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DTSTART:20091031T230000
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DESCRIPTION:Bio: Jie Huang is a Ph.D. candidate at UIUC working on various aspects of large language models\, including factuality\, reasoning\, and safety. His research aims to develop powerful and responsible large language models and apply them to solve real-world problems. (https://jeffhj.github.io/) \n\n Abstract: Large Language Models (LLMs) have emerged as a groundbreaking technology with their unparalleled text generation capabilities across various applications. Nevertheless\, concerns persist regarding the accuracy and appropriateness of their generated content. A contemporary methodology\, self-correction\, has been proposed as a remedy to these issues. Building upon this premise\, we critically examine the role and efficacy of self-correction within LLMs\, shedding light on its true potential and limitations. Central to our investigation is the notion of intrinsic self-correction\, whereby an LLM attempts to correct its initial responses based solely on its inherent capabilities\, without the crutch of external feedback. In the context of reasoning\, our research indicates that LLMs struggle to self-correct their responses without external feedback\, and at times\, their performance might even degrade post self-correction. Drawing from these insights\, we offer suggestions for future research and practical applications in this field. (https://arxiv.org/abs/2310.01798)\n\n------\n\nCreate your own Add to Calendar links with addevent.com/r/a \n
X-ALT-DESC;FMTTYPE=text/html:Bio: Jie Huang is a Ph.D. candidate at UIUC working on various aspects of large language models, including factuality, reasoning, and safety. His research aims to develop powerful and responsible large language models and apply them to solve real-world problems. (https://jeffhj.github.io/) <br><br> Abstract: Large Language Models (LLMs) have emerged as a groundbreaking technology with their unparalleled text generation capabilities across various applications. Nevertheless, concerns persist regarding the accuracy and appropriateness of their generated content. A contemporary methodology, self-correction, has been proposed as a remedy to these issues. Building upon this premise, we critically examine the role and efficacy of self-correction within LLMs, shedding light on its true potential and limitations. Central to our investigation is the notion of intrinsic self-correction, whereby an LLM attempts to correct its initial responses based solely on its inherent capabilities, without the crutch of external feedback. In the context of reasoning, our research indicates that LLMs struggle to self-correct their responses without external feedback, and at times, their performance might even degrade post self-correction. Drawing from these insights, we offer suggestions for future research and practical applications in this field. (https://arxiv.org/abs/2310.01798)<br /><br />------<br /><br />Create your own Add to Calendar links with addevent.com/r/a <br>
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SUMMARY:[C4AI] Jie Huang - Large Language Models Cannot Self-Correct Reasoning Yet (Geo-Asia)
DTSTART;TZID=Asia/Karachi:20240117T210000
DTEND;TZID=Asia/Karachi:20240117T220000
DTSTAMP:20261107T095124Z
TRANSP:OPAQUE
STATUS:CONFIRMED
SEQUENCE:0
LOCATION:https://meet.google.com/yhv-tiir-ava
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