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Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Paper · 2023 · Hannah Rose Kirk, Bertie Vidgen, Paul Rottger et al.

Summary

The paper is a conceptual and policy piece, with no experiments. It asks how far large language models should adapt to individual users. The authors review current alignment practice, mainly RLHF and rule-based feedback. They argue that it has three weaknesses: alignment is poorly defined, providers impose their own definitions of subjective values, and models are tuned to a small, mostly US-based, often undocumented group of annotators (a "tyranny of the crowdworker"). They call this situation implicit personalisation and expect explicit, per-user personalisation to follow.

The authors build a taxonomy of benefits and risks for individuals and for society. Benefits include tailored help and better inclusion of under-represented groups. Risks include reinforced biases, narrower information diets and social fragmentation similar to echo chambers. They then propose a three-tier governance scheme that allows personalisation within fixed legal and organisational limits. The work aims to set rules before personalised assistants spread widely.

Key points

  • The paper separates three kinds of personalisation:

    • value (ideology),
    • preference (style, length, formality),
    • knowledge (remembered facts about the user).

  • The three governance tiers are:

    • Tier One: fixed (supra-)national restrictions, such as on illegal content like CSAM or terrorism.
    • Tier Two: optional rules set by the provider.
    • Tier Three: requirements set by the end-user.

  • Several figures motivate the work:

    • ChatGPT passed 100 million users within two months.
    • RLHF annotator pools usually number fewer than 100 people.
    • Perez et al. found that more RLHF can make models' views more polarised.

  • A title search found 124 ACL Anthology papers and 10 arXiv cs.CL papers on personalisation, showing prior work on the topic.

  • Open problems include:

    • compliance with GDPR and the Online Safety Bill,
    • oversight of systems that change over time and are spread across many users,
    • open-sourced models such as LLaMA, which make enforcement harder.

    As next steps, the authors plan interviews with users, providers and policymakers.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of OxfordOxford · United Kingdom104 works · 157,183 citations

 Fields, topics and concepts

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksSafety Research · 543 worksLaw · 77 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as Hannah Rose Kirk, Bertie Vidgen, Paul Rottger and Scott A. Hale. 2023. Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback. arXiv:2303.05453.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2303.05453 · arXiv 2303.05453

@misc{kirk2023personalisation,
  title = {{Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback}},
  author = {Hannah Rose Kirk, Bertie Vidgen, Paul Rottger and Scott A. Hale},
  year = {2023},
  doi = {10.48550/arxiv.2303.05453},
  eprint = {2303.05453},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2303.05453}
}

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