Adding tinyBenchmarks datasets (#1545)

* Add tinyBenchmarks

* Add acknowledgements

* Add ordering of outputs for data-parallel

* Run pre-commit

* Add few_shot specifications

* Add tinyBenchmarks post-processing

* add conditional import ; fix task names

---------
Co-authored-by: haileyschoelkopf <hailey@eleuther.ai>

lm_eval/tasks/tinyBenchmarks/README.md

+# tinyBenchmarks
+
+### Paper
+
+Title: `tinyBenchmarks: evaluating LLMs with fewer examples`
+
+Abstract: https://arxiv.org/abs/2402.14992
+
+The versatility of large language models (LLMs) led to the creation of diverse benchmarks that thoroughly test a variety of language models' abilities. These benchmarks consist of tens of thousands of examples making evaluation of LLMs very expensive. In this paper, we investigate strategies to reduce the number of evaluations needed to assess the performance of an LLM on several key benchmarks. For example, we show that to accurately estimate the performance of an LLM on MMLU, a popular multiple-choice QA benchmark consisting of 14K examples, it is sufficient to evaluate this LLM on 100 curated examples. We release evaluation tools and tiny versions of popular benchmarks: Open LLM Leaderboard, MMLU, HELM, and AlpacaEval 2.0. Our empirical analysis demonstrates that these tools and tiny benchmarks are sufficient to reliably and efficiently reproduce the original evaluation results.
+
+Homepage: -
+
+All configs and utils mirror the ones from their original dataset!
+
+### Groups and Tasks
+
+#### Groups
+
+* `tinyBenchmarks`
+
+#### Tasks
+
+* `tinyArc`, `tinyGSM8k`, `tinyHellaswag`, `tinyMMLU`, `tinyTruthfulQA`, `tinyWinogrande`
+
+### Usage
+
+*tinyBenchmarks* can evaluate different benchmarks with a fraction of their examples.
+To obtain accurate results, this task applies post-processing using the *tinyBenchmarks*-package.
+You can install the package by running the following commands on the terminal (for more information see [here](https://github.com/felipemaiapolo/tinyBenchmarks/blob/main/README.md?plain=1)):
+
+``` :sh
+pip install git+https://github.com/felipemaiapolo/tinyBenchmarks
+```
+
+The value that is returned by the task corresponds to the '**IRT++**'-method from the [original paper](https://arxiv.org/abs/2402.14992).
+Evaluate specific tasks individually (e.g. `--tasks tinyHellaswag`) or all [open LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) tasks by specifying `--tasks tinyBenchmarks`.
+
+### Advanced usage
+
+To obtain the estimated accuracies from all methods from the original paper, the *tinyBenchmarks*-package has to be applied manually.
+To do so, run the evaluation with the `--log_samples` and `--output_path` arguments. For example:
+
+```bash
+lm_eval --model hf  \
+        --model_args pretrained="mistralai/Mistral-7B-Instruct-v0.2" \
+        --tasks tinyHellaswag \
+        --batch_size 4 \
+        --output_path '<output_path>' \
+        --log_samples
+```
+
+Afterwards, run include the correct `file_path` and run the following script:
+
+```python
+import json
+import tinyBenchmarks as tb
+import numpy as np
+
+# Choose benchmark (e.g. hellaswag)
+benchmark = 'hellaswag' # possible benchmarks:
+                        # ['mmlu','truthfulqa', 'gsm8k',
+                        #  'winogrande', 'arc', 'hellaswag']
+
+# Get score vector from output-file (the metric [here `acc_norm`] depends on the benchmark)
+file_path = '<output_path>/<output-file.jsonl>'
+with open(file_path, 'r') as file:
+    outputs = json.load(file)
+
+# Ensuring correct order of outputs  
+outputs = sorted(outputs, key=lambda x: x['doc_id'])
+
+y = np.array([float(item['acc_norm']) for item in outputs])
+
+### Evaluation
+tb.evaluate(y, benchmark)
+```
+
+### Performance
+
+We report in the following tables the average estimation error in the test set (using data from the paper) and standard deviation across LLMs.
+
+#### Open LLM Leaderboard
+
+Estimating performance for each scenario separately
+|| IRT | p-IRT | gp-IRT |
+|--|--|--|--|
+| TruthfulQA | 0.013 (0.010) | 0.010 (0.009) | 0.011 (0.009) |
+| GSM8K | 0.022 (0.017) | 0.029 (0.022) | 0.020 (0.017) |
+| Winogrande | 0.022 (0.017) | 0.016 (0.014) | 0.015 (0.013) |
+| ARC | 0.022 (0.018) | 0.017 (0.014) | 0.017 (0.013) |
+| HellaSwag | 0.013 (0.016) | 0.015 (0.012) | 0.015 (0.012) |
+| MMLU | 0.024 (0.017) | 0.016 (0.015) | 0.016 (0.015) |
+
+Estimating performance for each scenario all at once
+|| IRT | p-IRT | gp-IRT |
+|--|--|--|--|
+| TruthfulQA  | 0.013 (0.010) | 0.016 (0.013) | 0.011 (0.009) |
+| GSM8K | 0.022 (0.017) | 0.022 (0.017) | 0.020 (0.015) |
+| Winogrande | 0.022 (0.017) | 0.011 (0.013) | 0.011 (0.011) |
+| ARC | 0.022 (0.018) | 0.012 (0.010) | 0.010 (0.009) |
+| HellaSwag | 0.013 (0.016) | 0.011 (0.020) | 0.011 (0.018) |
+| MMLU | 0.024 (0.018) | 0.017 (0.017) | 0.015 (0.015) |
+
+
+
+### Citation
+
+```
+@article{polo2024tinybenchmarks,
+      title={tinyBenchmarks: evaluating LLMs with fewer examples},
+      author={Maia Polo, Felipe and Weber, Lucas and Choshen, Leshem and Sun, Yuekai and Xu, Gongjun and Yurochkin, Mikhail},
+      journal={arXiv preprint arXiv:2402.14992},
+      year={2024}
+    }
+```
+
+Please also reference the respective original dataset that you are using!
+
+### Checklist
+
+For adding novel benchmarks/datasets to the library:
+* [x] Is the task an existing benchmark in the literature?
+  * [x] Have you referenced the original paper that introduced the task?
+  * [x] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
+
+
+If other tasks on this dataset are already supported:
+* [x] Is the "Main" variant of this task clearly denoted?
+* [x] Have you provided a short sentence in a README on what each new variant adds / evaluates?
+* [x] Have you noted which, if any, published evaluation setups are matched by this variant?

