diff --git a/src/genbench/tasks/frequency_based_mathematics/__init__.py b/src/genbench/tasks/frequency_based_mathematics/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/genbench/tasks/frequency_based_mathematics/config.jsonnet b/src/genbench/tasks/frequency_based_mathematics/config.jsonnet new file mode 100644 index 0000000..2bf84c2 --- /dev/null +++ b/src/genbench/tasks/frequency_based_mathematics/config.jsonnet @@ -0,0 +1,53 @@ +{ + name: 'Frequency based mathematics', + description: 'This sample submission measures generalisation in the domain of mathematical + questions, by quantifying the extent to which correctness depends on the + frequency of the underlying terms. A model is said to be a stronger at + generalisation if its answers are less dependent on the term frequencies. + This test is inspired by the work of Razeghi et al (2022). + and the performance. + ', + keywords: [ + 'mathematics', + 'term_frequencies', + 'prompting', + 'LLMs' + ], + + authors: [ + 'Dieuwke Hupkes', + ], + + data_source: { + type: 'manual', + test: 'https://raw.githubusercontent.com/dieuwkehupkes/genbench_cbt_sample_submission/frequency_math/src/genbench/tasks/frequency_based_mathematics/test_data.jsonl', + }, + + has_validation_set: false, + has_train_set: false, + + task_type: 'free_form', + + evaluation_metrics: [ + { + hf_id: 'exact_match', + git_commit_sha: "758135da6a37ce962b7bc38c6dd5eab672d2b742", + best_score: 1.0, + } + ], + + preparation_strategies: { + // A recipe for preparing the model to perform the task by configuring its prompt. + // This recipe is suitable for generative LMs such as GPT-3, OPT, T5, etc. + // We provide a few options for configuring the prompt. But, the task creator can + // also provide a custom prompt preparation in the task's Python class. + prompt_based_testing: { + prompt_builder: { + instruction_zero_shot: '', // Left empty because the prompt is in the data + instruction_few_shot: '', // Left empty because the prompt is in the data + input_prefix: 'Q: ', + output_prefix: '\nA: ', + } + }, + }, +} diff --git a/src/genbench/tasks/frequency_based_mathematics/doc.md b/src/genbench/tasks/frequency_based_mathematics/doc.md new file mode 100644 index 0000000..8558275 --- /dev/null +++ b/src/genbench/tasks/frequency_based_mathematics/doc.md @@ -0,0 +1,69 @@ +# Frequency based mathematics -- an example task + +This is an example task for measuring generalisation in LLMs, given the frequency +of terms in their pretraining corpus. It quantifies memorisation in a simple +multiplication task, by computing the extent to which the accuracy of a model +depends on the frequency of the terms in the pretraining corpus. In particular, +it defines generalisation as `1 - abs(cor(accuracy, pretraining_term_frequencies)`. +We estimate the accuracy for a particular term by multiplying it with 20 other +terms, and computing the average score. +This measure is inspired by the work of [Razehgi et al (2022)](https://aclanthology.org/2022.findings-emnlp.59/), +who measure the correlation between the pretraining term frequencies and a models performance. + +*NB* This task serves illustrative purposes and is meant to provide inspiration. The measure has +not been tested on real data, and there are no empirical results (yet?) that prove that this +measure is useful in practice. + +## Abstract +Because this is an example task, there is no paper abstract. + +## Examples + +The samples in this task are simple arithmetics problems, e.g.: + +``` +What is ten times 31? +``` + +## Usage + +This task requires the user to compute the unigram frequencies of the numbers ten to fifty (spelled out). +Those terms should be provided in a dictionary, with as keys the terms (e.g. `twenty`) and as value the +(potentially normalised) frequency in the pretraining corpus. + +``` +# Load the task +task = load_task("frequency_based_mathematics") +ds = task.get_prepared_datasets( + PreparationStrategy.PROMPT_BASED_TESTING, + shot_list=[0])[0] + +# Load your pretraining frequencies and model predictions +pretraining_freqs = ... +preds = ... + +for pred_type, preds in preds.items(): + for freq_type, pretraining_freq in pretraining_freqs.items(): + scores = task.evaluate_predictions( + predictions=preds, + gold=ds, + term_freqs=pretraining_freq + ) + +print(f'Scores: {scores}') +``` + +## Data Source +The (dummy) test data is hosted at [https://github.com/dieuwkehupkes/genbench_cbt_sample_submission/blob/template/src/genbench/tasks/frequency_based_mathematics/test_data.jsonl], under an Apache 