Command Palette

Search for a command to run...

Context Compression

Benched.ai Editorial Team

Context compression reduces the token length of retrieved or user-supplied documents so they fit into a model's context window without sacrificing answer quality.

  Compression Techniques

TechniqueLossinessTypical RatioTools
Extractive summarizationLossy2–5×Llama-Index, T5-based
Abstractive summarizationLossy5–15×GPT-4o, Mixtral-summarize
Sentence scoring (TextRank)Lossy1.5–3×spaCy / NetworkX
Knowledge distillationLossless for key factsVariableQLoRA-compress
Token pruning (stopwords)Near-lossless1.2–1.5×Custom regex

  Quality vs Ratio Curve (sample)

RatioAnswer F1 (QA)
1× (no compression)87 %
85 %
10×78 %
20×60 %

  Design Trade-offs

  • Higher ratios save cost but may omit reasoning chains required for citations.
  • Abstractive approaches risk hallucinating details not present in source.
  • Extractive summaries preserve grounding but may miss implicit context.

  Current Trends (2025)

  • Hierarchical compressors first prune at chunk level, then sentence level.
  • Semantic hashing enables constant-time deduplication before compression.
  • Benchmarks like HARDC reduce evaluation to a single "preserve answerability" metric.

  Implementation Tips

  1. Measure downstream task accuracy at each compression ratio to set safe limits.
  2. Cache compressed chunks alongside embeddings to avoid recomputation.
  3. Keep citation spans unchanged when regulatory compliance demands verbatim text.