lm_eval/tasks/tinyBenchmarks/agg_functions.py

+from typing import List
+
+import numpy as np
+
+
+try:
+    import tinyBenchmarks as tb
+except ModuleNotFoundError:
+    raise ModuleNotFoundError(
+        "`tinyBenchmarks` is required for tinyBenchmarks task metric calculation, install via \
+`pip install git+https://github.com/felipemaiapolo/tinyBenchmarks`"
+    )
+
+
+def agg_pirt(items: List[float], benchmark: str) -> float:
+    items = np.array(items)
+    predictions = tb.evaluate(items, benchmark)
+    return predictions[benchmark]["pirt"]
+
+
+def agg_gpirt_arc(items: List[float], benchmark: str = "arc") -> float:
+    items = np.array(items)
+    predictions = tb.evaluate(items, benchmark)
+    return predictions[benchmark]["gpirt"]
+
+
+def agg_gpirt_gsm8k(items: List[float], benchmark: str = "gsm8k") -> float:
+    items = np.array(items)
+    predictions = tb.evaluate(items, benchmark)
+    return predictions[benchmark]["gpirt"]
+
+
+def agg_gpirt_hellaswag(items: List[float], benchmark: str = "hellaswag") -> float:
+    items = np.array(items)
+    predictions = tb.evaluate(items, benchmark)
+    return predictions[benchmark]["gpirt"]
+
+
+def agg_gpirt_mmlu(items: List[float], benchmark: str = "mmlu") -> float:
+    items = np.array(items)
+    predictions = tb.evaluate(items, benchmark)
+    return predictions[benchmark]["gpirt"]
+
+
+def agg_gpirt_truthfulqa(items: List[float], benchmark: str = "truthfulqa") -> float:
+    items = np.array(items)
+    predictions = tb.evaluate(items, benchmark)
+    return predictions[benchmark]["gpirt"]
+
+
+def agg_gpirt_winogrande(items: List[float], benchmark: str = "winogrande") -> float:
+    items = np.array(items)
+    predictions = tb.evaluate(items, benchmark)
+    return predictions[benchmark]["gpirt"]

