Inspired by human awareness this paper explores how LLMs can detect hallucinations before generating a response, achieving an accuracy rate of 84%.
Dean Taplin’s Post
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Interesting research - LLM’s and linear functions https://lnkd.in/e5xpeMJC
Large language models use a surprisingly simple mechanism to retrieve some stored knowledge
news.mit.edu
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Read my musings about what Dali’s quest for the irrational has in common with the challenges of Artificial Intelligence ! A mini essay in my blog! https://lnkd.in/eY_T-Pru
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With the consistant #ethical concerns around #ai, thay occur on a daily basis. And with AI development embracing #humanemotions (#emotionAI) e.g. the company Hume AI, the industry and VCs are throwing caution to the wind about the implications of this. This only hightens concerns for the potential impact on society, especialy when #AI already does not “design for multi-cultural, intersectional possibilities”. For more about this topic, and beyond, the incredible Suhair Khan explores this is the latest ‘the futures of intelligence’ post, below. #ai #emotionai #inclusiveai #accountablity #responsibleai #hcai #context Alexandra Deschamps-Sonsino Melanie Smith Stephanie Hare Alex Ash Kyle Soo Delfina Fantini van Ditmar Seán Boyle Myles Igwebuike https://lnkd.in/eRjW_kRb
the futures of intelligence
suhairk.substack.com
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To gain a deeper understanding of the mechanistic interpretability research problem and how a recent approach aims to make LLMs more explainable, head right over to Anish Dubey's helpful unpacking of monosemanticity.
Towards Monosemanticity: A Step Towards Understanding Large Language Models
towardsdatascience.com
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J'avoue que j'avais raté cette étude à l'époque : "random, noisy documents are actually helpful in increasing the accuracy of these systems when correctly positioned within a prompt". >> "there might be cases in which a pathologically low attention entropy causes the LLM to generate degenerate outputs ... We find that when we introduce random documents, the entropy of the systems has a 3X increase. Although these experiments show a pattern, we cannot yet answer this question in a definitive manner." >> "the retrieval component of RAG systems, be it dense or sparse, deserves increased attention from the research community" Entièrement d'accord. https://lnkd.in/eJySqGRB
The Power of Noise: Redefining Retrieval for RAG Systems
arxiv.org
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検索誘導性忘却 retrieval-induced forgetting Greer, J., Ali, A., Laksman, C., Huang, R., McClay, M., & Clewett, D. (2024). Effortful retrieval of semantic memories induces forgetting of related negative and neutral episodic memories. Cognition, 251, 105908. https://lnkd.in/gKbddkhu
Effortful retrieval of semantic memories induces forgetting of related negative and neutral episodic memories
sciencedirect.com
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Thanks to Dr. Lance Eliot, for featuring our paper in Forbes! Curious about the causes of LLM hallucinations? check out our new preprint, "Distinguishing Ignorance from Error in LLM Hallucinations" https://lnkd.in/d_pQZYSk Link for the article: https://lnkd.in/dwKxAbqF
Breakthrough In Preemptive Detection Of AI Hallucinations Reveals Vital Clues To Writing Prompts That Keep Generative AI From Freaking Out
social-www.forbes.com
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pub.towardsai.net: The Infini-Attention paper is discussed in detail in the article on Towards AI. The paper's concepts and insights are explored, providing valuable information for readers interested in this topic.
Infinite Context Window?!
pub.towardsai.net
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Our work "EdVAE: Mitigating Codebook Collapse with Evidential Discrete Variational Autoencoders" has been accepted for publication in Elsevier's Pattern Recognition journal 🎉 Thanks to Melih Kandemir and Gozde Unal for their invaluable guidance, advise, and support for the success of this research! You can read the preprint here: https://lnkd.in/gR7mJxyQ
EdVAE: Mitigating Codebook Collapse with Evidential Discrete Variational Autoencoders
arxiv.org
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Congrats to my PhD student Gülçin BAYKAL CAN for this solid work appearing in Pattern Recognition! Introduces the Evidential dVAE - EdVAE, where we improve the dVAEs (the discrete Variational Autoencoders, which are used in foundation models such as DALL-Es) with a hierarchical Bayesian modeling to mitigate the codebook collapse problem that persists in dVAEs.
Our work "EdVAE: Mitigating Codebook Collapse with Evidential Discrete Variational Autoencoders" has been accepted for publication in Elsevier's Pattern Recognition journal 🎉 Thanks to Melih Kandemir and Gozde Unal for their invaluable guidance, advise, and support for the success of this research! You can read the preprint here: https://lnkd.in/gR7mJxyQ
EdVAE: Mitigating Codebook Collapse with Evidential Discrete Variational Autoencoders
arxiv.org
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