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Understanding and addressing these nuanced error types is crucial for improving LLM reliability. Researchers from the OATML group at the University of Oxford have developed a statistical approach to detect a specific type of error in LLMs, known as “confabulations.” Check out the Paper , Project , and GitHub.
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Despite their wealth of general knowledge, state-of-the-art LLMs only have access to the information they were trained on. This can lead to factual inaccuracies (hallucinations) when the LLM is prompted to generate text based on information they didn’t see during their training.
This surprising trend highlights the continued relevance of SLMs and raises important questions about their role in the LLM era, a topic previously overlooked in research. This study examines the role of SMs in the LLM era from two perspectives: collaboration with LLMs and competition against them.
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