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Speculative decoding accelerates inference for a large language model (LLM), referred to as the \emph{target model}, by first using a smaller model, referred to as the \emph{draft model}, to generate candidate tokens and then verifying them with the target model for acceptance or rejection. Prior studies primarily focu...
This work identifies a class of user-issued instructions, Session Constraints, that are meant to constrain LLM's behavior for the remainder of a session but are silently dropped during compaction, and introduces COMPINT, an evaluation suite that evaluates compactors across three long-context scenarios: multi-turn chat,...
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