LLMLingua vs. LongLLMLingua vs. LLMLingua-2 vs. SeCom: Which Prompt Compression Method Fits Your Use Case?

Updated

The LLMLingua Series is a Microsoft Research project family for compressing prompts so that long inputs fit more efficiently into large language model (LLM) context windows. The four named methods differ mainly in how they decide what to remove and what kind of prompt they target: LLMLingua prunes non-essential tokens using a small language model's perplexity, LongLLMLingua adds query-aware compression and reorganization for long contexts, LLMLingua-2 learns compression targets via data distillation for fast, task-agnostic compression, and SeCom appears in the series listing but the site provides no dedicated description of its mechanism. Choose based on whether your bottleneck is general prompt length, very long retrieved context, or compression speed and faithfulness.

What each method does

Method Core mechanism (per site) Best suited to
LLMLingua Identifies and removes non-essential tokens in prompts using perplexity from a small language model (SLM) General prompt shortening where a small model can judge token importance
LongLLMLingua Enhances long-context information via query-aware compression and reorganization Long documents or retrieved contexts where the query should guide what survives
LLMLingua-2 Uses data distillation to learn compression targets for efficient and faithful task-agnostic compression Workloads needing speed and task-agnostic behavior; the site reports a 3x–6x speed improvement over LLMLingua
SeCom Listed as part of the LLMLingua Series; no mechanism described on the page Not determinable from the site alone — check the linked paper or code before adopting

How to choose

If you have a general, moderately long prompt

Start with LLMLingua. It is the baseline method: an SLM scores tokens by perplexity, and low-information tokens are dropped. This is the simplest mental model and the natural first experiment.

If your prompt is dominated by long context or retrieved documents

Use LongLLMLingua. Its distinguishing feature is query-aware compression and reorganization — the compression is conditioned on what the user actually asked, which matters when a long context contains much irrelevant material. This is the method to evaluate for RAG-style pipelines with lengthy retrieved passages.

If compression speed and task-agnostic behavior matter

Use LLMLingua-2. Rather than relying on perplexity at inference time, it learns compression targets through data distillation, which the site frames as efficient and faithful, with a stated 3x–6x speed gain over LLMLingua. If you are compressing at high volume or need consistent behavior across task types, this is the candidate.

If you are considering SeCom

The series page lists SeCom alongside the other methods but does not describe its approach. Treat it as an open item: consult its paper or repository before committing, and do not assume it shares the perplexity-based design of LLMLingua.

Practical notes before you commit

  • Integration path: the site states LLMLingua has been integrated into LangChain and LlamaIndex, two widely used RAG frameworks — relevant if you want compression inside an existing retrieval pipeline rather than a standalone script.
  • Examples available: the project's ./examples directory covers LLMLingua-2, RAG, online meeting, chain-of-thought, code, and RAG using LlamaIndex, which gives you concrete starting points for each scenario.
  • Adjacent work: the same team also publishes MInference (long-context inference speedup, reported up to 10X lower pre-filling latency on an A100 while maintaining accuracy at 1M-token prompts), RetrievalAttention (KV cache offloading via vector retrieval), and SCBench (KV-cache-centric evaluation of long-context methods). These address inference cost rather than prompt length, so they complement rather than replace compression.

A reasonable evaluation sequence

  1. Reproduce your current prompt and measure output quality and token count as a baseline.
  2. Run LLMLingua first to see how much can be removed without quality loss.
  3. If quality degrades on long or retrieval-heavy inputs, switch to LongLLMLingua and compare with the same query set.
  4. If latency of compression itself is a constraint, benchmark LLMLingua-2 against your chosen method.
  5. Only then investigate SeCom, using its paper or code as the source of truth.

The site does not publish pricing or access terms for these methods, so confirm licensing and hosting details from the linked code and papers before production use.