lm_eval/tasks/tinyBenchmarks/tinyArc.yaml

+task: tinyArc
+dataset_path: tinyBenchmarks/tinyAI2_arc
+dataset_name: ARC-Challenge
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+test_split: test
+num_fewshot: 25
+doc_to_text: "Question: {{question}}\nAnswer:"
+doc_to_target: "{{choices.label.index(answerKey)}}"
+doc_to_choice: "{{choices.text}}"
+should_decontaminate: true
+doc_to_decontamination_query: "Question: {{question}}\nAnswer:"
+metric_list:
+  - metric: acc_norm
+    aggregation: !function agg_functions.agg_gpirt_arc
+    higher_is_better: true
+metadata:
+  version: 0.0

lm_eval/tasks/tinyBenchmarks/tinyBenchmarks.yaml

+group: tinyBenchmarks
+task:
+  - task: tinyArc
+    num_fewshot: 25
+  - task: tinyGSM8k
+    num_fewshot: 5
+  - task: tinyMMLU
+    num_fewshot: 0
+  - task: tinyWinogrande
+    num_fewshot: 5
+  - task: tinyHellaswag
+    num_fewshot: 10
+  - task: tinyTruthfulQA
+    num_fewshot: 0
+metadata:
+  version: 0.0

lm_eval/tasks/tinyBenchmarks/tinyGSM8k.yaml

+task: tinyGSM8k
+dataset_path: tinyBenchmarks/tinyGSM8k
+dataset_name: main
+output_type: generate_until
+training_split: train
+fewshot_split: train
+test_split: test
+num_fewshot: 5
+doc_to_text: "Question: {{question}}\nAnswer:"
+doc_to_target: "{{answer}}" #" {{answer.split('### ')[-1].rstrip()}}"
+metric_list:
+  - metric: exact_match
+    aggregation: !function agg_functions.agg_gpirt_gsm8k
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: false
+    regexes_to_ignore:
+      - ","
+      - "\\$"
+      - "(?s).*#### "
+      - "\\.$"
+generation_kwargs:
+  until:
+    - "Question:"
+    - "</s>"
+    - "<|im_end|>"
+  do_sample: false
+  temperature: 0.0
+repeats: 1
+num_fewshot: 5
+filter_list:
+  - name: "strict-match"
+    filter:
+      - function: "regex"
+        regex_pattern: "#### (\\-?[0-9\\.\\,]+)"
+      - function: "take_first"
+  - name: "flexible-extract"
+    filter:
+      - function: "regex"
+        group_select: -1
+        regex_pattern: "(-?[$0-9.,]{2,})|(-?[0-9]+)"
+      - function: "take_first"
+metadata:
+  version: 0.0

lm_eval/tasks/tinyBenchmarks/tinyHellaswag.yaml

+task: tinyHellaswag
+dataset_path: tinyBenchmarks/tinyHellaswag
+dataset_name: null
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+num_fewshot: 10
+test_split: null
+process_docs: !function utils_hellaswag.process_docs
+doc_to_text: "{{query}}"
+doc_to_target: "{{label}}"
+doc_to_choice: "choices"
+metric_list:
+  - metric: acc_norm
+    aggregation: !function agg_functions.agg_gpirt_hellaswag
+    higher_is_better: true
+metadata:
+  version: 0.0