2.0 license. + +## Limitations and Bias +This is an example task for illustrative purposes, it has not been tested empirically. + +## GenBench eval card +This test can be used to test generalisation in LLMs (pretrain - test locus). +It is designed to better understand how LLMs generalise (intrinsic motivation), and can be used to assess +compositional generalisation or -- more generally -- robustness. +Because the test (input) samples differ in their frequency distribution wrt the training corpus, +we assume that the shift is a covariate shift. + +![GenBench Eval Card](eval_card.png) diff --git a/src/genbench/tasks/frequency_based_mathematics/eval_card.png b/src/genbench/tasks/frequency_based_mathematics/eval_card.png new file mode 100644 index 0000000..6d0adc2 Binary files /dev/null and b/src/genbench/tasks/frequency_based_mathematics/eval_card.png differ diff --git a/src/genbench/tasks/frequency_based_mathematics/task.py b/src/genbench/tasks/frequency_based_mathematics/task.py new file mode 100644 index 0000000..88cb939 --- /dev/null +++ b/src/genbench/tasks/frequency_based_mathematics/task.py @@ -0,0 +1,87 @@ +from typing import Any, List, Dict + +import datasets +from scipy.stats import pearsonr +from sklearn.metrics import accuracy_score + +from genbench.utils.logging import get_logger +from genbench import Task + +logger = get_logger(__name__) + + +class FrequencyBasedMathematicsTask(Task): + pass + """Python implementation of the FrequencyBasedMathematics task. + + This LLM task assess to what extent the computation of mathematical expressions + depends on the frequency of the individual terms during pretraining. + The test is inspired by the work of Razeghi et al (2022). + It defines a generalisation score as 1 - abs(pearsonsR(term_freq, score)) + + To facilitate this, it reimplements the following default functions: + - TODO list functions to be reimplemented + - __init__ : this function checks if a file with term frequences is + added by the user, and warns the user if it is not + - evaluate_predictions: this function implements the generalisation + metric used for this task + """ + + def evaluate_predictions( + self, + *, + predictions: List[Dict[str, Any]] = None, + gold: datasets.Dataset = None, + term_freqs: Dict[str, int] = None, + ) -> Dict[str, float]: + """Evaluate the predictions of the model against the gold data. + + For this task, the evaluation metric returns the minimum accuracy + among different prompts (i.e. if one of the prompts returns a wrong + answer, the score is 0 for that example). + + Args: + predictions: A list of dictionaries, where each dictionary contains the predicted + values for an example. The keys are strings and the values the model predictions. + gold: A HuggingFace `datasets.Dataset` object containing the ground truth data for the task. + term_freqs: A dictionary that maps the terms 10-30 to their frequencies in the pretraining corpus + + Returns: + A dictionary containing key-value pairs for the evaluation metric(s) computed on the predicted + values. The keys are strings representing the name of the evaluation metric and the values are + floating-point numbers. + """ + + if term_freqs is None: + raise Exception("This evaluation requires information about term frequencies in the pretraining corpus, that should be provided by the user. For more information, we refer to the readme of this task.") + + scores, freqs = [], [] + + preds_list = [pred["target"] for pred in predictions] + refs_list = [g["target"] for g in gold] + + ref_type = type(refs_list[0]) + pred_type = type(preds_list[0]) + + # Make sure predictions and gold are the same type + if pred_type != ref_type: + if pred_type == str and ref_type == int: + logger.warning("Predictions are strings, but references are ints. Converting predictions to ints.") + elif pred_type == int and ref_type == str: + logger.warning("Predictions are ints, but references are strings. Converting references to ints.") + + # In the data, there are 20 examples for each term + # in range (10, 50), we compute the avg accuracy per term + # by averaging over all samples with the term as first number + + for n in range(40): + # Fetch all accuracy scores for the predictions + for prediction in predictions: + acc = accuracy_score(preds_list[n:n+20], refs_list[n:n+20]) + freq = term_freqs[n+10] + scores.append(acc) + freqs.append(freq) + + corr = pearsonr(scores, freqs)[0] + + return {'gen_score': 1-abs(corr), 'accuracy': sum(scores)/len(scores)} diff --git