# HintEval: An Open-Source Python Toolkit for Hint Generation and Hint Evaluation

arXiv: https://arxiv.org/abs/2502.00857 · HTML: https://mlsanity.com/paper/2502.00857/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 2 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [Large Language Models Meet NLP: A Survey](https://mlsanity.com/paper/2405.12819/) (2024)
- [A Survey of Automated Programming Hint Generation -- The HINTS Framework](https://mlsanity.com/paper/1908.11566/) (2019)
- [A Practical Toolkit for Multilingual Question and Answer Generation](https://mlsanity.com/paper/2305.17416/) (2023)
- [TriviaHG: A Dataset for Automatic Hint Generation from Factoid Questions](https://mlsanity.com/paper/2403.18426/) (2024)
- [Navigating the Landscape of Hint Generation Research: From the Past to the Future](https://mlsanity.com/paper/2404.04728/) (2024)
- [WikiHint: A Human-Annotated Dataset for Hint Ranking and Generation](https://mlsanity.com/paper/2412.01626/) (2024)

## Built on by (most cited first, 3 of 3)

- [LeanTutor: Towards a Verified AI Mathematical Proof Tutor](https://mlsanity.com/paper/2506.08321/) (2025)
- [Designing and Evaluating Chain-of-Hints for Scientific Question Answering](https://mlsanity.com/paper/2510.21087/) (2025)
- [LeanTutor: Towards a Verified AI Mathematical Proof Tutor](https://mlsanity.com/paper/2601.17473/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) increasingly provide direct answers to user questions, raising concerns about reduced engagement in critical thinking and problem-solving. Hint generation offers an alternative by guiding users toward answers without revealing them, while hint evaluation assesses the quality of such guidance. Research in this area is hindered by fragmented datasets, inconsistent annotation formats, and evaluation tools that are often dataset-specific or unavailable. To address these challenges, we introduce HintEval, an open-source Python library for unified hint generation and evaluation. HintEval standardizes access to diverse hint datasets, supports answer-aware and answer-agnostic generation methods, and implements multiple evaluation metrics within a shared data model. The toolkit enables reproducible experimentation, cross-dataset analysis, and multi-dimensional evaluation with minimal engineering effort. We further demonstrate its utility through human studies in which participants assess generated hints and use them to answer questions, showing that hints can effectively support users in reaching correct answers. HintEval is accompanied by comprehensive documentation, an executable Google Colab notebook for rapid experimentation, and a demonstration video. By promoting consistent evaluation practices and lowering barriers to entry, it facilitates systematic research on hint-based question answering (QA) in NLP and IR.