Contextual Compression

Contextual Compression & Extractive Summarization is a post-retrieval distillation pipeline that strips redundant tokens from retrieved passages prior to generation; leveraging lightweight compression models (LLMLingua, LongLLMLingua) or SLMs, it evaluates token-level mutual information against the query intent, aggressively pruning uninformative fillers, boilerplate background, and conversational fluff to construct a dense factual excerpt.

Token-constrained contexts, cost-sensitive production APIs, and long-context RAG pipelines fighting information overload.

Raw retrieved passages contain 70%+ irrelevant background noise that triggers LLM 'Lost in the Middle' attention degradation and inflates billing; contextual compression slashes token payload by 50%-80% while sharpening reasoning accuracy.

Detail the LLMLingua compression algorithm using small language model conditional perplexity to evaluate token information density?

How does LongLLMLingua mitigate 'Lost in the Middle' via query-aware dynamic budget allocation across retrieved passages?

Compare Sentence-level Extractive Compression vs Token-level Pruning in maintaining semantic coherence?

How robust are modern LLMs when reasoning over compressed, grammatically fragmented text excerpts?

Weigh the latency cost of running compression inference vs net latency savings on downstream LLM prefill and generation?