orgrctera/beir_fiqa

FiQA (BEIR) — Financial QA retrieval

Dataset description

FiQA (Financial Question Answering) is a domain-specific benchmark for retrieving passages that answer real financial questions. It was introduced as part of the WWW 2018 workshop challenge on Financial Opinion Mining and Question Answering, motivated by the need for NLP and IR methods that cope with noisy, opinion-rich, domain-specific text in finance.

BEIR (Benchmarking IR) repackaged FiQA—along with many other public corpora—as a standard retrieval benchmark for zero-shot evaluation of dense, sparse, and hybrid information retrieval models across heterogeneous tasks and domains. FiQA is one of the question-focused retrieval tasks in BEIR: systems must rank passages from a financial Q&A corpus so that human-judged relevant documents appear at the top of the list.

This repository (orgrctera/beir_fiqa) provides train / validation / test splits in Parquet form for use in retrieval evaluation pipelines. Each row is one query with relevance judgments pointing at corpus document identifiers (as distributed in the BEIR FiQA benchmark).

Scale and domain (BEIR FiQA)

Typical statistics for the BEIR FiQA setting (corpus + queries + qrels) are on the order of:

• Tens of thousands of short documents (passages) drawn from English financial community Q&A.
• Thousands of queries (natural-language financial questions).
• Binary (or graded) qrels linking each query to one or more relevant document IDs.

Exact counts follow the standard BEIR FiQA release; see the upstream project for version-precise figures.

Task: retrieval

The task is ad hoc passage (or document) retrieval:

1. Input: a natural-language question (the query).
2. Output: a ranked list of document IDs from the FiQA corpus (or scores over the full collection), such that relevant IDs—according to the official qrels—receive high rank.

Downstream metrics are standard IR metrics (e.g., nDCG@k, Recall@k, MRR), as implemented in BEIR’s evaluation scripts or in frameworks such as Pyserini / BEIR’s own API.

Note: Full retrieval evaluation also requires the corpus (passage text keyed by ID). This dataset card describes the query + qrels side as prepared for CTERA-style evaluation rows; align corpus IDs with the same BEIR FiQA corpus you use for indexing.

Data format (this repository)

Each record includes:

Field	Description
id	UUID for this example row.
input	The query text (financial question).
expected_output	JSON string: list of objects {"id": "<corpus-doc-id>", "score": <relevance>}. Scores follow the BEIR qrels convention (typically 1 for relevant in binary settings).
metadata.query_id	Original BEIR / FiQA query identifier (string).
metadata.split	Split name: train, dev, or test.

Example 1

{
  "id": "730c40a0-689d-45de-8044-76a8f6a3b1e1",
  "input": "If I go to a seminar held overseas, may I claim my flights on my tax return?",
  "expected_output": "[{\"id\": \"324513\", \"score\": 1}, {\"id\": \"351169\", \"score\": 1}, {\"id\": \"104464\", \"score\": 1}]",
  "metadata.query_id": "1857",
  "metadata.split": "train"
}

Example 2

{
  "id": "d47d4e77-7b21-410c-b568-fbd606e79e13",
  "input": "When is the right time to buy a new/emerging technology?",
  "expected_output": "[{\"id\": \"290647\", \"score\": 1}, {\"id\": \"90238\", \"score\": 1}, {\"id\": \"510872\", \"score\": 1}, {\"id\": \"157480\", \"score\": 1}]",
  "metadata.query_id": "3483",
  "metadata.split": "train"
}

References

BEIR benchmark (FiQA as a subset)

Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, Iryna Gurevych
BEIR: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models
NeurIPS 2021 (Datasets and Benchmarks Track).

Abstract (from arXiv): “Existing neural information retrieval (IR) models have often been studied in homogeneous and narrow settings, which has considerably limited insights into their out-of-distribution (OOD) generalization capabilities. To address this, and to facilitate researchers to broadly evaluate the effectiveness of their models, we introduce Benchmarking-IR (BEIR), a robust and heterogeneous evaluation benchmark for information retrieval. We leverage a careful selection of 18 publicly available datasets from diverse text retrieval tasks and domains and evaluate 10 state-of-the-art retrieval systems including lexical, sparse, dense, late-interaction and re-ranking architectures on the BEIR benchmark. Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs. In contrast, dense and sparse-retrieval models are computationally more efficient but often underperform other approaches, highlighting the considerable room for improvement in their generalization capabilities.”

• Paper: arXiv:2104.08663 — also OpenReview (NeurIPS 2021 Datasets & Benchmarks); code and data: BEIR on GitHub.

FiQA @ WWW 2018 (original challenge)

The FiQA challenge at The Web Conference (WWW) 2018 promoted research on financial opinion mining and opinion-aware question answering, including retrieval-oriented settings over community Q&A style text.

• Challenge overview: ACM DL entry — WWW’18 Open Challenge: Financial Opinion Mining and Question Answering (organizers include Maia, Handschuh, Freitas, and colleagues).
• Community site (historical): FiQA 2018.

Related resources

• BEIR FiQA mirrors on Hugging Face (raw BEIR layout), e.g. BeIR/fiqa, for corpus / queries / qrels in classic BEIR JSONL + TSV form.
• IRDS packaging: irds/beir_fiqa exposes FiQA via the ir-datasets tooling.

Citation

If you use the BEIR benchmark or FiQA through BEIR, cite the BEIR paper (BibTeX from the official repository). If you cite the original FiQA challenge, use the WWW 2018 challenge publication above.

License

Content in FiQA traces to public community Q&A sources (see FiQA documentation); Stack Exchange network content is typically shared under Creative Commons terms. This card marks cc-by-sa-4.0 as a common choice for Stack Exchange–derived text; verify against your corpus snapshot and upstream terms if compliance is strict.

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Dataset card maintained for the orgrctera/beir_fiqa Hub repository.