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BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive license to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/2208.03188

Summary

The paper addresses a scaling problem in open-domain dialogue. Fine-tuning large language models on crowdsourced data works, but such data is costly, limited in volume, and may not match what real users want. The authors present BlenderBot 3 (BB3), a dialogue agent that Meta AI deployed on a public website for adults in the United States. Its interactions are intended to feed later rounds of retraining. BB3 is a 175B-parameter transformer initialized from OPT-175B. A single network runs a chain of sequence-to-sequence modules, each selected by a control code: deciding whether to search, writing a search query, using retrieved documents, and reading and writing a long-term memory. Fine-tuning drew on many public dialogue datasets and on findings about learning from human feedback. A companion study covers methods for spotting adversarial users. In human evaluations, BB3 outperformed openly available chatbots, including BlenderBot 1 and 2, by a wide margin. The work matters because it releases weights, code and data, giving researchers a reproducible base for studying agents that improve through use while remaining responsible.

Key points

  • BB3 comes in three sizes. The 30B and 175B versions build on OPT, while the 3B version builds on the R2C2 model from SeeKeR.
  • Internet search in the deployed system uses the Mojeek engine. Special control codes tell the shared model which module it is currently executing.
  • The "continual learning" is not online. Models are retrained in large successive batches using data gathered from deployment.
  • Access to the 175B weights is granted by request to academic, government, civil-society and industry research labs. Code, logbook and a model card are public.
  • Known limitations include training on English data only, occasional toxic or wrong outputs (especially under adversarial prompting), and added safety risks from users trying to teach the bot misinformation.

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

Fields: Other

Cited by

139 papers of our library cite it (of the 140673 whose bibliographies we read); 17 of them have a page here, 122 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2208.03188 · arXiv 2208.03188

@misc{shuster2022blenderbot,
  title = {{BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage}},
  author = {Shuster, Kurt and Xu, Jing and Komeili, Mojtaba and Ju, Da and Smith, Eric Michael and Roller, Stephen and Ung, Megan and Chen, Moya and Arora, Kushal and Lane, Joshua and Behrooz, Morteza and Ngan, William and Poff, Spencer and Goyal, Naman and Szlam, Arthur and Boureau, Y-Lan and Kambadur, Melanie and Weston, Jason},
  year = {2022},
  doi = {10.48550/arxiv.2208.03188},
  eprint = {2208.03188},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2208.03188}
}

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