a/src/genbench/tasks/frequency_based_mathematics/test_data.jsonl b/src/genbench/tasks/frequency_based_mathematics/test_data.jsonl new file mode 100644 index 0000000..341d945 --- /dev/null +++ b/src/genbench/tasks/frequency_based_mathematics/test_data.jsonl @@ -0,0 +1,800 @@ +{"input": "What is 10 times 10", "target": "100", "term1": "10", "term2": "10"} +{"input": "What is 10 times 11", "target": "110", "term1": "10", "term2": "11"} +{"input": "What is 10 times 12", "target": "120", "term1": "10", "term2": "12"} +{"input": "What is 10 times 13", "target": "130", "term1": "10", "term2": "13"} +{"input": "What is 10 times 14", "target": "140", "term1": "10", "term2": "14"} +{"input": "What is 10 times 15", "target": "150", "term1": "10", "term2": "15"} +{"input": "What is 10 times 16", "target": "160", "term1": "10", "term2": "16"} +{"input": "What is 10 times 17", "target": "170", "term1": "10", "term2": "17"} +{"input": "What is 10 times 18", "target": "180", "term1": "10", "term2": "18"} +{"input": "What is 10 times 19", "target": "190", "term1": "10", "term2": "19"} +{"input": "What is 10 times 20", "target": "200", "term1": "10", "term2": "20"} +{"input": "What is 10 times 21", "target": "210", "term1": "10", "term2": "21"} +{"input": "What is 10 times 22", "target": "220", "term1": "10", "term2": "22"} +{"input": "What is 10 times 23", "target": "230", "term1": "10", "term2": "23"} +{"input": "What is 10 times 24", "target": "240", "term1": "10", "term2": "24"} +{"input": "What is 10 times 25", "target": "250", "term1": "10", "term2": "25"} +{"input": "What is 10 times 26", "target": "260", "term1": "10", "term2": "26"} +{"input": "What is 10 times 27", "target": "270", "term1": "10", "term2": "27"} +{"input": "What is 10 times 28", "target": "280", "term1": "10", "term2": "28"} +{"input": "What is 10 times 29", "target": "290", "term1": "10", "term2": "29"} +{"input": "What is 11 times 10", "target": "110", "term1": "11", "term2": "10"} +{"input": "What is 11 times 11", "target": "121", "term1": "11", "term2": "11"} +{"input": "What is 11 times 12", "target": "132", "term1": "11", "term2": "12"} +{"input": "What is 11 times 13", "target": "143", "term1": "11", "term2": "13"} +{"input": "What is 11 times 14", "target": "154", "term1": "11", "term2": "14"} +{"input": "What is 11 times 15", "target": "165", "term1": "11", "term2": "15"} +{"input": "What is 11 times 16", 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"17"} +{"input": "What is 14 times 18", "target": "252", "term1": "14", "term2": "18"} +{"input": "What is 14 times 19", "target": "266", "term1": "14", "term2": "19"} +{"input": "What is 14 times 20", "target": "280", "term1": "14", "term2": "20"} +{"input": "What is 14 times 21", "target": "294", "term1": "14", "term2": "21"} +{"input": "What is 14 times 22", "target": "308", "term1": "14", "term2": "22"} +{"input": "What is 14 times 23", "target": "322", "term1": "14", "term2": "23"} +{"input": "What is 14 times 24", "target": "336", "term1": "14", "term2": "24"} +{"input": "What is 14 times 25", "target": "350", "term1": "14", "term2": "25"} +{"input": "What is 14 times 26", "target": "364", "term1": "14", "term2": "26"} +{"input": "What is 14 times 27", "target": "378", "term1": "14", "term2": "27"} +{"input": "What is 14 times 28", "target": "392", "term1": "14", "term2": "28"} +{"input": "What is 14 times 29", "target": "406", "term1": "14", "term2": "29"} +{"input": "What is 15 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"14"} +{"input": "What is 16 times 15", "target": "240", "term1": "16", "term2": "15"} +{"input": "What is 16 times 16", "target": "256", "term1": "16", "term2": "16"} +{"input": "What is 16 times 17", "target": "272", "term1": "16", "term2": "17"} +{"input": "What is 16 times 18", "target": "288", "term1": "16", "term2": "18"} +{"input": "What is 16 times 19", "target": "304", "term1": "16", "term2": "19"} +{"input": "What is 16 times 20", "target": "320", "term1": "16", "term2": "20"} +{"input": "What is 16 times 21", "target": "336", "term1": "16", "term2": "21"} +{"input": "What is 16 times 22", "target": "352", "term1": "16", "term2": "22"} +{"input": "What is 16 times 23", "target": "368", "term1": "16", "term2": "23"} +{"input": "What is 16 times 24", "target": "384", "term1": "16", "term2": "24"} +{"input": "What is 16 times 25", "target": "400", "term1": "16", "term2": "25"} +{"input": "What is 16 times 26", "target": "416", "term1": "16", "term2": "26"} +{"input": "What is 16 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