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Retrieval-augmented generation

LLMLingua

LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models

Superseded baseline#25 of 1,179 most-superseded · first seen Oct 9, 2023

Superseded — cited as a baseline and beaten by newer methods

4 papers critique it · 6 beat it on benchmarks

What papers say

Verbatim critique sentences, each from a paper that cites LLMLingua as a baseline.

they rely exclusively on post-retrieval context compression without improving initial retrieval quality, creating an inherent performance ceiling
— MacRAG: Compress, Slice, and Scale-up for Multi-Scale Adaptive Context RAG
“CASC consistently outperforms strong baselines, including standard Top-K RAG, and existing context compression methods like RECOMP fangyuan2024recomp and LLMLingua huiqiang2023llmlin, across various Reader LLM backbones”
— Context-Adaptive Synthesis and Compression for Enhanced Retrieval-Augmented Generation in Complex Domains
“However, these methods lack context-awareness, making it challenging to determine the optimal compression ratio for a given LLM, resulting in context redundancy or over-compression.”
— AttentionRAG: Attention-Guided Context Pruning in Retrieval-Augmented Generation
“While explicit methods are generally agnostic to downstream LLMs and therefore more practical, they may suffer from higher compression loss due to the over-removal of input tokens.”
— Lighter And Better: Towards Flexible Context Adaptation For Retrieval Augmented Generation

Beaten on benchmarks

Head-to-head results where a newer method reports beating LLMLingua. Values are copied from the source paper's tables — verify against the cited paper.

What to use instead

Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.