PaLM (2022) › Applications and Impact
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PaLM's evaluations covered arithmetic and reasoning tasks, multilingual transfer, and code generation. These were research results on a pretrained model rather than a released product or a guarantee of dependable problem solving.
The multilingual results showed that one training mixture could support few-shot tasks across many languages. Quality still varied by language and benchmark, especially where training data was scarce.
Code benchmarks tested completion and generation from natural-language prompts. They did not measure debugging or code review. Nor did they cover testing and long-term maintenance.
Google published the PaLM paper and evaluation results but did not openly release the 540-billion-parameter model weights. Researchers could study the reported methods and outputs without reproducing the complete system.
PaLM's broad evaluation table became a comparison point for later models. As with other benchmark suites, results depended on the prompts and datasets. Scoring procedures also affected the comparison, which did not summarize deployment reliability.