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# coding=utf-8
# Copyright 2022 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""NoMIRACL: A dataset to evaluation LLM robustness across 18 languages."""
import os
import json
import csv
import datasets
from collections import defaultdict
_CITATION = """\
@inproceedings{thakur-etal-2024-knowing,
title = "{``}Knowing When You Don{'}t Know{''}: A Multilingual Relevance Assessment Dataset for Robust Retrieval-Augmented Generation",
author = "Thakur, Nandan and
Bonifacio, Luiz and
Zhang, Crystina and
Ogundepo, Odunayo and
Kamalloo, Ehsan and
Alfonso-Hermelo, David and
Li, Xiaoguang and
Liu, Qun and
Chen, Boxing and
Rezagholizadeh, Mehdi and
Lin, Jimmy",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.730",
pages = "12508--12526",
abstract = "Retrieval-Augmented Generation (RAG) grounds Large Language Model (LLM) output by leveraging external knowledge sources to reduce factual hallucinations. However, prior work lacks a comprehensive evaluation of different language families, making it challenging to evaluate LLM robustness against errors in external retrieved knowledge. To overcome this, we establish **NoMIRACL**, a human-annotated dataset for evaluating LLM robustness in RAG across 18 typologically diverse languages. NoMIRACL includes both a non-relevant and a relevant subset. Queries in the non-relevant subset contain passages judged as non-relevant, whereas queries in the relevant subset include at least a single judged relevant passage. We measure relevance assessment using: (i) *hallucination rate*, measuring model tendency to hallucinate when the answer is not present in passages in the non-relevant subset, and (ii) *error rate*, measuring model inaccuracy to recognize relevant passages in the relevant subset. In our work, we observe that most models struggle to balance the two capacities. Models such as LLAMA-2 and Orca-2 achieve over 88{\%} hallucination rate on the non-relevant subset. Mistral and LLAMA-3 hallucinate less but can achieve up to a 74.9{\%} error rate on the relevant subset. Overall, GPT-4 is observed to provide the best tradeoff on both subsets, highlighting future work necessary to improve LLM robustness. NoMIRACL dataset and evaluation code are available at: https://github.com/project-miracl/nomiracl.",
}
"""
_DESCRIPTION = """\
Data Loader for the NoMIRACL dataset.
"""
_URL = "https://nomiracl.github.io"
_DL_URL_FORMAT = "data/{name}"
def load_topics(filepath: str):
"""
Loads queries from a file and stores them in a dictionary.
"""
queries = {}
with open(filepath, 'r', encoding='utf-8') as f:
reader = csv.reader(f, delimiter='\t', quoting=csv.QUOTE_NONE)
for row in reader:
queries[row[0]] = row[1]
return queries
def load_corpus(filepath: str):
"""
Loads the corpus file as a dictionary.
"""
corpus = {}
with open(filepath, encoding='utf8') as fIn:
for line in fIn:
line = json.loads(line)
corpus[line.get("docid")] = {
"text": line.get("text", "").strip(),
"title": line.get("title", "").strip(),
}
return corpus
def load_qrels(filepath: str):
if filepath is None:
return None
qrels = defaultdict(dict)
with open(filepath, encoding="utf-8") as f:
for line in f:
qid, _, docid, rel = line.strip().split('\t')
qrels[qid][docid] = int(rel)
return qrels
class NoMIRACLConfig(datasets.BuilderConfig):
"""BuilderConfig for NoMIRACL."""
def __init__(self, name, **kwargs):
"""
Args:
name: `string`, name of dataset config (=language)
**kwargs: keyword arguments forwarded to super.
"""
super(NoMIRACLConfig, self).__init__(
version=datasets.Version("1.0.0", ""), name=name.lower(), **kwargs
)
# relative path to full data inside a repo (for example `data/german`)
self.data_root_url = _DL_URL_FORMAT.format(name=name)
class NoMIRACL(datasets.GeneratorBasedBuilder):
"""Multilingual NoMIRACL dataset."""
