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AmazonScience/PersonaLens

991 downloads in 30 days2 likesLast modified 20 Aug 2025View on Hugging Face
Use restrictions

The declared licence (cc-by-nc-4.0) is classified as having use restrictions.

Declared licence
cc-by-nc-4.0, from the tagEvidence: README.md at 1d44916
Licence files
LICENSE.md, detected as cc-by-nc-4.0Evidence: LICENSE.md at 1d44916
Declared sources
None listed in the card metadata
Access gated on Hugging Face
No
AmazonScience/PersonaLensAmazonScience/Perso…cc-by-nc-4.0: declared licencecc-by-nc-4.0declared licenceREADME.md ↗cc-by-nc-4.0: licence filecc-by-nc-4.0licence fileLICENSE.md ↗
AmazonScience/PersonaLensAmazonScience/P…cc-by-nc-4.0: declared licencecc-by-nc-4.0declared licenceREADME.md ↗cc-by-nc-4.0: licence filecc-by-nc-4.0licence fileLICENSE.md ↗
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Tasks

Citation text from the card

@inproceedings{zhao-etal-2025-personalens,
    title = "{P}ersona{L}ens: A Benchmark for Personalization Evaluation in Conversational {AI} Assistants",
    author = "Zhao, Zheng  and
      Vania, Clara  and
      Kayal, Subhradeep  and
      Khan, Naila  and
      Cohen, Shay B  and
      Yilmaz, Emine",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-acl.927/",
    doi = "10.18653/v1/2025.findings-acl.927",
    pages = "18023--18055",
    ISBN = "979-8-89176-256-5",
    abstract = "Large language models (LLMs) have advanced conversational AI assistants. However, systematically evaluating how well these assistants apply personalization{---}adapting to individual user preferences while completing tasks{---}remains challenging. Existing personalization benchmarks focus on chit-chat, non-conversational tasks, or narrow domains, failing to capture the complexities of personalized task-oriented assistance. To address this, we introduce PersonaLens, a comprehensive benchmark for evaluating personalization in task-oriented AI assistants. Our benchmark features diverse user profiles equipped with rich preferences and interaction histories, along wit

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dataset_licence({ "id": "AmazonScience/PersonaLens" })
signal: "restrictive"
stated_licence: "cc-by-nc-4.0"
sources: 0