 15
- Citations by year
- 2025: 4 · 2024: 4 · 2023: 4 · 2022: 2 · 2021: 24 · 2020: 17 · 2019: 7 · 2018: 12 · 2017: 11 · 2016: 2
- FWCI
- 5.93
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- UvA-DARE (University of Amsterdam) (submittedVersion, cc-by)
- Institutional Research Information System (Università degli Studi di Trento) (submittedVersion, cc-by-nc-sa)
- Open Research (University of Surrey) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Nvidia
- European Commission (655577, 283554, HORIZON2020)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (276-89-008)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- word prediction, discourse context, language models, discourse coherence, next sentence prediction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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