Mayo Clinic is combating AI "hallucinations" (fabricating information) with a technique called "reverse retrieval-augmented generation" (Reverse RAG). Instead of feeding context to the AI before it generates text, Mayo's system generates text first and then uses retrieval to verify the generated information against a trusted knowledge base. If the AI's output can't be substantiated, it's flagged as potentially inaccurate, helping ensure the AI provides only evidence-based information, crucial in a medical context. This approach prioritizes accuracy over creativity, addressing a major challenge in applying generative AI to healthcare.
While "hallucinations" where LLMs fabricate facts are a significant concern for tasks like writing prose, Simon Willison argues they're less problematic in coding. Code's inherent verifiability through testing and debugging makes these inaccuracies easier to spot and correct. The greater danger lies in subtle logical errors, inefficient algorithms, or security vulnerabilities that are harder to detect and can have more severe consequences in a deployed application. These less obvious mistakes, rather than outright fabrications, pose the real challenge when using LLMs for software development.
Hacker News users generally agreed with the article's premise that code hallucinations are less dangerous than other LLM failures, particularly in text generation. Several commenters pointed out the existing robust tooling and testing practices within software development that help catch errors, making code hallucinations less likely to cause significant harm. Some highlighted the potential for LLMs to be particularly useful for generating boilerplate or repetitive code, where errors are easier to spot and fix. However, some expressed concern about over-reliance on LLMs for security-sensitive code or complex logic, where subtle hallucinations could have serious consequences. The potential for LLMs to create plausible but incorrect code requiring careful review was also a recurring theme. A few commenters also discussed the inherent limitations of LLMs and the importance of understanding their capabilities and limitations before integrating them into workflows.
The article analyzes Erowid trip reports to understand common visual hallucinations experienced on psychedelics. By processing thousands of reports, the author identifies recurring visual themes, categorized as "form constants." These include spirals, lattices, vortexes, and other geometric patterns, often accompanied by visual distortions like breathing walls and morphing objects. The analysis also highlights the influence of set and setting, showing how factors like dosage, substance, and environment impact the intensity and nature of visuals. Ultimately, the research aims to demystify psychedelic experiences and provide a data-driven understanding of the subjective effects of these substances.
HN commenters discuss the methodology of analyzing Erowid trip reports, questioning the reliability and representativeness of self-reported data from a self-selected group. Some point out the difficulty in quantifying subjective experiences and the potential for biases, like recall bias and the tendency to report more unusual or intense experiences. Others suggest alternative approaches, such as studying fMRI data or focusing on specific aspects of perception. The lack of a control group and the variability in dosage and individual responses are also raised as concerns, making it difficult to draw definitive conclusions about the typical psychedelic experience. Several users share anecdotes of their own experiences, highlighting the diverse and unpredictable nature of these altered states. The overall sentiment seems to be one of cautious interest in the research, tempered by skepticism about the robustness of the methods.
Near-death experiences, often characterized by vivid hallucinations and a sense of peace, are increasingly understood as a natural biological process rather than a mystical or spiritual one. As the brain faces oxygen deprivation and cellular breakdown, various physiological changes can trigger these altered states of consciousness. These experiences, frequently involving visions of deceased loved ones, comforting figures, or life reviews, likely result from the brain's attempt to create order and meaning amid neurological chaos. While culturally interpreted in diverse ways, the underlying mechanisms suggest that these end-of-life experiences are a common human phenomenon linked to the dying brain's struggle to function.
HN commenters discuss the prevalence of end-of-life visions and their potential explanations. Some share personal anecdotes of loved ones experiencing comforting hallucinations in their final moments, often involving deceased relatives or religious figures. Others question the article's focus on the "hallucinatory" nature of these experiences, suggesting that the brain's activity during the dying process might be generating something beyond simply hallucinations, perhaps offering a glimpse into a different state of consciousness. Several commenters highlight the importance of providing comfort and support to dying individuals, regardless of the nature of their experiences. Some also mention the possibility of cultural and societal influences shaping these end-of-life visions. The potential role of medication in contributing to these experiences is also briefly discussed. A few express skepticism, suggesting more research is needed before drawing firm conclusions about the meaning or nature of these phenomena.
