Galactica large language model

Published: November 2022

Galatica is a large language model developed by Facebook that 'can store, combine and reason about scientific knowledge' in order to assist scientists 'summarize academic papers, solve math problems, generate Wiki articles, write scientific code, annotate molecules and proteins, and more'.

The system was trained on 106 billion tokens of open-access scientific text and data, including papers, textbooks, scientific websites, encyclopedias, reference material, and knowledge bases.

Released in November 2022, the model was withdrawn after three days in the wake of criticism from prominent AI researchers and technology commentators.

Critical response

Criticism of Galactica has focused on the ease with which it could be prompted to generate inaccurate, racist, anti-semitic, homophobic, and offensive articles and research, and scientific misinformation.

According to AI researcher and entrepreneur Gary Marcus, Galactica constitutes 'pitch perfect and utterly bogus imitations of science and math, presented as the real thing.'

'It’s no longer possible to have some fun by casually misusing it. Happy?' Meta’s chief AI scientist Yann LeCun followed-up.

Transparency

In a nod to the actual and/or potential limitations of its system, Meta notes (pdf) that 'there are no guarantees for truthful or reliable output from language models, even large ones on high-quality data like Galactica,' adding that the generated text might appear 'very authentic and highly confident,' but could still be wrong.

For Technology Review's Will Douglas Heaven, Meta's suggestion that 'the human-like text such models generate will always contain trustworthy information, as Meta appeared to do in its promotion of Galactica, is reckless and irresponsible.'

The marketing of the system demonstrates 'the all-too-common tendency of AI researchers to exaggerate the abilities of the systems they build', according to AI commentator Alberto Romero.

Operator: Meta/Facebook
Developer: Meta/Facebook
Country: USA
Sector: Technology
Purpose: Assist scientists
Technology: Large language model (LLM); NLP/text analysis; Neural network; Deep learning
Issue: Accuracy/reliability; Bias/discrimination - race, ethnicity, gender, religion; Mis/disinformation; Safety
Transparency: Black box; Marketing

System reference

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