Datasets:

nvidia
/

IFEval-Hi

IFEval-Hi is a Hindi language adaptation of the IFEval (Instruction Following Evaluation) benchmark, designed to evaluate the instruction-following capabilities of Large Language Models (LLMs) in Hindi. This implementation maintains the core evaluation methodology of the original English IFEval while incorporating language-specific modifications to ensure accurate and fair assessment of Hindi language models.

Getting Started

You have two options to use this evaluation framework:

Option 1: Use the Ready-to-Use Fork (Recommended)
- Fork or clone the repository directly from: https://github.com/anushaknvidia/lm-evaluation-harness
- This fork already includes all the Hindi-specific configurations and modifications
- Skip to Step 3: Run Evaluation
Option 2: Manual Setup
- Follow the step-by-step instructions below to set up IFEval-Hi from scratch
- This is useful if you want to customize or understand the implementation details

Setup and Usage

Step 1: Create Task Configuration

Navigate to the lm-evaluation-harness tasks directory:

https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/ifeval

Create a copy of the English IFEval directory and rename it to ifevalhi
Rename the task file in the copied folder to ifevalhi.yaml for Hindi-specific configuration

Step 2: Configure Parameters

Update the ifevalhi.yaml configuration file with the following Hindi-specific parameters:

# Dataset Configuration
dataset_path: nvidia/IFEval-Hi

# Generation Parameters
max_gen_toks: 4096  # Increased from 1280 to accommodate Hindi morphology

# Additional Hindi-specific settings
# (Include language-specific preprocessing and normalization settings as needed)

Key Configuration Changes:

dataset_path: Changed from google/IFEval to nvidia/IFEval-Hi
max_gen_toks: Increased to 4096 tokens to handle Hindi's linguistic complexity

Step 3: Run Evaluation

Execute the evaluation using the lm-eval-harness framework with the Hindi task configuration:

# Basic evaluation command add other arguments as per lm-eval-harness repo
lm-eval --model hf \
        --model_args pretrained=<model_name_or_path> \
        --tasks ifevalhi \
        --batch_size auto \
        --output_path ./results/

Expected Output

The evaluation will generate results including:

prompt_level_strict_acc: Primary accuracy metric
normalised_acc: Normalized accuracy with text preprocessing

Key Differences from English IFEval

1. Configuration Parameters

Maximum Generation Token Limit

English IFEval: 1,280 tokens
IFEval-Hindi: 4,096 tokens

The increased token limit accommodates the morphological and syntactic properties of Hindi text, which often requires more tokens to express equivalent content compared to English.

2. Language-Specific Processing

Tokenization and Segmentation

English Implementation: Uses standard tokenizer for sentence and word segmentation
IFEval-Hi: Incorporates Hindi-specific punctuation handling, including:
- Sentence delimitation using the vertical bar (|) character
- Custom punctuation rules tailored to Hindi text structure

3. Constrained Response Categories

IFEval-Hi expands the constrained response category with Hindi-specific response patterns:

- मेरा जवाब हाँ है (My answer is yes)
- मेरा जवाब नहीं है (My answer is no)
- मेरा जवाब शायद है (My answer is maybe)
- हाँ (Yes)
- नहीं (No)
- शायद (Maybe)

These additions ensure fair evaluation for Hindi responses and align with natural Hindi language usage patterns.

4. Text Normalization

IFEval-Hindi implements comprehensive normalization procedures for model-generated Hindi text and evaluation parameters:

Character Normalization

Consonant Unification: Characters like क़ and क are unified to maintain consistency
Diacritic Removal: Diacritical marks such as "ँ" (chandrabindu) are stripped
Symbol Cleanup: Redundant symbols and spacing irregularities are removed
Orthographic Standardization: Variations in Hindi script representation are normalized

These normalization steps ensure consistent processing across input prompts and model-generated outputs, reducing evaluation bias from orthographic variations.

5. Validation Logic Updates

Letter Frequency Checker

English IFEval: Includes English alphabet-only validation logic
IFEval-Hi: English alphabet validation has been deprecated and removed from instructions.py to align with Hindi-specific evaluation requirements

This modification ensures that character-level constraints are appropriately evaluated for the Devanagari script used in Hindi.

IFEval-Hi follows the same execution pipeline as the English variant within the lm-eval-harness repository

Pipeline Structure:
1. Load dataset from nvidia/IFEval-Hi
2. Generate model responses with Hindi-specific configurations
3. Apply Hindi text normalization
4. Evaluate instruction-following accuracy
5. Report metrics

Both implementations utilize the same core Python utility modules, ensuring consistency in evaluation methodology while supporting language-specific adaptations. Please find the fork to the evaluation repo with the above changes here https://github.com/anushaknvidia/lm-evaluation-harness