dataset_info:
features:
- name: id
dtype: string
- name: task_type
dtype: string
- name: difficulty
dtype: string
- name: source
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: reference_answer
dtype: string
- name: source_document
dtype: string
splits:
- name: test
num_bytes: 296135
num_examples: 324
- name: dev
num_bytes: 73010
num_examples: 82
download_size: 165242
dataset_size: 369145
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: dev
path: data/dev-*
license: cc-by-4.0
task_categories:
- question-answering
- text-classification
language:
- en
tags:
- finance
- legal
- regulatory
- india
- benchmark
- llm-evaluation
- sebi
- rbi
pretty_name: IndiaFinBench
size_categories:
- n<1K
IndiaFinBench
An Evaluation Benchmark for Large Language Model Performance on Indian Financial Regulatory Text
Rajveer Singh Pall — Gyan Ganga Institute of Technology and Sciences, Jabalpur, India
Dataset Summary
IndiaFinBench is, to our knowledge, the first publicly available evaluation benchmark for assessing large language model (LLM) performance on Indian financial regulatory text. Existing financial NLP benchmarks draw exclusively from Western corpora — SEC filings, US earnings reports, and English-language financial news — leaving a significant gap in coverage of non-Western regulatory frameworks.
IndiaFinBench addresses this gap with 406 expert-annotated question-answer pairs drawn from 192 regulatory documents sourced directly from the Securities and Exchange Board of India (SEBI) and the Reserve Bank of India (RBI), spanning documents from 1992 to 2026.
The benchmark covers four task types that probe distinct reasoning capabilities required for Indian regulatory text:
| Task Type | Code | Items | Description |
|---|---|---|---|
| Regulatory Interpretation | REG | 174 | Identify correct rules, thresholds, or scope from regulatory passages |
| Numerical Reasoning | NUM | 92 | Perform arithmetic over figures embedded in regulatory text |
| Contradiction Detection | CON | 62 | Determine whether two regulatory passages contradict each other |
| Temporal Reasoning | TMP | 78 | Order regulatory events, identify which circular was operative at a given time |
| Total | 406 |
Key Results
Twelve models evaluated under zero-shot conditions on the full 406-item benchmark:
| Model | REG | NUM | CON | TMP | Overall |
|---|---|---|---|---|---|
| Gemini 2.5 Flash | 93.1% | 84.8% | 88.7% | 88.5% | 89.7% |
| Qwen3-32B | 85.1% | 77.2% | 90.3% | 92.3% | 85.5% |
| LLaMA-3.3-70B | 86.2% | 75.0% | 95.2% | 79.5% | 83.7% |
| Llama 4 Scout 17B | 86.2% | 66.3% | 98.4% | 84.6% | 83.3% |
| Kimi K2 | 89.1% | 65.2% | 91.9% | 75.6% | 81.5% |
| LLaMA-3-8B | 79.9% | 64.1% | 93.5% | 78.2% | 78.1% |
| GPT-OSS 120B | 79.9% | 59.8% | 95.2% | 76.9% | 77.1% |
| GPT-OSS 20B | 79.9% | 58.7% | 95.2% | 76.9% | 76.8% |
| Gemini 2.5 Pro | 89.7% | 48.9% | 93.5% | 64.1% | 76.1% |
| Mistral-7B | 79.9% | 66.3% | 80.6% | 74.4% | 75.9% |
| DeepSeek R1 70B | 72.4% | 69.6% | 96.8% | 70.5% | 75.1% |
| Gemma 4 E4B | 83.9% | 50.0% | 72.6% | 62.8% | 70.4% |
| Human Baseline (non-specialist) | 55.6% | 44.4% | 83.3% | 66.7% | 60.0% |
All models substantially outperform the non-specialist human baseline. Numerical reasoning is the most discriminative task (35.9 percentage-point spread across models).
Dataset Details
Source Documents
| Source | Documents | Types |
|---|---|---|
| SEBI (sebi.gov.in) | 92 | Circulars, master circulars, regulations, orders |
| RBI (rbi.org.in) | 100 | Circulars, monetary policy statements, master directions |
| Total | 192 |
Difficulty Distribution
| Difficulty | Items | Description |
|---|---|---|
| Easy | 160 (39.4%) | Single-step extraction from context |
| Medium | 182 (44.8%) | Multi-clause reasoning or calculation |
| Hard | 64 (15.8%) | Multi-instrument tracking or complex arithmetic |
Splits
The dataset is split into test (324 items, 79.8%) and dev (82 items, 20.2%).
| Split | Items |
|---|---|
| test | 324 |
| dev | 82 |
| Total | 406 |
Data Fields
| Field | Type | Description |
|---|---|---|
id |
string | Unique item identifier (e.g., REG_001, NUM_042) |
task_type |
string | One of: regulatory_interpretation, numerical_reasoning, contradiction_detection, temporal_reasoning |
difficulty |
string | One of: easy, medium, hard |
source |
string | Regulatory body: SEBI or RBI |
context |
string | Regulatory passage(s) provided to the model (80–500 words). For contradiction detection items, contains Passage A and Passage B separated by a delimiter |
question |
string | The question to be answered from the context |
reference_answer |
string | Gold-standard reference answer |
source_document |
string | Filename of the source regulatory document |
Annotation and Validation
All 406 QA pairs were authored by a domain expert in Indian financial regulation. Every item was individually reviewed to ensure:
- The answer is unambiguously derivable from the provided context
- The question has exactly one correct answer
- The context is sufficient without external knowledge
Model-based secondary validation (LLaMA-3.3-70B, 150-item subset): 90.7% agreement, κ = 0.918 on contradiction detection.
Human inter-annotator agreement (second human annotator, 60-item sample): 76.7% overall agreement, κ = 0.611 for contradiction detection (substantial agreement per Landis & Koch 1977).
Evaluation Protocol
Models are evaluated under zero-shot, context-only conditions. The scoring pipeline applies four stages in sequence:
- Exact match after case-normalisation and punctuation stripping
- Fuzzy token match using RapidFuzz
token_set_ratio ≥ 0.72 - Numerical extraction match for items where extracted number sets agree
- Yes/No match for contradiction detection (leading word comparison)
Full evaluation code and all model predictions are available at: https://github.com/rajveerpall/IndiaFinBench
Limitations
- All evaluation is zero-shot; few-shot or chain-of-thought prompting may improve performance
- The benchmark does not currently cover Hindi–English code-switched regulatory text
- Coverage is limited to SEBI and RBI; extension to IRDAI, PFRDA, and commodity regulation is planned
- The benchmark evaluates short extractive responses, not longer-form regulatory reasoning or document summarisation
- The dataset is a snapshot of documents as of early 2026; regulatory frameworks evolve continuously
Citation
If you use IndiaFinBench in your research, please cite:
@article{pall2025indiafinbench,
title={IndiaFinBench: An Evaluation Benchmark for Large Language Model Performance on Indian Financial Regulatory Text},
author={Pall, Rajveer Singh},
journal={arXiv preprint},
year={2025},
url={https://github.com/rajveerpall/IndiaFinBench}
}
License
This dataset is released under CC BY 4.0. All source documents are publicly available from sebi.gov.in and rbi.org.in and carry no copyright restrictions on research use.
Contact
Rajveer Singh Pall — rajveer.singhpall.cb23@ggits.net
Gyan Ganga Institute of Technology and Sciences, Jabalpur, India