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  1. RESEARCH · CL_320793 ·

    TokenGate speeds up LLM long-context prefill up to 11.3x with learned token pruning

    Researchers from Stanford University and UC Berkeley have developed TokenGate, a novel module designed to accelerate the prefill process for large language models (LLMs) with long contexts. By employing a learned token …

  2. RESEARCH · CL_107823 ·

    New CAMS framework improves LLM summary faithfulness with claim-anchored attribution

    Researchers have developed a new framework called CAMS (Claim-Anchored Multi-document Summarization) to address hallucination and attribution issues in large language models. CAMS extracts atomic claims with token-level…