Copertina

Confabulation: The Surprising Value of Large Language Model Hallucinations

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Sui, Peiqi, Duede, Eamon, Wu, Sophie, So, Richard Jean
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Computation and Language Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Critical Confabulation: Can LLMs Hallucinate for Social Good?
di: Sui, Peiqi, et al.
Pubblicazione: (2025)

The Representational Status of Deep Learning Models
di: Duede, Eamon
Pubblicazione: (2023)

Deep Learning Opacity in Scientific Discovery
di: Duede, Eamon
Pubblicazione: (2022)

AI and the Decentering of Disciplinary Creativity
di: Duede, Eamon
Pubblicazione: (2025)

Why Slop Matters
di: Kommers, Cody, et al.
Pubblicazione: (2025)

Apriori Knowledge in an Era of Computational Opacity: The Role of AI in Mathematical Discovery
di: Duede, Eamon, et al.
Pubblicazione: (2024)

Can LLMs Detect Their Confabulations? Estimating Reliability in Uncertainty-Aware Language Models
di: Zhou, Tianyi, et al.
Pubblicazione: (2025)

How Large Language Models are Designed to Hallucinate
di: Ackermann, Richard, et al.
Pubblicazione: (2025)

Alleviating Hallucinations of Large Language Models through Induced Hallucinations
di: Zhang, Yue, et al.
Pubblicazione: (2023)

HAVE: Head-Adaptive Gating and ValuE Calibration for Hallucination Mitigation in Large Language Models
di: Tong, Xin, et al.
Pubblicazione: (2025)

Copy-Paste to Mitigate Large Language Model Hallucinations
di: Long, Yongchao, et al.
Pubblicazione: (2025)

First Logit Boosting: Visual Grounding Method to Mitigate Object Hallucination in Large Vision-Language Models
di: Ha, Jiwoo, et al.
Pubblicazione: (2026)

An Evolutionary Large Language Model for Hallucination Mitigation
di: Boulesnane, Abdennour, et al.
Pubblicazione: (2024)

Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models
di: Lv, Qitan, et al.
Pubblicazione: (2024)

Predictable Confabulations: Factual Recall by LLMs Scales with Model Size and Topic Frequency
di: Smith, Matthew L., et al.
Pubblicazione: (2026)

ANAH: Analytical Annotation of Hallucinations in Large Language Models
di: Ji, Ziwei, et al.
Pubblicazione: (2024)

The Impact of Negated Text on Hallucination with Large Language Models
di: Seo, Jaehyung, et al.
Pubblicazione: (2025)

Theoretical Foundations and Mitigation of Hallucination in Large Language Models
di: Gumaan, Esmail
Pubblicazione: (2025)

MALM: A Multi-Information Adapter for Large Language Models to Mitigate Hallucination
di: Jia, Ao, et al.
Pubblicazione: (2025)

HalluSAE: Detecting Hallucinations in Large Language Models via Sparse Auto-Encoders
di: Chen, Boshui, et al.
Pubblicazione: (2026)

Iter-AHMCL: Alleviate Hallucination for Large Language Model via Iterative Model-level Contrastive Learning
di: Wu, Huiwen, et al.
Pubblicazione: (2024)

Triggering Hallucinations in LLMs: A Quantitative Study of Prompt-Induced Hallucination in Large Language Models
di: Sato, Makoto
Pubblicazione: (2025)

Woodpecker: Hallucination Correction for Multimodal Large Language Models
di: Yin, Shukang, et al.
Pubblicazione: (2023)

Micro-Macro Retrieval: Reducing Long-Form Hallucination in Large Language Models
di: Feng, Yujie, et al.
Pubblicazione: (2026)

Reference-free Hallucination Detection for Large Vision-Language Models
di: Li, Qing, et al.
Pubblicazione: (2024)

Beyond Facts: Evaluating Intent Hallucination in Large Language Models
di: Hao, Yijie, et al.
Pubblicazione: (2025)

HALO: An Ontology for Representing and Categorizing Hallucinations in Large Language Models
di: Nananukul, Navapat, et al.
Pubblicazione: (2023)

Investigating Hallucinations in Pruned Large Language Models for Abstractive Summarization
di: Chrysostomou, George, et al.
Pubblicazione: (2023)

Neural Probe-Based Hallucination Detection for Large Language Models
di: Liang, Shize, et al.
Pubblicazione: (2025)

Counterfactual Probing for Hallucination Detection and Mitigation in Large Language Models
di: Feng, Yijun
Pubblicazione: (2025)

HICD: Hallucination-Inducing via Attention Dispersion for Contrastive Decoding to Mitigate Hallucinations in Large Language Models
di: Jiang, Xinyan, et al.
Pubblicazione: (2025)

Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models
di: Su, Weihang, et al.
Pubblicazione: (2024)

Attention-guided Self-reflection for Zero-shot Hallucination Detection in Large Language Models
di: Liu, Qiang, et al.
Pubblicazione: (2025)

On The Truthfulness of 'Surprisingly Likely' Responses of Large Language Models
di: Goel, Naman
Pubblicazione: (2023)

Maintaining Informative Coherence: Migrating Hallucinations in Large Language Models via Absorbing Markov Chains
di: Wu, Jiemin, et al.
Pubblicazione: (2024)

Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models
di: Dahl, Matthew, et al.
Pubblicazione: (2024)

MedHalu: Hallucinations in Responses to Healthcare Queries by Large Language Models
di: Agarwal, Vibhor, et al.
Pubblicazione: (2024)

Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models
di: Chen, Yuyan, et al.
Pubblicazione: (2024)

Delta -- Contrastive Decoding Mitigates Text Hallucinations in Large Language Models
di: Huang, Cheng Peng, et al.
Pubblicazione: (2025)

Noise Augmented Fine Tuning for Mitigating Hallucinations in Large Language Models
di: Khadangi, Afshin, et al.
Pubblicazione: (2025)