| Challenge: | Grasping the intricacies of hallucination in LLMs can be daunting, especially for those new to the field. |
| Approach: | This tutorial aims to bridge the gap between the field and the field of hallucination . it will explore the key aspects of hallucinonation, including benchmarking, detection, and mitigation techniques . |
| Outcome: | This tutorial will explore the key aspects of hallucination in LLMs . it will also explore the specific constraints and shortcomings of current approaches . |
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Nihar Ranjan Sahoo, Ashita Saxena, Kishan Maharaj, Arif A. Ahmad, Abhijit Mishra, Pushpak Bhattacharyya
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| Challenge: | a growing number of researchers are studying the hallucination issue in large language models. |
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| Challenge: | Recent studies on hallucination in large language models (LLMs) have been actively progressing in natural language processing. |
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How Much Do LLMs Hallucinate across Languages? On Realistic Multilingual Estimation of LLM Hallucination (2025.emnlp-main)
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An Audit on the Perspectives and Challenges of Hallucinations in NLP (2024.emnlp-main)
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| Challenge: | Existing methods for detecting hallucinations in large language models are limited due to their high frequency and high accuracy. |
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Enhancing Hallucination Detection via Future Context (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are widely used to generate plausible text on online platforms, without revealing the generation process. |
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HALoGEN: Fantastic LLM Hallucinations and Where to Find Them (2025.acl-long)
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| Challenge: | generative large language models produce hallucinations that are not aligned with world knowledge or input context. |
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