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The LAMBADA dataset: Word prediction requiring a broad discourse context
Open accessAbstract
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, 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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“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
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“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]
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“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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Metadata
- 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
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