1. #Authors shouldn't be using #LLMs to create #reference #lists, bcz LLMs #hallucinate often.
2. Every #science #journal should run authors' ref lists through the @retractionwatch Database. https://retractiondatabase.org/RetractionSearch.aspx
Doing both things would improve #science #trustworthiness.
A cool test of how much different #AI models #hallucinate: the #BullshitBenchmark
The #Claude and #Qwen models seem to push back more when confronted with nonsensical questions. The #OpenAI models do not fare well.
Blog post: https://adam.holter.com/bullshitbench-v2-claude-and-qwen-are-the-only-models-that-push-back/
Results: https://petergpt.github.io/bullshit-benchmark/viewer/index.v2.html
BullshitBench v2 is out. Peter Gostev tested 70+ model variants across 100 questions spanning coding, medical, legal, finance, and physics. The benchmark measures one specific thing: whether a model will push back against a plausible-sounding but factually wrong statement, or just go along with it. Only two model families score meaningfully above 60% on bullshit [β¦]
A Large Language Model (LLM) is a deep-learning algorithm, often using a transformer architecture, that is trained on massive amounts of text data to understand, process, and generate human-like text.
A major shortcoming of LLMs is their tendency to "#Hallucinate" or confidently generate false or nonsensical information, along with the risk of perpetuating #Biases present in their training data.
https://knowledgezone.co.in/trends/browser?topic=Language-Model
You know you're doomed when your operating system vendor is selling their "#AI" fetish to you with a text like this.
Β»Agentic AI has powerful capabilities todayβfor example, it can complete many complex tasks in response to user prompts, transforming how users interact with their PCs. As these capabilities are introduced, AI models still face functional limitations in terms of how they behave and occasionally may #hallucinate and produce unexpected outputs. Additionally, agentic AI applications introduce novel #security #risks, such as cross-prompt injection (XPIA), where malicious content embedded in UI elements or documents can override agent instructions, leading to unintended actions like data #exfiltration or #malware installation.Β«
#OpenAI Says Models Programmed To Make Stuff Up Instead of Admitting Ignorance - Slashdot
#AI models often produce false outputs, or "hallucinations." Now OpenAI has admitted they may result from fundamental mistakes it makes when #training its models. The Register:
The admission came in a paper [PDF] published in early September, titled "Why Language Models #Hallucinate ,"
#llm #hallucinations #artificialintelligence
AI models often produce false outputs, or "hallucinations." Now OpenAI has admitted they may result from fundamental mistakes it makes when training its models. The Register: The admission came in a paper [PDF] published in early September, titled "Why Language Models Hallucinate," and penned by thr...
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