End-of-life experiences, often involving visions of deceased loved ones, are extremely common and likely stem from natural brain processes rather than supernatural phenomena. As the brain nears death, various physiological changes, including oxygen deprivation and medication effects, can trigger these hallucinations. These visions are typically comforting and shouldn't be dismissed as mere delirium, but understood as a meaningful part of the dying process. They offer solace and a sense of connection during a vulnerable time, potentially serving as a psychological mechanism to help prepare for death. While research into these experiences is ongoing, understanding their biological basis can destigmatize them and allow caregivers and loved ones to offer better support to the dying.
Hacker News users discussed the potential causes of end-of-life hallucinations, with some suggesting they could be related to medication, oxygen deprivation, or the brain's attempt to make sense of deteriorating sensory input. Several commenters shared personal anecdotes of witnessing these hallucinations in loved ones, often involving visits from deceased relatives or friends. Some questioned the article's focus on the "hallucinatory" nature of these experiences, arguing they could be interpreted as comforting or meaningful for the dying individual, regardless of their neurological basis. Others emphasized the importance of compassionate support and acknowledging the reality of these experiences for those nearing death. A few also recommended further reading on the topic, including research on near-death experiences and palliative care.
Summary of Comments ( 42 )
https://news.ycombinator.com/item?id=43336609
Hacker News commenters discuss the Mayo Clinic's "reverse RAG" approach, expressing skepticism about its novelty and practicality. Several suggest it's simply a more complex version of standard prompt engineering, arguing that prepending context with specific instructions or questions is a common practice. Some question the scalability and maintainability of a large, curated knowledge base for every specific use case, highlighting the ongoing challenge of keeping such a database up-to-date and relevant. Others point out potential biases introduced by limiting the AI's knowledge domain, and the risk of reinforcing existing biases present in the curated data. A few commenters note the lack of clear evaluation metrics and express doubt about the claimed 40% hallucination reduction, calling for more rigorous testing and comparisons to simpler methods. The overall sentiment leans towards cautious interest, with many awaiting further evidence of the approach's real-world effectiveness.
The Hacker News post titled "Mayo Clinic's secret weapon against AI hallucinations: Reverse RAG in action" has generated several comments discussing the concept of Reverse Retrieval Augmented Generation (Reverse RAG) and its application in mitigating AI hallucinations.
Several commenters express skepticism about the novelty and efficacy of Reverse RAG. One commenter points out that the idea of checking the source material isn't new, and that existing systems like Perplexity.ai already implement similar fact-verification methods. Another echoes this sentiment, suggesting that the article is hyping a simple concept and questioning the need for a new term like "Reverse RAG." This skepticism highlights the view that the core idea isn't groundbreaking but rather a rebranding of existing fact-checking practices.
There's discussion about the practical limitations and potential downsides of Reverse RAG. One commenter highlights the cost associated with querying a vector database for every generated sentence, arguing that it might be computationally expensive and slow down the generation process. Another commenter raises concerns about the potential for confirmation bias, suggesting that focusing on retrieving supporting evidence might inadvertently reinforce existing biases present in the training data.
Some commenters delve deeper into the technical aspects of Reverse RAG. One commenter discusses the challenges of handling negation and nuanced queries, pointing out that simply retrieving supporting documents might not be sufficient for complex questions. Another commenter suggests using a dedicated "retrieval model" optimized for retrieval tasks, as opposed to relying on the same model for both generation and retrieval.
A few comments offer alternative approaches to address hallucinations. One commenter suggests generating multiple answers and then selecting the one with the most consistent supporting evidence. Another commenter proposes incorporating a "confidence score" for each generated sentence, reflecting the strength of supporting evidence.
Finally, some commenters express interest in learning more about the specific implementation details and evaluation metrics used by the Mayo Clinic, indicating a desire for more concrete evidence of Reverse RAG's effectiveness. One user simply states their impression that the Mayo Clinic is making impressive strides in using AI in healthcare.
In summary, the comments on Hacker News reveal a mixed reception to the concept of Reverse RAG. While some acknowledge its potential, many express skepticism about its novelty and raise concerns about its practicality and potential drawbacks. The discussion highlights the ongoing challenges in addressing AI hallucinations and the need for more robust and efficient solutions.