Abstract Participants in the SemEval-2024 Task 6 were tasked with executing binary
classification aimed at discerning instances of fluent overgeneration hallucinations across …

This paper describes our submission for SemEval-2024 Task 6: SHROOM, a Shared-task on
Hallucinations and Related Observable Overgeneration Mistakes. We propose four groups …

In Natural Language Generation (NLG), contemporary Large Language Models (LLMs) face
several challenges, such as generating fluent yet inaccurate outputs and reliance on fluency …

ELOQUENT is a set of shared tasks for evaluating the quality and usefulness of generative
language models. ELOQUENT aims to apply high-level quality criteria, grounded in …

Semantic hallucinations in neural language gen-eration systems pose a significant
challenge tothe reliability and accuracy of natural languageprocessing applications. Current …

This paper discusses challenges in Natural Language Generation (NLG), specifically
addressing neural networks producing output that is fluent but incorrect, leading to …

The inclination of large language models (LLMs) to produce mistaken assertions, known as
hallucinations, can be problematic. These hallucinations could potentially be harmful since …

This paper presents the Hallucination Recognition Model for New Experiment Evaluation
(HaRMoNEE) team's winning (# 1) and# 10 submissions for SemEval-2024 Task 6: Shared …

The advancement of large language models (LLMs), their ability to produce eloquent and
fluent content, and their vast knowledge have resulted in their usage in various tasks and …

In these working notes we describe the UMUTeam's participation in SemEval-2024 shared
task 6, which aims at detecting grammatically correct output of Natural Language Generation …