Tootfinder

Sommige rechtse partijen willen dat we net als Trump/VS worden terwijl ik het een groot goed vind dat mensen verschrikkelijke dingen mogen zeggen die mij helemaal niet bevallen en tegen al mijn normen ingaan. Onze wet bepaalt waar de grens ligt voor die vrijheid. #JA21

Halsema na kritiek op Bob Vylan: "Ik val niet als een kleine dictator cultuurhuizen binnen"
Halsema na kritiek op Bob Vylan: "Ik val niet als een kleine dictator cultuurhuizen binnen"

Diving into Mitigating Hallucinations from a Vision Perspective for Large Vision-Language Models
Weihang Wang, Xinhao Li, Ziyue Wang, Yan Pang, Jielei Zhang, Peiyi Li, Qiang Zhang, Longwen Gao
https://arxiv.org/abs/2509.13836

Diving into Mitigating Hallucinations from a Vision Perspective for Large Vision-Language Models
Object hallucination in Large Vision-Language Models (LVLMs) significantly impedes their real-world applicability. As the primary component for accurately interpreting visual information, the choice of visual encoder is pivotal. We hypothesize that the diverse training paradigms employed by different visual encoders instill them with distinct inductive biases, which leads to their diverse hallucination performances. Existing benchmarks typically focus on coarse-grained hallucination detection a…

#SteadyCommunityContent
Wieso geben #Sprachmodelle oft selbstbewusst falsche Antworten?
Wenn Raten besser bewertet wird als Ehrlichkeit, droht Vertrauensverlust. Wie lässt sich das ändern, und warum ist „Ich weiß es nicht“ bisher ein Problem für KI-Modelle? Eine neue…

Lassen sich Halluzinationen bei Sprachmodellen vermeiden?
Warum halluzinieren große Sprachmodelle? Eine neue Studie von OpenAI liefert überraschende Erklärungen und Lösungsvorschläge.

EGOILLUSION: Benchmarking Hallucinations in Egocentric Video Understanding
Ashish Seth, Utkarsh Tyagi, Ramaneswaran Selvakumar, Nishit Anand, Sonal Kumar, Sreyan Ghosh, Ramani Duraiswami, Chirag Agarwal, Dinesh Manocha
https://arxiv.org/abs/2508.12687

EGOILLUSION: Benchmarking Hallucinations in Egocentric Video Understanding
Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in complex multimodal tasks. While MLLMs excel at visual perception and reasoning in third-person and egocentric videos, they are prone to hallucinations, generating coherent yet inaccurate responses. We present EgoIllusion, a first benchmark to evaluate MLLM hallucinations in egocentric videos. EgoIllusion comprises 1,400 videos paired with 8,000 human-annotated open and closed-ended questions designed to trigger…

Geometric Uncertainty for Detecting and Correcting Hallucinations in LLMs
Edward Phillips, Sean Wu, Soheila Molaei, Danielle Belgrave, Anshul Thakur, David Clifton
https://arxiv.org/abs/2509.13813

Geometric Uncertainty for Detecting and Correcting Hallucinations in LLMs
Large language models demonstrate impressive results across diverse tasks but are still known to hallucinate, generating linguistically plausible but incorrect answers to questions. Uncertainty quantification has been proposed as a strategy for hallucination detection, but no existing black-box approach provides estimates for both global and local uncertainty. The former attributes uncertainty to a batch of responses, while the latter attributes uncertainty to individual responses. Current loca…

"In theory, AI model makers could eliminate hallucinations by using a dataset that contains no errors."
I think someone has fundamentally misunderstood the technology. Developing a model using a 100% correct training dataset does not mean that the resulting AI will be able to correctly answer questions that were not in the training data.
Over-fitting is a thing.

OpenAI says models are programmed to make stuff up instead of admitting ignorance
: Even a wrong answer is right some of the time

Hallucination in LLM-Based Code Generation: An Automotive Case Study
Marc Pavel, Nenad Petrovic, Lukasz Mazur, Vahid Zolfaghari, Fengjunjie Pan, Alois Knoll
https://arxiv.org/abs/2508.11257

Hallucination in LLM-Based Code Generation: An Automotive Case Study
Large Language Models (LLMs) have shown significant potential in automating code generation tasks offering new opportunities across software engineering domains. However, their practical application remains limited due to hallucinations - outputs that appear plausible but are factually incorrect, unverifiable or nonsensical. This paper investigates hallucination phenomena in the context of code generation with a specific focus on the automotive domain. A case study is presented that evaluates m…

Librarians Are Being Asked to Find AI-Hallucinated Books https://www.404media.co/librarians-are-being-asked-to-find-ai-hallucinated-books/

I watched 3 episodes of Hellsing Ultimate and turned it off after Millennium was introduced.
I really thought it was going to take a different direction.
#anime #hellsing #netflix

DSCC-HS: A Dynamic Self-Reinforcing Framework for Hallucination Suppression in Large Language Models
Xiao Zheng
https://arxiv.org/abs/2509.13702 https://ar…

DSCC-HS: A Dynamic Self-Reinforcing Framework for Hallucination Suppression in Large Language Models
Large Language Model (LLM) hallucination is a significant barrier to their reliable deployment. Current methods like Retrieval-Augmented Generation (RAG) are often reactive. We introduce **Dynamic Self-reinforcing Calibration for Hallucination Suppression (DSCC-HS)**, a novel, proactive framework that intervenes during autoregressive decoding. Inspired by dual-process cognitive theory, DSCC-HS uses a compact proxy model, trained in adversarial roles as a Factual Alignment Proxy (FAP) and a Hall…