Sparrow is achatbot developed by theartificial intelligence research labDeepMind, a subsidiary ofAlphabet Inc. It is designed to answer users' questions correctly, while reducing the risk of unsafe and inappropriate answers.[1] One motivation behind Sparrow is to address the problem oflanguage models producing incorrect, biased or potentially harmful outputs.[1][2] Sparrow is trained using human judgements, in order to be more “Helpful, Correct and Harmless” compared to baseline pre-trained language models.[1] The development of Sparrow involved asking paid study participants to interact with Sparrow, and collecting their preferences to train a model of how useful an answer is.[2]
To improve accuracy and help avoid the problem ofhallucinating incorrect answers, Sparrow has the ability to search the Internet using Google Search[1][2][3] in order to find and cite evidence for any factual claims it makes.
To make the model safer, its behaviour is constrained by a set of rules, for example "don't make threatening statements" and "don't make hateful or insulting comments", as well as rules about possibly harmful advice, and not claiming to be a person.[1] During development study participants were asked to converse with the system and try to trick it into breaking these rules.[2] A 'rule model' was trained on judgements from these participants, which was used for further training.
Sparrow was introduced in a paper in September 2022, titled "Improving alignment of dialogue agents via targeted human judgements";[4] however, the bot was not released publicly.[1][3] DeepMind CEO Demis Hassabis said DeepMind is considering releasing Sparrow for a "private beta" some time in 2023.[4][5][6]
Sparrow is a deep neural network based on thetransformer machine learning model architecture. It is fine-tuned from DeepMind'sChinchilla AI pre-trainedlarge language model (LLM),[1] which has 70 Billion parameters.[7]
Sparrow is trained usingreinforcement learning from human feedback (RLHF),[1][3] although some supervised fine-tuning techniques are also used. The RLHF training utilizes two reward models to capture human judgements: a “preference model” that predicts what a human study participant would prefer and a “rule model” that predicts if the model has broken one of the rules.[3]
Sparrow's training data corpus is mainly in English, meaning it performs worse in other languages.[citation needed]
When adversarially probed by study participants it breaks the rules 8% of the time;[2] however, this is still three times lower than the baseline prompted pre-trained model (Chinchilla).
| Computer programs |
| ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Machine learning |
| ||||||||||
| Generative AI |
| ||||||||||
| See also | |||||||||||
