Metric List

Automatic metrics for multiple choice tasks

These metrics use log-likelihood of the different possible targets.

• loglikelihood_acc: Fraction of instances where the choice with the best logprob was correct - also exists in a faster version for tasks where the possible choices include only one token (loglikelihood_acc_single_token)
• loglikelihood_acc_norm: Fraction of instances where the choice with the best logprob, normalized by sequence length, was correct - also exists in a faster version for tasks where the possible choices include only one token (loglikelihood_acc_norm_single_token)
• loglikelihood_acc_norm_nospace: Fraction of instances where the choice with the best logprob, normalized by sequence length, was correct, with the first space ignored
• loglikelihood_f1: Corpus level F1 score of the multichoice selection - also exists in a faster version for tasks where the possible choices include only one token (loglikelihood_f1_single_token)
• mcc: Matthew’s correlation coefficient (a measure of agreement between statistical distributions),
• recall_at_1: Fraction of instances where the choice with the best logprob was correct - also exists in a faster version for tasks where the possible choices include only one token per choice (recall_at_1_single_token)
• recall_at_2: Fraction of instances where the choice with the 2nd best logprob or better was correct - also exists in a faster version for tasks where the possible choices include only one token per choice (recall_at_2_single_token)
• mrr: Mean reciprocal rank, a measure of the quality of a ranking of choices ordered by correctness/relevance - also exists in a faster version for tasks where the possible choices include only one token (mrr_single_token)
• target_perplexity: Perplexity of the different choices available.
• acc_golds_likelihood:: A bit different, it actually checks if the average logprob of a single target is above or below 0.5
• multi_f1_numeric: Loglikelihood F1 score for multiple gold targets

All these metrics also exist in a “single token” version (loglikelihood_acc_single_token, loglikelihood_acc_norm_single_token, loglikelihood_f1_single_token, mcc_single_token, recall@2_single_token and mrr_single_token). When the multichoice option compares only one token (ex: “A” vs “B” vs “C” vs “D”, or “yes” vs “no”), using these metrics in the single token version will divide the time spent by the number of choices. Single token evals also include:

• multi_f1_numeric: computes the f1 score of all possible choices and averages it.

Automatic metrics for perplexity and language modeling

These metrics use log-likelihood of prompt.

• word_perplexity: Perplexity (log probability of the input) weighted by the number of words of the sequence.
• byte_perplexity: Perplexity (log probability of the input) weighted by the number of bytes of the sequence.
• bits_per_byte: Average number of bits per byte according to model probabilities.
• log_prob: Predicted output’s average log probability (input’s log prob for language modeling).

Automatic metrics for generative tasks

These metrics need the model to generate an output. They are therefore slower.

• Base:
  • perfect_exact_match: Fraction of instances where the prediction matches the gold exactly.
  • exact_match: Fraction of instances where the prediction matches the gold with the exception of the border whitespaces (= after a strip has been applied to both).
  • quasi_exact_match: Fraction of instances where the normalized prediction matches the normalized gold (normalization done on whitespace, articles, capitalization, …). Other variations exist, with other normalizers, such as quasi_exact_match_triviaqa, which only normalizes the predictions after applying a strip to all sentences.
  • prefix_exact_match: Fraction of instances where the beginning of the prediction matches the gold at the exception of the border whitespaces (= after a strip has been applied to both).
  • prefix_quasi_exact_match: Fraction of instances where the normalized beginning of the prediction matches the normalized gold (normalization done on whitespace, articles, capitalization, …)
  • exact_match_indicator: Exact match with some preceding context (before an indicator) removed
  • f1_score_quasi: Average F1 score in terms of word overlap between the model output and gold, with both being normalized first
  • f1_score: Average F1 score in terms of word overlap between the model output and gold without normalisation
  • f1_score_macro: Corpus level macro F1 score
  • f1_score_macro: Corpus level micro F1 score
  • maj_at_5 and maj_at_8: Model majority vote. Takes n (5 or 8) generations from the model and assumes the most frequent is the actual prediction.
• Summarization:
  • rouge: Average ROUGE score (Lin, 2004)
  • rouge1: Average ROUGE score (Lin, 2004) based on 1-gram overlap.
  • rouge2: Average ROUGE score (Lin, 2004) based on 2-gram overlap.
  • rougeL: Average ROUGE score (Lin, 2004) based on longest common subsequence overlap.
  • rougeLsum: Average ROUGE score (Lin, 2004) based on longest common subsequence overlap.
  • rouge_t5 (BigBench): Corpus level ROUGE score for all available ROUGE metrics
  • faithfulness: Faithfulness scores based on the SummaC method of Laban et al. (2022).
  • extractiveness: Reports, based on (Grusky et al., 2018)
    • summarization_coverage: Extent to which the model-generated summaries are extractive fragments from the source document,
    • summarization_density: Extent to which the model-generated summaries are extractive summaries based on the source document,
    • summarization_compression: Extent to which the model-generated summaries are compressed relative to the source document.
  • bert_score: Reports the average BERTScore precision, recall, and f1 score (Zhang et al., 2020) between model generation and gold summary.
  • Translation
  • bleu: Corpus level BLEU score (Papineni et al., 2002) - uses the sacrebleu implementation.
  • bleu_1: Average sample BLEU score (Papineni et al., 2002) based on 1-gram overlap - uses the nltk implementation.
  • bleu_4: Average sample BLEU score (Papineni et al., 2002) based on 4-gram overlap - uses the nltk implementation.
  • chrf: Character n-gram matches f-score.
  • ter: Translation edit/error rate.
• Copyright
  • copyright: Reports:
    • longest_common_prefix_length: average length of longest common prefix between model generation and reference,
    • edit_distance: average Levenshtein edit distance between model generation and reference,
    • edit_similarity: average Levenshtein edit similarity (normalized by length of longer sequence) between model generation and reference.
• Math:
  • quasi_exact_match_math: Fraction of instances where the normalized prediction matches the normalized gold (normalization done for math, where latex symbols, units, etc are removed)
  • maj_at_4_math: Majority choice evaluation, using the math normalisation for the predictions and gold
  • quasi_exact_match_gsm8k: Fraction of instances where the normalized prediction matches the normalized gold (normalization done for gsm8k, where latex symbols, units, etc are removed)
  • maj_at_8_gsm8k: Majority choice evaluation, using the gsm8k normalisation for the predictions and gold

LLM-as-Judge

• llm_judge_gpt3p5: Can be used for any generative task, the model will be scored by a GPT3.5 model using the OpenAI API
• llm_judge_llama_3_405b: Can be used for any generative task, the model will be scored by a Llama 3.405B model using the HuggingFace API
• llm_judge_multi_turn_gpt3p5: Can be used for any generative task, the model will be scored by a GPT3.5 model using the OpenAI API. It is used for multiturn tasks like mt-bench.
• llm_judge_multi_turn_llama_3_405b: Can be used for any generative task, the model will be scored by a Llama 3.405B model using the HuggingFace API. It is used for multiturn tasks like mt-bench.

< > Update on GitHub