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Autoformalization with Large Language Models

NeurIPS 2022 · Large language models › Agents, code and math · approach: Formal verification and proof data · 42 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Scale PaLM from 8B to 540BPaLM autoformalization · MiniF2F algebraBLEU: 31.49 -> 50.30Helped
Scale PaLM from 8B to 540BPaLM autoformalization · MiniF2F number theoryBLEU: 22.10 -> 36.16Helped
Use Codex instead of PaLM 540BAutoformalization · MiniF2F algebraBLEU: 50.30 -> 57.13Helped
Use Codex instead of PaLM 540BAutoformalization · MiniF2F number theoryBLEU: 36.16 -> 43.33Helped
Add one expert iteration using proofs found on autoformalized statementsThor (M0) · MiniF2F validproof success rate: 28.3% -> 36.1%Helped
Add one expert iteration using proofs found on autoformalized statementsThor (M0) · MiniF2F testproof success rate: 29.9% -> 34.0%Helped
Add a second expert iterationThor after one expert iteration (M1) · MiniF2F validproof success rate: 36.1% -> 37.3%Helped
Add a second expert iterationThor after one expert iteration (M1) · MiniF2F testproof success rate: 34.0% -> 35.2%Helped
Use an additional, tangentially related few-shot example that explains how to formalize a lineCodex autoformalization · Linear-function statement case studyformalization correctnessHelped
Increase PaLM model size from 8B or 64B to 540BPaLM autoformalization · Madeline's school rows-of-eight problemformalization correctnessHelped

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