--- title: Multiple Choice Normalization in LM Evaluation | daily.dev description: Four normalization methods for scoring multiple choice tasks on autoregressive language models are compared: unnormalized (raw log probability), token-length... canonical: https://daily.dev/posts/multiple-choice-normalization-in-lm-evaluation-tdbmakhmz twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Multiple Choice Normalization in LM Evaluation | daily.dev og:description: Four normalization methods for scoring multiple choice tasks on autoregressive language models are compared: unnormalized (raw log probability), token-length... og:url: https://daily.dev/posts/multiple-choice-normalization-in-lm-evaluation-tdbmakhmz og:image: https://api.daily.dev/og/posts/TdBMAKhmZ.png og:image:alt: Multiple Choice Normalization in LM Evaluation og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Multiple Choice Normalization in LM Evaluation **[EleutherAI](https://daily.dev/sources/eleutherai)** · 2 min read · 0 upvotes · 0 comments ## Summary Four normalization methods for scoring multiple choice tasks on autoregressive language models are compared: unnormalized (raw log probability), token-length normalized (average log prob per token, used by GPT-3 but tokenization-dependent), byte-length normalized (average log prob per byte, tokenization-agnostic), and unconditional likelihood normalized (measures how much the prompt shifts the model's output probability). The unnormalized and byte-length normalized methods are both reported by EleutherAI's eval harness as `acc` and `acc_norm` respectively. Token-length normalization is excluded from the harness because it violates tokenization independence. Unconditional likelihood normalization requires an extra LM inference call. ## Full article daily.dev links to this article rather than hosting it. 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