BUILDER_CONFIGS = [
NoMIRACLConfig(name="arabic", description="Arabic NoMIRACL dataset"),
NoMIRACLConfig(name="chinese", description="Chinese NoMIRACL dataset"),
NoMIRACLConfig(name="finnish", description="Finnish NoMIRACL dataset"),
NoMIRACLConfig(name="german", description="German NoMIRACL dataset"),
NoMIRACLConfig(name="indonesian", description="Indonesian NoMIRACL dataset"),
NoMIRACLConfig(name="korean", description="Korean NoMIRACL dataset"),
NoMIRACLConfig(name="russian", description="Russian NoMIRACL dataset"),
NoMIRACLConfig(name="swahili", description="Swahili NoMIRACL dataset"),
NoMIRACLConfig(name="thai", description="Thai NoMIRACL dataset"),
NoMIRACLConfig(name="bengali", description="Bengali NoMIRACL dataset"),
NoMIRACLConfig(name="english", description="English NoMIRACL dataset"),
NoMIRACLConfig(name="french", description="French NoMIRACL dataset"),
NoMIRACLConfig(name="hindi", description="Hindi NoMIRACL dataset"),
NoMIRACLConfig(name="japanese", description="Japanese NoMIRACL dataset"),
NoMIRACLConfig(name="persian", description="Persian NoMIRACL dataset"),
NoMIRACLConfig(name="spanish", description="Spanish NoMIRACL dataset"),
NoMIRACLConfig(name="telugu", description="Telugu NoMIRACL dataset"),
NoMIRACLConfig(name="yoruba", description="Yoruba NoMIRACL dataset"),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({
'query_id': datasets.Value('string'),
'query': datasets.Value('string'),
'positive_passages': [{
'docid': datasets.Value('string'),
'text': datasets.Value('string'),
'title': datasets.Value('string')
}],
'negative_passages': [{
'docid': datasets.Value('string'),
'text': datasets.Value('string'),
'title': datasets.Value('string'),
}],
}),
supervised_keys=("file", "text"),
homepage=_URL,
citation=_CITATION
)
def _split_generators(self, dl_manager):
# Download downloaded_files
downloaded_files = dl_manager.download_and_extract({
"corpus": self.config.data_root_url + "/corpus.jsonl.gz",
"dev": {"qrels": {"relevant": self.config.data_root_url + "/qrels/dev.relevant.tsv",
"non_relevant": self.config.data_root_url + "/qrels/dev.non_relevant.tsv"},
"topics": {"relevant": self.config.data_root_url + "/topics/dev.relevant.tsv",
"non_relevant": self.config.data_root_url + "/topics/dev.non_relevant.tsv"}},
"test": {"qrels": {"relevant": self.config.data_root_url + "/qrels/test.relevant.tsv",
"non_relevant": self.config.data_root_url + "/qrels/test.non_relevant.tsv"},
"topics": {"relevant": self.config.data_root_url + "/topics/test.relevant.tsv",
"non_relevant": self.config.data_root_url + "/topics/test.non_relevant.tsv"}},
})
splits = [
datasets.SplitGenerator(
name="dev.relevant",
gen_kwargs={
"corpus_path": downloaded_files["corpus"],
"qrels_path": downloaded_files["dev"]["qrels"]["relevant"],
"topics_path": downloaded_files["dev"]["topics"]["relevant"],
}
),
datasets.SplitGenerator(
name="dev.non_relevant",
gen_kwargs={
"corpus_path": downloaded_files["corpus"],
"qrels_path": downloaded_files["dev"]["qrels"]["non_relevant"],
"topics_path": downloaded_files["dev"]["topics"]["non_relevant"],
},
),
datasets.SplitGenerator(
name="test.relevant",
gen_kwargs={
"corpus_path": downloaded_files["corpus"],
"qrels_path": downloaded_files["test"]["qrels"]["relevant"],
"topics_path": downloaded_files["test"]["topics"]["relevant"],
}
),
datasets.SplitGenerator(
name="test.non_relevant",
gen_kwargs={
"corpus_path": downloaded_files["corpus"],
"qrels_path": downloaded_files["test"]["qrels"]["non_relevant"],
"topics_path": downloaded_files["test"]["topics"]["non_relevant"],
},
),
]
return splits
def _generate_examples(self, corpus_path, qrels_path, topics_path):
corpus = load_corpus(corpus_path)
qrels = load_qrels(qrels_path)
topics = load_topics(topics_path)
for qid in topics:
data = {}
data['query_id'] = qid
data['query'] = topics[qid]
pos_docids = [docid for docid, rel in qrels[qid].items() if rel == 1] if qrels is not None else []
neg_docids = [docid for docid, rel in qrels[qid].items() if rel == 0] if qrels is not None else []
data['positive_passages'] = [{
'docid': docid,
**corpus[docid]
} for docid in pos_docids if docid in corpus]
data['negative_passages'] = [{
'docid': docid,
**corpus[docid]
} for docid in neg_docids if docid in corpus]
yield qid, data
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