LREC-COLING 2024 Tutorial:
Hallucination in Large Language Models
About this tutorial
In the fast-paced domain of Large Language Models (LLMs), the issue of hallucination is a prominent challenge. Despite continuous endeavors to address this concern, it remains a highly active area of research within the LLM landscape. Grasping the intricacies of this problem can be daunting, especially for those new to the field. This tutorial aims to bridge this knowledge gap by introducing the emerging realm of hallucination in LLMs. It will comprehensively explore the key aspects of hallucination, including benchmarking, detection, and mitigation techniques. Furthermore, we will delve into the specific constraints and shortcomings of current approaches, providing valuable insights to guide future research efforts for participants.
Schedule
Our tutorial will be held on May 25. Slides are available here.
| Time | Section | Presenter |
|---|---|---|
| 09:00—09:45 | Section 1: Introduction | Vipula |
| 09:45—10:30 | Section 2: Hallucination Detection | Aman |
| 10:30—11:00 | Coffee break | |
| 11:00—11:45 | Section 3: Hallucination Mitigation | Vipula |
| 11:45—12:30 | Section 4: Open Challenges | Amitava |
| 12:30—13:00 | Q & A Session |
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-COLING 2024 Tutorial: Hallucination in Large Language Models },
journal = { LREC-COLING 2024 },
year = { 2024 },
}