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School of Computer Science and Engineering, Sun Yat-sen University, China, Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China, Shenzhen Loop Area Institute, China
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FRAP achieves substantial improvements in performance estimation under distribution shifts by effectively merging the strengths of foundation and base models.
LLMs can maintain generation quality in long-context scenarios while using significantly less context, simply by adaptively allocating context based on uncertainty.
