# HILL: A Hallucination Identifier for Large Language Models

arXiv: https://arxiv.org/abs/2403.06710 · HTML: https://mlsanity.com/paper/2403.06710/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2024.

## Built on by (most cited first, 11 of 11)

- [LLM Post-Training: A Deep Dive into Reasoning Large Language Models](https://mlsanity.com/paper/2502.21321/) (2025)
- [How Far Are We From AGI: Are LLMs All We Need?](https://mlsanity.com/paper/2405.10313/) (2024)
- [Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey](https://mlsanity.com/paper/2411.10478/) (2024)
- [Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review](https://mlsanity.com/paper/2501.12557/) (2025)
- [RODS: Robust Optimization Inspired Diffusion Sampling for Detecting and Reducing Hallucination in Generative Models](https://mlsanity.com/paper/2507.12201/) (2025)
- [ChatGPT and U(X): A Rapid Review on Measuring the User Experience](https://mlsanity.com/paper/2503.15808/) (2025)
- [MALM: A Multi-Information Adapter for Large Language Models to Mitigate Hallucination](https://mlsanity.com/paper/2506.12483/) (2025)
- [Learning from AVA: Early Lessons from a Curated and Trustworthy Generative AI for Policy and Development Research](https://mlsanity.com/paper/2604.17843/) (2026)
- [Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agents](https://mlsanity.com/paper/2506.20062/) (2025)
- [A Concise Review of Hallucinations in LLMs and their Mitigation](https://mlsanity.com/paper/2512.02527/) (2025)
- [WordCraft: Scaffolding the Keyword Method for L2 Vocabulary Learning with Multimodal LLMs](https://mlsanity.com/paper/2602.00762/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are prone to hallucinations, i.e., nonsensical, unfaithful, and undesirable text. Users tend to overrely on LLMs and corresponding hallucinations which can lead to misinterpretations and errors. To tackle the problem of overreliance, we propose HILL, the "Hallucination Identifier for Large Language Models". First, we identified design features for HILL with a Wizard of Oz approach with nine participants. Subsequently, we implemented HILL based on the identified design features and evaluated HILL's interface design by surveying 17 participants. Further, we investigated HILL's functionality to identify hallucinations based on an existing question-answering dataset and five user interviews. We find that HILL can correctly identify and highlight hallucinations in LLM responses which enables users to handle LLM responses with more caution. With that, we propose an easy-to-implement adaptation to existing LLMs and demonstrate the relevance of user-centered designs of AI artifacts.