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Prompt-Response Semantic Divergence Metrics for Faithfulness Hallucination and Misalignment Detection in Large Language Models
Igor Halperin
https://arxiv.org/abs/2508.10192 htt…

Prompt-Response Semantic Divergence Metrics for Faithfulness Hallucination and Misalignment Detection in Large Language Models
The proliferation of Large Language Models (LLMs) is challenged by hallucinations, critical failure modes where models generate non-factual, nonsensical or unfaithful text. This paper introduces Semantic Divergence Metrics (SDM), a novel lightweight framework for detecting Faithfulness Hallucinations -- events of severe deviations of LLMs responses from input contexts. We focus on a specific implementation of these LLM errors, {confabulations, defined as responses that are arbitrary and semanti…

"In short, the OpenAI paper inadvertently highlights an uncomfortable truth: the business incentives driving consumer AI development remain fundamentally misaligned with reducing hallucinations. Until these incentives change, hallucinations will persist."
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KI: Drohnenhersteller Helsing hilft beim Entwickeln von Robo-Panzern
Helsing kooperiert mit Arx Robotics, um Waffensysteme mit KI zu vernetzen. Autonome Panzer sollen Gelände auskundschaften oder Munition und Verwundete bergen.

Trauer-Hysterie um Rechtsradikalen: Warum Charlie Kirk kein Märtyrer ist - Nach seinem gewaltsamen Tod wird der rechtsradikale Aktivist Charlie Kirk zum „Heiligen“ und „Märtyrer“ erklärt. Seine Botschaften und Methoden aber sind dessen unwürdig.
Philipp Greifenstein #USA#Trump #Demokratie

Warum Charlie Kirk kein Märtyrer ist | Die Eule
Foto: Gage Skidmore (CC BY-SA 2.0). Nach seinem gewaltsamen Tod wird der rechtsradikale Aktivist Charlie Kirk zum „Heiligen“ und „Märtyrer“ erklärt. Seine Botschaften und Methoden aber sind dessen unwürdig.

Hallucination vs interpretation: rethinking accuracy and precision in AI-assisted data extraction for knowledge synthesis
Xi Long, Christy Boscardin, Lauren A. Maggio, Joseph A. Costello, Ralph Gonzales, Rasmyah Hammoudeh, Ki Lai, Yoon Soo Park, Brian C. Gin
https://arxiv.org/abs/2508.09458

Hallucination vs interpretation: rethinking accuracy and precision in AI-assisted data extraction for knowledge synthesis
Knowledge syntheses (literature reviews) are essential to health professions education (HPE), consolidating findings to advance theory and practice. However, they are labor-intensive, especially during data extraction. Artificial Intelligence (AI)-assisted extraction promises efficiency but raises concerns about accuracy, making it critical to distinguish AI 'hallucinations' (fabricated content) from legitimate interpretive differences. We developed an extraction platform using large language m…

Liito-orava on taigametsien tulevaisuuden avainlaji. Tuore geneettinen tutkimus paljastaa yllättäviä piirteitä liito-oravan evoluutiosta sekä vakavia huolia lajin suojelun kannalta. Kaukoidässä saattaa asustaa oma alalaji. https://www.helsinki.fi/fi/uutiset/evoluut

Liito-orava on taigametsien tulevaisuuden avainlaji | Helsingin yliopisto
Tuore geneettinen tutkimus paljastaa yllättäviä piirteitä liito-oravan evoluutiosta sekä vakavia huolia lajin suojelun kannalta. Kaukoidässä saattaa asustaa oma alalaji.

Hallucinations in Code Change to Natural Language Generation: Prevalence and Evaluation of Detection Metrics
Chunhua Liu, Hong Yi Lin, Patanamon Thongtanunam
https://arxiv.org/abs/2508.08661

Hallucinations in Code Change to Natural Language Generation: Prevalence and Evaluation of Detection Metrics
Language models have shown strong capabilities across a wide range of tasks in software engineering, such as code generation, yet they suffer from hallucinations. While hallucinations have been studied independently in natural language and code generation, their occurrence in tasks involving code changes which have a structurally complex and context-dependent format of code remains largely unexplored. This paper presents the first comprehensive analysis of hallucinations in two critical tasks i…

The Curious Case of Factuality Finetuning: Models' Internal Beliefs Can Improve Factuality
Benjamin Newman, Abhilasha Ravichander, Jaehun Jung, Rui Xin, Hamish Ivison, Yegor Kuznetsov, Pang Wei Koh, Yejin Choi
https://arxiv.org/abs/2507.08371

The Curious Case of Factuality Finetuning: Models' Internal Beliefs Can Improve Factuality
Language models are prone to hallucination - generating text that is factually incorrect. Finetuning models on high-quality factual information can potentially reduce hallucination, but concerns remain; obtaining factual gold data can be expensive and training on correct but unfamiliar data may potentially lead to even more downstream hallucination. What data should practitioners finetune on to mitigate hallucinations in language models? In this work, we study the relationship between the factu…

OpenAI Paper: Halluzinationen offenbar unumgänglich
Große Sprachmodelle und KI-Chatbots, die nicht halluzinieren? Das hält selbst OpenAI für unmöglich. Aber es gibt einen Ausweg.
https://www.