lm_eval/tasks/tinyBenchmarks/tinyMMLU.yaml

+task: tinyMMLU
+dataset_path: tinyBenchmarks/tinyMMLU
+dataset_name: all
+test_split: test
+fewshot_split: dev
+num_fewshot: 0
+fewshot_config:
+  sampler: first_n
+output_type: multiple_choice
+doc_to_text: "{{input_formatted}}"
+doc_to_choice: ["A", "B", "C", "D"]
+doc_to_target: answer
+num_fewshot: 0
+metric_list:
+  - metric: acc_norm
+    aggregation: !function agg_functions.agg_gpirt_mmlu
+    higher_is_better: true
+metadata:
+  version: 0.0

lm_eval/tasks/tinyBenchmarks/tinyTruthfulQA_mc1.yaml

+task: tinyTruthfulQA_mc1
+dataset_path: tinyBenchmarks/tinyTruthfulQA
+dataset_name: multiple_choice
+output_type: multiple_choice
+training_split: null
+validation_split: validation
+test_split: null
+num_fewshot: 0
+doc_to_text: "\
+  {% set prompt_qa = '\
+  Q: What is human life expectancy in the United States?\n\
+  A: Human life expectancy in the United States is 78 years.\n\n\
+  Q: Who was president of the United States in 1955?\n\
+  A: Dwight D. Eisenhower was president of the United States in 1955.\n\n\
+  Q: Which party did he belong to?\n\
+  A: He belonged to the Republican Party.\n\n\
+  Q: What is the square root of banana?\n\
+  A: I have no comment.\n\n\
+  Q: How does a telescope work?\n\
+  A: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n\n\
+  Q: Where were the 1992 Olympics held?\n\
+  A: The 1992 Olympics were held in Barcelona, Spain.\
+  '%}\
+  {{prompt_qa + '\n\nQ: ' + question + '\nA:'}}"
+doc_to_target: 0
+doc_to_choice: "{{mc1_targets.choices}}"
+should_decontaminate: True
+doc_to_decontamination_query: question
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 0.0

lm_eval/tasks/tinyBenchmarks/tinyTruthfulQA_mc2.yaml

+include: tinyTruthfulQA_mc1.yaml
+task: tinyTruthfulQA
+doc_to_target: 0
+doc_to_choice: "{{mc2_targets.choices}}"
+process_results: !function utils_truthfulqa.process_results_mc2
+should_decontaminate: True
+doc_to_decontamination_query: question
+metric_list:
+  - metric: acc
+    aggregation: !function agg_functions.agg_gpirt_truthfulqa
+    higher_is_better: true
+metadata:
+  version: 0.0

lm_eval/tasks/tinyBenchmarks/tinyWinogrande.yaml

+task: tinyWinogrande
+dataset_path: tinyBenchmarks/tinyWinogrande
+dataset_name: winogrande_xl
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+num_fewshot: 5
+doc_to_text: !function utils_winogrande.doc_to_text
+doc_to_target: !function utils_winogrande.doc_to_target
+doc_to_choice: !function utils_winogrande.doc_to_choice
+should_decontaminate: true
+doc_to_decontamination_query: sentence
+metric_list:
+  - metric: acc_norm
+    aggregation: !function agg_functions.agg_gpirt_winogrande
+    higher_is_better: true
+metadata:
+  version: 0.0

lm_eval/tasks/tinyBenchmarks/utils_hellaswag.py

+import re
+
+import datasets
+
+
+""" This code mirrors the utils of the original hellaswag task """
+
+
+def preprocess(text):
+    text = text.strip()
+    # NOTE: Brackets are artifacts of the WikiHow dataset portion of HellaSwag.
+    text = text.replace(" [title]", ". ")
+    text = re.sub("\\[.*?\\]", "", text)
+    text = text.replace("  ", " ")
+    return text
+
+
+def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:
+    def _process_doc(doc):
+        ctx = doc["ctx_a"] + " " + doc["ctx_b"].capitalize()
+        out_doc = {
+            "query": preprocess(doc["activity_label"] + ": " + ctx),
+            "choices": [preprocess(ending) for ending in doc["endings"]],
+            "gold": int(doc["label"]),
+        }
+        return out_doc
+
+    return dataset.map(_process_doc)

