Developer(s) | Google AI |
---|---|
Predecessor | LaMDA |
Successor | Google Gemini |
Available in | English |
Type | Large language model |
Website | ai |
PaLM (Pathways Language Model) is a 540 billion-parameter transformer-based large language model (LLM) developed by Google AI.[1] Researchers also trained smaller versions of PaLM (with 8 and 62 billion parameters) to test the effects of model scale.[2]
PaLM is capable of a wide range of tasks, including commonsense reasoning, arithmetic reasoning, joke explanation, code generation, and translation.[2][3][4][5] When combined with chain-of-thought prompting, PaLM achieved significantly better performance on datasets requiring reasoning of multiple steps, such as word problems and logic-based questions.[1][2]
The model was first announced in April 2022 and remained private until March 2023, when Google launched an API for PaLM and several other technologies.[6] The API was initially available to a limited number of developers who joined a waitlist before it was released to the public.[7]
Google and DeepMind developed a version of PaLM 540B (the parameter count, 540 billion), called Med-PaLM, that is fine-tuned on medical data and outperforms previous models on medical question answering benchmarks.[8][9] Med-PaLM was the first to obtain a passing score on U.S. medical licensing questions, and in addition to answering both multiple choice and open-ended questions accurately, it also provides reasoning and is able to evaluate its own responses.[10]
Google also extended PaLM using a vision transformer to create PaLM-E, a state-of-the-art vision-language model that can be used for robotic manipulation.[11][12] The model can perform tasks in robotics competitively without the need for retraining or fine-tuning.[13]
In May 2023, Google announced PaLM 2 at the annual Google I/O keynote.[14] PaLM 2 is reported to be a 340 billion-parameter model trained on 3.6 trillion tokens.[15]
In June 2023, Google announced AudioPaLM for speech-to-speech translation, which uses the PaLM-2 architecture and initialization.[16]
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