ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2406.05494
  4. Cited By
Investigating and Addressing Hallucinations of LLMs in Tasks Involving
  Negation

Investigating and Addressing Hallucinations of LLMs in Tasks Involving Negation

8 June 2024
Neeraj Varshney
Satyam Raj
Venkatesh Mishra
Agneet Chatterjee
Ritika Sarkar
Amir Saeidi
Chitta Baral
    LRM
ArXivPDFHTML

Papers citing "Investigating and Addressing Hallucinations of LLMs in Tasks Involving Negation"

8 / 8 papers shown
Title
Hallucination Detection in Large Language Models with Metamorphic Relations
Hallucination Detection in Large Language Models with Metamorphic Relations
Borui Yang
Md Afif Al Mamun
Jie M. Zhang
Gias Uddin
HILM
64
0
0
20 Feb 2025
From No to Know: Taxonomy, Challenges, and Opportunities for Negation Understanding in Multimodal Foundation Models
From No to Know: Taxonomy, Challenges, and Opportunities for Negation Understanding in Multimodal Foundation Models
Mayank Vatsa
Aparna Bharati
S. Mittal
Richa Singh
58
0
0
10 Feb 2025
Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction
Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction
Yooseop Lee
Suin Kim
Yohan Jo
AI4Ed
61
2
0
21 Jan 2025
On A Scale From 1 to 5: Quantifying Hallucination in Faithfulness Evaluation
On A Scale From 1 to 5: Quantifying Hallucination in Faithfulness Evaluation
Xiaonan Jing
Srinivas Billa
Danny Godbout
HILM
42
0
0
16 Oct 2024
Towards Understanding Sycophancy in Language Models
Towards Understanding Sycophancy in Language Models
Mrinank Sharma
Meg Tong
Tomasz Korbak
David Duvenaud
Amanda Askell
...
Oliver Rausch
Nicholas Schiefer
Da Yan
Miranda Zhang
Ethan Perez
213
192
0
20 Oct 2023
Can Large Language Models Truly Understand Prompts? A Case Study with
  Negated Prompts
Can Large Language Models Truly Understand Prompts? A Case Study with Negated Prompts
Joel Jang
Seonghyeon Ye
Minjoon Seo
ELM
LRM
95
64
0
26 Sep 2022
Hallucinated but Factual! Inspecting the Factuality of Hallucinations in
  Abstractive Summarization
Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization
Mengyao Cao
Yue Dong
Jackie C.K. Cheung
HILM
178
145
0
30 Aug 2021
A Token-level Reference-free Hallucination Detection Benchmark for
  Free-form Text Generation
A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation
Tianyu Liu
Yizhe Zhang
Chris Brockett
Yi Mao
Zhifang Sui
Weizhu Chen
W. Dolan
HILM
228
143
0
18 Apr 2021
1