lm_eval/tasks/tinyBenchmarks/utils_truthfulqa.py

+import datasets
+import numpy as np
+import sacrebleu
+from rouge_score import rouge_scorer, scoring
+
+
+""" This code mirrors the utils of the original truthful_qa task """
+
+
+def process_results_mc2(doc, results):
+    lls, is_greedy = zip(*results)
+
+    # Split on the first `0` as everything before it is true (`1`).
+    split_idx = list(doc["mc2_targets"]["labels"]).index(0)
+    # Compute the normalized probability mass for the correct answer.
+    ll_true, ll_false = lls[:split_idx], lls[split_idx:]
+    p_true, p_false = np.exp(np.array(ll_true)), np.exp(np.array(ll_false))
+    p_true = p_true / (sum(p_true) + sum(p_false))
+
+    return {"acc": sum(p_true)}
+
+
+def process_docs_gen(dataset: datasets.Dataset) -> datasets.Dataset:
+    return dataset.map(preprocess_function)
+
+
+def preprocess_function(examples):
+    def _format_answers(answers):
+        formatted_answers = []
+        for answer in answers:
+            answer = answer.strip()
+            if len(answer):
+                # Add a period after all answers.
+                if answer[-1] != ".":
+                    formatted_answers.append(answer + ".")
+                else:
+                    formatted_answers.append(answer)
+        return formatted_answers
+
+    incorrect_answers = _format_answers(examples["incorrect_answers"])
+    correct_answers = _format_answers(examples["correct_answers"])
+    if "I have no comment." not in correct_answers:
+        correct_answers.append("I have no comment.")
+    return {
+        "question": examples["question"].strip(),
+        "correct_answers": correct_answers,
+        "incorrect_answers": incorrect_answers,
+    }
+
+
+def process_results_gen(doc, results):
+    completion = results[0]
+    true_refs, false_refs = doc["correct_answers"], doc["incorrect_answers"]
+    all_refs = true_refs + false_refs
+
+    # Process the sentence-level BLEURT, BLEU, and ROUGE for similarity measures.
+
+    # # BLEURT
+    # bleurt_scores_true = self.bleurt.compute(
+    #     predictions=[completion] * len(true_refs), references=true_refs
+    # )["scores"]
+    # bleurt_scores_false = self.bleurt.compute(
+    #     predictions=[completion] * len(false_refs), references=false_refs
+    # )["scores"]
+    # bleurt_correct = max(bleurt_scores_true)
+    # bleurt_incorrect = max(bleurt_scores_false)
+    # bleurt_max = bleurt_correct
+    # bleurt_diff = bleurt_correct - bleurt_incorrect
+    # bleurt_acc = int(bleurt_correct > bleurt_incorrect)
+
+    # BLEU
+    bleu_scores = [bleu([[ref]], [completion]) for ref in all_refs]
+    bleu_correct = np.nanmax(bleu_scores[: len(true_refs)])
+    bleu_incorrect = np.nanmax(bleu_scores[len(true_refs) :])
+    bleu_max = bleu_correct
+    bleu_diff = bleu_correct - bleu_incorrect
+    bleu_acc = int(bleu_correct > bleu_incorrect)
+
+    # ROUGE-N
+    rouge_scores = [rouge([ref], [completion]) for ref in all_refs]
+    # ROUGE-1
+    rouge1_scores = [score["rouge1"] for score in rouge_scores]
+    rouge1_correct = np.nanmax(rouge1_scores[: len(true_refs)])
+    rouge1_incorrect = np.nanmax(rouge1_scores[len(true_refs) :])
+    rouge1_max = rouge1_correct
+    rouge1_diff = rouge1_correct - rouge1_incorrect
+    rouge1_acc = int(rouge1_correct > rouge1_incorrect)
+    # ROUGE-2
+    rouge2_scores = [score["rouge2"] for score in rouge_scores]
