JamshidJDMY/HintRank_AnswerAware

WikiHint: A Human-Annotated Dataset for Hint Ranking and Generation

WikiHint is a human-annotated dataset designed for automatic hint generation and ranking for factoid questions. This dataset, based on Wikipedia, contains 5,000 hints for 1,000 questions and supports research in hint evaluation, ranking, and generation.

🗂 Overview

• 1,000 questions with 5,000 manually created hints.
• Hints ranked by human annotators based on helpfulness.
• Evaluated using LLMs (LLaMA, GPT-4) and human performance studies.
• Supports hint ranking and automatic hint evaluation.

🔬 Research Contributions

✅ First human-annotated dataset for hint generation and ranking.
✅ HintRank: A lightweight method for automatic hint ranking.
✅ Human study evaluating hint effectiveness in helping users answer questions.
✅ Fine-tuning open-source LLMs (LLaMA-3.1, GPT-4) for hint generation.

📈 Key Insights

• Answer-aware hints improve hint effectiveness.
• Finetuned LLaMA models generate better hints than vanilla models.
• Shorter hints tend to be more effective than longer ones.
• Human-generated hints outperform LLM-generated hints in clarity and ranking.

🚀 Getting Started

1️⃣ Clone the Repository

git clone https://github.com/DataScienceUIBK/WikiHint.git
cd WikiHint

2️⃣ Load the Dataset

import json

with open("./WikiHint/training.json", "r") as f:
    training_data = json.load(f)

with open("./WikiHint/test.json", "r") as f:
    test_data = json.load(f)

print(f"Training set: {len(training_data)} questions")
print(f"Test set: {len(test_data)} questions")

🏆 HintRank: A Lightweight Hint Ranking Method

HintRank is an automatic ranking method for hints using BERT-based models. It operates on pairwise comparisons, determining the relative helpfulness of hints.

✨ Features:

✔ Lightweight: Runs locally without requiring massive computational resources.
✔ LLM-free evaluation: Works without relying on large-scale generative models.
✔ Human-aligned ranking: Strong correlation with human-assigned hint rankings.

🔍 How It Works:

1. Concatenates question & two hints → Converts them into BERT-compatible format.
2. Computes hint quality → Determines which hint is more useful.
3. Generates hint rankings → Assigns ranks based on pairwise comparisons.

📌 Using HintRank for Hint Ranking

The HintRank module is designed to automatically rank hints based on their helpfulness using BERT-based models.

🚀 Run the HintRank Demo in Google Colab

You can easily try HintRank in your browser via Google Colab, with no local installation required. Simply launch the Colab notebook to explore HintRank interactively.

1️⃣ Install Dependencies

If running locally, ensure you have the required dependencies installed:

pip install transformers torch numpy scipy

2️⃣ Import and Initialize HintRank

Navigate to the HintRank directory and import the hint_rank module:

from HintRank.hint_rank import HintRank

# Initialize the HintRank model
ranker = HintRank()

3️⃣ Rank Hints for a Given Question

question = "What is the capital of Austria?"
answer = "Vienna"
hints = [
    "Mozart and Beethoven once lived here.",
    "It is a big city in Europe.",
    "Austria’s largest city."
]

# Pairwise Comparison Example
better_hint_answer_aware = ranker.pairwise_compare(question, hints[1], hints[2], answer)
better_hint_answer_agnostic = ranker.pairwise_compare(question, hints[0], hints[1])

print(f"Answer-Aware: Hint {2 if better_hint_answer_aware == 1 else 3} is better than Hint {3 if better_hint_answer_aware == 0 else 2}.")
print(f"Answer-Agnostic: Hint {1 if better_hint_answer_agnostic == 1 else 2} is better than Hint {2 if better_hint_answer_agnostic == 0 else 1}.")

4️⃣ Listwise Hint Ranking

You can also rank multiple hints at once:

print("\nAnswer-Aware Ranked Hints:")
ranked_hints_answer_aware = ranker.listwise_compare(question, hints, answer)
for i, (hint, _) in enumerate(ranked_hints_answer_aware):
    print(f"Rank {i + 1}: {hint}")

print("\nAnswer-Agnostic Ranked Hints:")
ranked_hints_answer_agnostic = ranker.listwise_compare(question, hints)
for i, (hint, _) in enumerate(ranked_hints_answer_agnostic):
    print(f"Rank {i + 1}: {hint}")

📌 Expected Output

Pairwise Hint Comparison
    Answer-Aware: Hint 3 is better than Hint 2.
    Answer-Agnostic: Hint 2 is better than Hint 1.

Listwise Hint Ranking
    Answer-Aware Ranked Hints:
        Rank 1: Austria’s largest city.
        Rank 2: Mozart and Beethoven once lived here.
        Rank 3: It is a big city in Europe.

