SeCo performs semantic-driven context compression for LLMs by anchoring on query-relevant semantic centers and applying consistency-weighted token merging, yielding better downstream performance, lower latency, and stronger out-of-domain robustness than position-based methods across 14 benchmarks.
DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs
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Framing visual text compression as measure transport decomposes encoding loss into precision and coverage costs, enabling a label-free routing rule that matches oracle performance on 17 of 24 NLP datasets while using 10% fewer tokens.
citing papers explorer
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Beyond Position Bias: Shifting Context Compression from Position-Driven to Semantic-Driven
SeCo performs semantic-driven context compression for LLMs by anchoring on query-relevant semantic centers and applying consistency-weighted token merging, yielding better downstream performance, lower latency, and stronger out-of-domain robustness than position-based methods across 14 benchmarks.
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Visual Text Compression as Measure Transport
Framing visual text compression as measure transport decomposes encoding loss into precision and coverage costs, enabling a label-free routing rule that matches oracle performance on 17 of 24 NLP datasets while using 10% fewer tokens.