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City University of Hong Kong
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Reranking in recommender systems can be revolutionized by shifting from local indices to generating global identifiers, enhancing robustness and user satisfaction.
Stop relying on LLMs to "hallucinate" reasoning paths – SEARCH-R uses a fine-tuned Llama3.1-8B model and dependency tree-based retrieval to navigate multi-hop question answering more reliably.
LLMs can now reliably extract job skills from text, even in low-resource settings, thanks to a novel framework that enforces output validity and reduces hallucinations.
