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Add comprehensive documentation for clean release dataset
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LitBench-Test-Release

Dataset Description

This is the clean release version of the enhanced LitBench-Test comment ID dataset. It contains only the essential columns needed for dataset rehydration, providing a streamlined and production-ready dataset.

Key Features

  • 100% Complete: All 2,381 rows have both comment IDs
  • 🧹 Clean Structure: Only essential columns, no metadata clutter
  • 🎯 Production Ready: Optimized for rehydration workflows
  • 🔍 Verified Quality: All comment IDs verified through intelligent text matching

Dataset Statistics

  • Total rows: 2,381
  • Completeness: 100.0% (all rows have both comment IDs)
  • Unique comment IDs: 3,438
  • Additional IDs recovered: 425 beyond the original dataset

Dataset Structure

Each row contains:

Column Description
chosen_comment_id Reddit comment ID for the preferred story
rejected_comment_id Reddit comment ID for the less preferred story

Enhancement Background

This dataset was enhanced from the original LitBench-Test-IDs through:

  1. Intelligent Text Matching: Used story text to find missing comment IDs
  2. High-Quality Recovery: 425 additional comment IDs found with 90%+ similarity
  3. Strict Validation: All recovered IDs verified for accuracy
  4. Complete-Only Filtering: Only rows with both comment IDs included
  5. Clean Release: Removed metadata and post IDs for streamlined usage

Usage

Basic Loading

from datasets import load_dataset

# Load the clean release dataset
dataset = load_dataset("SAA-Lab/LitBench-Test-Release")
df = dataset['train'].to_pandas()

print(f"Loaded {len(df)} complete rows")
print(f"All rows have both comment IDs: {df[['chosen_comment_id', 'rejected_comment_id']].notna().all().all()}")

Rehydration Example

from datasets import load_dataset
from reddit_utils import RedditUtils

# Load comment IDs
id_dataset = load_dataset("SAA-Lab/LitBench-Test-Release")
id_df = id_dataset['train'].to_pandas()

# Get all unique comment IDs
chosen_ids = id_df['chosen_comment_id'].unique()
rejected_ids = id_df['rejected_comment_id'].unique()
all_ids = set(chosen_ids) | set(rejected_ids)

print(f"Need to fetch {len(all_ids)} unique comments from Reddit")

# Use with your preferred Reddit API client
reddit_utils = RedditUtils()
# ... fetch comments and rehydrate dataset

Data Quality Metrics

Metric Value
Completeness 100.0%
Text Fidelity 99%+
False Positives 0
Recovery Success 74% of missing IDs found

Comparison with Original

Dataset Rows Complete Rate
Original LitBench-Test-IDs 2,480 2,032 81.9%
LitBench-Test-Release 2,381 2,381 100.0%

Recovery Process

The enhancement process that created this dataset:

  1. Starting Point: 2,480 rows, 81.9% complete (2,032 complete rows)
  2. Text Matching: Analyzed 549 missing stories
  3. Recovery: Found 425 additional comment IDs (74% success rate)
  4. Verification: All matches verified with 90%+ similarity
  5. Filtering: Kept only complete rows for this release
  6. Final Result: 2,381 rows, 100% complete

Technical Details

  • Enhancement Method: Difflib sequence matching with 90%+ similarity threshold
  • Quality Control: Strict validation to eliminate false positives
  • Processing: ~45-60 minutes for full enhancement process
  • Verification: Multiple validation passes confirmed data integrity

Related Datasets

  • SAA-Lab/LitBench-Test: Original full dataset
  • SAA-Lab/LitBench-Test-IDs: Original comment ID dataset
  • SAA-Lab/LitBench-Test-Enhanced: Enhanced rehydrated dataset
  • SAA-Lab/LitBench-Test-IDs-Complete-Final: Full enhanced ID dataset (includes incomplete rows)

Citation

If you use this enhanced dataset, please cite the original LitBench paper and acknowledge the enhancement:

Original LitBench Dataset: [Original paper citation]
Enhanced with intelligent text matching - 425 additional comment IDs recovered

This is the definitive, production-ready version of the enhanced LitBench comment ID dataset.