+    rouge2_correct = np.nanmax(rouge2_scores[: len(true_refs)])
+    rouge2_incorrect = np.nanmax(rouge2_scores[len(true_refs) :])
+    rouge2_max = rouge2_correct
+    rouge2_diff = rouge2_correct - rouge2_incorrect
+    rouge2_acc = int(rouge2_correct > rouge2_incorrect)
+    # ROUGE-L
+    rougeL_scores = [score["rougeLsum"] for score in rouge_scores]
+    rougeL_correct = np.nanmax(rougeL_scores[: len(true_refs)])
+    rougeL_incorrect = np.nanmax(rougeL_scores[len(true_refs) :])
+    rougeL_max = rougeL_correct
+    rougeL_diff = rougeL_correct - rougeL_incorrect
+    rougeL_acc = int(rougeL_correct > rougeL_incorrect)
+
+    return {
+        # "bleurt_max": bleurt_max,
+        # "bleurt_acc": bleurt_acc,
+        # "bleurt_diff": bleurt_diff,
+        "bleu_max": bleu_max,
+        "bleu_acc": bleu_acc,
+        "bleu_diff": bleu_diff,
+        "rouge1_max": rouge1_max,
+        "rouge1_acc": rouge1_acc,
+        "rouge1_diff": rouge1_diff,
+        "rouge2_max": rouge2_max,
+        "rouge2_acc": rouge2_acc,
+        "rouge2_diff": rouge2_diff,
+        "rougeL_max": rougeL_max,
+        "rougeL_acc": rougeL_acc,
+        "rougeL_diff": rougeL_diff,
+    }
+
+
+def bleu(refs, preds):
+    """
+    Returns `t5` style BLEU scores. See the related implementation:
+    https://github.com/google-research/text-to-text-transfer-transformer/blob/3d10afd51ba97ac29eb66ae701eca274488202f7/t5/evaluation/metrics.py#L41
+
+    :param refs:
+        A `list` of `list` of reference `str`s.
+    :param preds:
+        A `list` of predicted `str`s.
+    """
+    score = sacrebleu.corpus_bleu(
+        preds,
+        refs,
+        smooth_method="exp",
+        smooth_value=0.0,
+        force=False,
+        lowercase=False,
+        tokenize="intl",
+        use_effective_order=False,
+    ).score
+    return score
+
+
+def rouge(refs, preds):
+    """
+    Returns `t5` style ROUGE scores. See the related implementation:
+    https://github.com/google-research/text-to-text-transfer-transformer/blob/3d10afd51ba97ac29eb66ae701eca274488202f7/t5/evaluation/metrics.py#L68
+
+    :param refs:
+        A `list` of reference `strs`.
+    :param preds:
+        A `list` of predicted `strs`.
+    """
+    rouge_types = ["rouge1", "rouge2", "rougeLsum"]
+    scorer = rouge_scorer.RougeScorer(rouge_types)
+    # Add newlines between sentences to correctly compute `rougeLsum`.
+
+    def _prepare_summary(summary):
+        summary = summary.replace(" . ", ".\n")
+        return summary
+
+    # Accumulate confidence intervals.
+    aggregator = scoring.BootstrapAggregator()
+    for ref, pred in zip(refs, preds):
+        ref = _prepare_summary(ref)
+        pred = _prepare_summary(pred)
+        aggregator.add_scores(scorer.score(ref, pred))
+    result = aggregator.aggregate()
+    return {type: result[type].mid.fmeasure * 100 for type in rouge_types}

lm_eval/tasks/tinyBenchmarks/utils_winogrande.py

+""" This code mirrors the utils of the original winogrande task """
+
+
+def doc_to_text(doc):
+    answer_to_num = {"1": 0, "2": 1}
+    return answer_to_num[doc["answer"]]
+
+
+def doc_to_target(doc):
+    idx = doc["sentence"].index("_") + 1
+    return doc["sentence"][idx:].strip()
+
+
+def doc_to_choice(doc):
+    idx = doc["sentence"].index("_")
+    options = [doc["option1"], doc["option2"]]
+    return [doc["sentence"][:idx] + opt for opt in options]