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Dive into "AI's Alternate Realities" as we explore Large Language Model (LLM) hallucinations – the curious phenomenon where AI confidently generates plausible, yet nonfactual, content. This podcast unpacks why these "alternate realities" emerge, from factual inconsistencies to logical and context divergences. We'll investigate the root causes spanning data issues, training challenges, and inference shortcomings. Join us to discover cutting-edge detection methods and mitigation strategies, including Retrieval-Augmented Generation (RAG) and self-correction techniques, to build more reliable and trustworthy AI systems.
By ML-whoDive into "AI's Alternate Realities" as we explore Large Language Model (LLM) hallucinations – the curious phenomenon where AI confidently generates plausible, yet nonfactual, content. This podcast unpacks why these "alternate realities" emerge, from factual inconsistencies to logical and context divergences. We'll investigate the root causes spanning data issues, training challenges, and inference shortcomings. Join us to discover cutting-edge detection methods and mitigation strategies, including Retrieval-Augmented Generation (RAG) and self-correction techniques, to build more reliable and trustworthy AI systems.