LREC 2026 Tutorial:
Hallucination in Large Foundation Models
About this tutorial
Large Foundation Models (LFMs) have advanced significantly in generating human-like text, but their tendency to hallucinate - producing incorrect or fabricated information - remains a critical challenge. This tutorial offers an in-depth look at hallucinations in LFMs, introducing key concepts and issues within this area. We will explore various types of hallucinations, including Factual Mirage and Silver Lining, and present cutting-edge methods for benchmarking, detection, and mitigation. Understanding hallucination is especially important in a multimodal context, as Vision-Language Models (VLMs) can worsen the problem by combining hallucinated text with misleading images or video. We'll also address code hallucination in LFMs. The tutorial provides practical techniques for minimizing hallucinations through both black-box and gray-box approaches. Tailored for researchers and professionals in generative AI, this session bridges the gap between emerging research and practical solutions, offering participants valuable insights and tools to improve the factual accuracy of LFM outputs. Attendees will gain a deeper understanding of the complexities around LFM hallucination and learn strategies to drive future advancements in the field.
Reading List
- A Survey of Hallucination in Large Foundation Models (Rawte et al., 2023)
- Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Models (Shi et al., 2023)
- Cognitive Mirage: A Review of Hallucinations in Large Language Models (Ye et al., 2023)
- Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization (Cao et al., 2023)
- The Troubling Emergence of Hallucination in Large Language Models - An Extensive Definition, Quantification, and Prescriptive Remediations (Rawte et al., 2023)
- CHAIN-OF-VERIFICATION REDUCES HALLUCINATION IN LARGE LANGUAGE MODELS (Dhuliawala et al., 2023)
- Halo: Estimation and reduction of hallucinations in open-source weak large language models (Elaraby et al., 2023)
- When Do Pre-Training Biases Propagate to Downstream Tasks? A Case Study in Text Summarization (Ladhak et al., 2023)
- HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models (Li et al., 2023)
- A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation (Varshney et al., 2023)
- How Language Model Hallucinations Can Snowball (Zhang et al., 2023)
- SELFCHECKGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models (Manakaul et al., 2023)
- SELF-CONTRADICTORY HALLUCINATIONS OF LLMS: EVALUATION, DETECTION AND MITIGATION (Mündler et al., 2023)
BibTeX
@article{ hallucination-llm-tutorial,
author = { Rawte, Vipula and Chadha, Aman and Sheth, Amit and Das, Amitava },
title = { LREC 2026 Tutorial: Hallucination in Large Foundation Models },
journal = { LREC 2026 },
year = { 2026 },
}