    Answer-Agnostic Ranked Hints:
        Rank 1: It is a big city in Europe.
        Rank 2: Austria’s largest city.
        Rank 3: Mozart and Beethoven once lived here.

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📊 🆚 WikiHint vs. TriviaHG Dataset Comparison

The table below compares WikiHint with TriviaHG, the largest previous dataset for hint generation. WikiHint has better convergence, shorter hints, and higher-quality hints based on multiple evaluation metrics.

Dataset	Subset	Relevance	Readability	Convergence	Familiarity	Length	Answer Leakage (Avg.)	Answer Leakage (Max.)
TriviaHG	Entire	0.95	0.71	0.57	0.77	20.82	0.23	0.44
WikiHint	Entire	0.98	0.72	0.73	0.75	17.82	0.24	0.49
TriviaHG	Train	0.95	0.73	0.57	0.75	21.19	0.22	0.44
WikiHint	Train	0.98	0.71	0.74	0.76	17.77	0.24	0.49
TriviaHG	Test	0.95	0.73	0.60	0.77	20.97	0.23	0.44
WikiHint	Test	0.98	0.83	0.72	0.73	18.32	0.24	0.47

📌 Key Findings:

• WikiHint outperforms TriviaHG in convergence, meaning its hints help users arrive at answers more effectively.
• WikiHint’s hints are shorter, leading to more concise and effective guidance.

📊🤖 Evaluation of Generated Hints

This table presents an evaluation of generated hints across different LLMs (LLaMA-3.1, GPT-4) based on Relevance, Readability, Convergence, Familiarity, Hint Length, and Answer Leakage. It provides insights into how finetuning (FT) and answer-awareness (wA) affect hint quality.

Model	Config	Use Answer?	Rel	Read	Conv (LLaMA-8B)	Conv (LLaMA-70B)	Fam	Len	AnsLkg (Avg.)	AnsLkg (Max.)
GPT-4	Vanilla	✅	0.91	1.00	0.14	0.48	0.84	26.36	0.23	0.51
GPT-4	Vanilla	❌	0.92	1.10	0.12	0.47	0.81	26.93	0.24	0.52
LLaMA-3.1-405b	Vanilla	✅	0.94	1.49	0.11	0.47	0.76	41.81	0.23	0.50
LLaMA-3.1-405b	Vanilla	❌	0.92	1.53	0.10	0.45	0.78	50.91	0.23	0.50
LLaMA-3.1-70b	FTwA	✅	0.88	1.50	0.09	0.42	0.84	43.69	0.22	0.48
LLaMA-3.1-70b	Vanilla	✅	0.86	1.53	0.05	0.42	0.80	45.51	0.23	0.50
LLaMA-3.1-70b	FTwoA	❌	0.86	1.50	0.08	0.38	0.80	51.07	0.22	0.51
LLaMA-3.1-70b	Vanilla	❌	0.87	1.56	0.06	0.38	0.76	53.24	0.22	0.50
LLaMA-3.1-8b	FTwA	✅	0.78	1.63	0.05	0.37	0.79	50.33	0.22	0.52
LLaMA-3.1-8b	Vanilla	✅	0.81	1.72	0.05	0.32	0.80	54.38	0.22	0.50
LLaMA-3.1-8b	FTwoA	❌	0.76	1.70	0.03	0.32	0.80	55.02	0.22	0.51
LLaMA-3.1-8b	Vanilla	❌	0.78	1.76	0.04	0.30	0.83	52.99	0.22	0.50

📌 Key Takeaways:

• Relevance: Larger models (405b, 70b) provide better hints compared to smaller (8b) models.
• Readability: GPT-4 produces the most readable hints.
• Convergence: Answer-aware hints (wA) help LLMs generate better hints.
• Familiarity: Larger models generate more familiar hints based on common knowledge.
• Hint Length: Finetuned models (FTwA, FTwoA) generate shorter and better hints.

📜 License

This project is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to use, share, and adapt the dataset with proper attribution.

📑 Citation

If you find this work useful, please cite 📜our paper:

Mozafari, J., Gerhold, F., & Jatowt, A. (2024). WikiHint: A Human-Annotated Dataset for Hint Ranking and Generation. arXiv preprint arXiv:2412.01626.

📄 BibTeX:

@article{mozafari2025wikihinthumanannotateddatasethint,
      title={WikiHint: A Human-Annotated Dataset for Hint Ranking and Generation}, 
      author={Jamshid Mozafari and Florian Gerhold and Adam Jatowt},
      year={2025},
      eprint={2412.01626},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      doi={10.48550/arXiv.2412.01626}, 
}

🙏Acknowledgments

Thanks to our contributors and the University of Innsbruck for supporting this project.