@inproceedings{rawte-etal-2024-tutorial,
title = "Tutorial Proposal: Hallucination in Large Language Models",
author = "Rawte, Vipula and
Chadha, Aman and
Sheth, Amit and
Das, Amitava",
editor = "Klinger, Roman and
Okazaki, Naozaki and
Calzolari, Nicoletta and
Kan, Min-Yen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024): Tutorial Summaries",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-tutorials.11",
pages = "68--72",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Tutorial Proposal: Hallucination in Large Language Models
%A Rawte, Vipula
%A Chadha, Aman
%A Sheth, Amit
%A Das, Amitava
%Y Klinger, Roman
%Y Okazaki, Naozaki
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024): Tutorial Summaries
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F rawte-etal-2024-tutorial
%X 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.
%U https://aclanthology.org/2024.lrec-tutorials.11
%P 68-72
Markdown (Informal)
[Tutorial Proposal: Hallucination in Large Language Models](https://aclanthology.org/2024.lrec-tutorials.11) (Rawte et al., LREC 2024)
ACL
- Vipula Rawte, Aman Chadha, Amit Sheth, and Amitava Das. 2024. Tutorial Proposal: Hallucination in Large Language Models. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024): Tutorial Summaries, pages 68–72, Torino, Italia. ELRA and ICCL.