Papers by Yongqin Zeng

    1 papers
    DAST: Context-Aware Compression in LLMs via Dynamic Allocation of Soft Tokens (2025.findings-acl)

    Copied to clipboard

    Challenge: Existing semantic vector-based compression methods do not account for the intrinsic information density variations between context chunks, instead allocating soft tokens uniformly across context chunk.
    Approach: They propose a method that leverages the LLM's intrinsic understanding of contextual relevance to guide compression.
    Outcome: The proposed method surpasses state-of-the-art methods on long context tasks.

    What is GenGO?

    GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

    Information

    About
    Limitations