# De-Hallucinator: Mitigating LLM Hallucinations in Code Generation Tasks via Iterative Grounding

arXiv: https://arxiv.org/abs/2401.01701 · HTML: https://mlsanity.com/paper/2401.01701/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2024.

## Built on by (most cited first, 15 of 15)

- [Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectories](https://mlsanity.com/paper/2506.18824/) (2025)
- [Retrieval-Augmented Code Generation: A Survey with Focus on Repository-Level Approaches](https://mlsanity.com/paper/2510.04905/) (2025)
- [What to Retrieve for Effective Retrieval-Augmented Code Generation? An Empirical Study and Beyond](https://mlsanity.com/paper/2503.20589/) (2025)
- [Hallucination by Code Generation LLMs: Taxonomy, Benchmarks, Mitigation, and Challenges](https://mlsanity.com/paper/2504.20799/) (2025)
- [Assessing the Latent Automated Program Repair Capabilities of Large Language Models using Round-Trip Translation](https://mlsanity.com/paper/2401.07994/) (2024)
- [Hallucination in LLM-Based Code Generation: An Automotive Case Study](https://mlsanity.com/paper/2508.11257/) (2025)
- [Library Hallucinations in LLM-Generated Code: A Risk Analysis Grounded in Developer Queries](https://mlsanity.com/paper/2509.22202/) (2025)
- [FASE: Fast Adaptive Semantic Entropy for Code Quality](https://mlsanity.com/paper/2606.09800/) (2026)
- [Intent-Driven Storage Systems: From Low-Level Tuning to High-Level Understanding](https://mlsanity.com/paper/2510.15917/) (2025)
- [An Empirical Analysis of Static Analysis Methods for Detection and Mitigation of Code Library Hallucinations](https://mlsanity.com/paper/2604.07755/) (2026)
- [Neuro-Symbolic Agents for Hallucination-Free Requirements Reuse](https://mlsanity.com/paper/2605.01562/) (2026)
- [Task Abstention for Large Language Models in Code Generation](https://mlsanity.com/paper/2605.17029/) (2026)
- [Mitigating Errors in LLM-Generated Web API Invocations via Retrieval-Augmented Generation and Constrained Decoding](https://mlsanity.com/paper/2607.05936/) (2026)
- [Model-Driven Requirements Configuration with Three-Valued Uncertainty Scoring](https://mlsanity.com/paper/2607.26220/) (2026)
- [Better Understanding, Better Fixes? A Study of Hallucination in LLM-based Automated Program Repair](https://mlsanity.com/paper/2609.04909/) (2026)

## Abstract (arXiv)

Large language models (LLMs) trained on datasets of publicly available source code have established a new state of the art in code generation tasks. However, these models are mostly unaware of the code that exists within a specific project, preventing the models from making good use of existing APIs. Instead, LLMs often invent, or "hallucinate", non-existent APIs or produce variants of already existing code. This paper presents De-Hallucinator, a technique that grounds the predictions of an LLM through a novel combination of retrieving suitable API references and iteratively querying the model with increasingly suitable context information in the prompt. The approach exploits the observation that predictions by LLMs often resemble the desired code, but they fail to correctly refer to already existing APIs. De-Hallucinator automatically identifies project-specific API references related to the model's initial predictions and adds these references into the prompt. Unlike retrieval-augmented generation (RAG), our approach uses the initial prediction(s) by the model to iteratively retrieve increasingly suitable API references. Our evaluation applies the approach to two tasks: predicting API usages in Python and generating tests in JavaScript. We show that De-Hallucinator consistently improves the generated code across five LLMs. In particular, the approach improves the edit distance by 23.3-50.6% and the recall of correctly predicted API usages by 23.9-61.0% for code completion, and improves the number of fixed tests that initially failed because of hallucinations by 63.2%, resulting in a 15.5% increase in statement coverage for test generation.