FrugalPrompt: Efficient Token Compression for LLMs

• FrugalPrompt is a prompt compression paradigm that selects high-salience tokens to reduce API costs and latency while maintaining task performance.
• It employs attribution-based methods like GlobEnc and DecompX to rank tokens, ensuring that only the most semantically significant details are retained.
• Empirical evaluations show that while tasks such as sentiment analysis tolerate aggressive compression, tasks like mathematical reasoning require fuller context for accuracy.

FrugalPrompt is a prompt compression and selection paradigm for LLMs centered on reducing contextual overhead by retaining only the most semantically significant tokens or input segments. The central aim is to eliminate low-utility redundancy in prompts, thereby decreasing API costs, inference-time latency, and energy usage, yet maintaining high task performance for most NLP workloads. The FrugalPrompt framework achieves these goals through attribution-based token ranking methods and systematic prompt reduction, with results that clarify both the practical benefits and the limitations in settings requiring dense token continuity, such as mathematical reasoning (Raiyan et al., 18 Oct 2025).

1. Motivation and Scope

Contemporary LLMs derive much of their zero-shot and few-shot prowess from large input contexts that often include both critical “signal” tokens and large numbers of functionally redundant or low-utility tokens. This contextual verbosity inflates monetary costs, model carbon footprint, and system latency. FrugalPrompt asserts, and empirically demonstrates, that a small fraction of the input—properly identified—often suffices for strong downstream performance on a broad array of NLP tasks. This approach is motivated by the observation that only a fraction of tokens typically carries the majority of semantic weight in a prompt (Raiyan et al., 18 Oct 2025).

2. Token Attribution Methodologies

FrugalPrompt operationalizes prompt compression by algorithmically scoring and ranking tokens for salience using two attribution paradigms:

• GlobEnc (Global Encoder Attribution): Aggregates layer-wise contributions for each token using attention rollout and vector norm statistics, capturing both direct token influence and residual path contributions. For a given input sequence $T = \langle t_1,\ldots,t_n \rangle$, GlobEnc produces a saliency vector $s = \langle s_1,\ldots,s_n \rangle$. Key technical formulation:

$$z_{i\leftarrow j} = g_{z_i^+}\left(\sum_h \alpha_{ij}^{(h)} f^{(h)}(x_j) + \mathbb{1}[i=j]\cdot x_j\right)$$

• DecompX (Decomposition-based Attribution): Propagates locally decomposed token representations through the network, explicitly separating attention-driven and feed-forward network contributions at each layer, and avoiding inter-layer mixing. At each layer, contributions are assembled via:

$$z^l_{i\leftarrow k} = \mathrm{LN}\left(\sum_k \left(x^l_{i\leftarrow k} + z^l_{i\leftarrow k}\right)\right)$$

Both methods yield a real-valued importance score $s_i$ per token $t_i$, quantifying each token’s impact on the ultimate model output. The set of tokens is then ranked by these scores.

3. Prompt Compression Procedure

Prompt compression in FrugalPrompt unfolds as follows:

1. Saliency Scoring: For input $T$, a function $\phi_\tau: \mathcal{T} \to \mathbb{R}^n$ computes scores $\mathbf{s}$ for each token.
2. Ranking: Tokens are permuted into monotonic decreasing order by $\mathbf{s}$, with a permutation $\pi$ so that $s_{\pi(1)}\geq\cdots\geq s_{\pi(n)}$.
3. Top-$k\%$ Retention: For a chosen $k\%$, only the top $p=\lceil\frac{k}{100} n\rceil$ tokens are retained.
4. Order Restoration: The indices are re-sorted in their original input order to ensure grammatical/syntactic coherence.
5. Frugalized Prompt Construction: Tokens in $T$ with indices in the selected set are concatenated to produce the final frugalized prompt $F_k$.

This workflow ensures prompt brevity while attempting to preserve task-relevant context.

4. Empirical Evaluation Across Tasks

FrugalPrompt was evaluated on four representative tasks:

• Sentiment Analysis (IMDb): 20% prompt reduction resulted in only marginal drops in accuracy and F1, with core sentiment-bearing tokens preserved.
• Commonsense QA (CosmosQA): Similar minor performance decline with aggressive compression; critical cues for answer selection remained discernible for LLMs.
• Summarization (e.g., news, with BLEU/ROUGE/BERTScore): Compression rates in the 60–80% retention range yielded near-baseline summary quality across metrics, though severe reduction impacted informativeness more sharply.
• Mathematical Reasoning (GSM8k): Performance declined rapidly with even moderate reduction; pass@1 scores dropped significantly, highlighting the necessity of exhaustive token continuity for multi-step reasoning.

This pattern indicates that task tolerance to contextual sparsity varies considerably—text classification and QA are robust to prompt reduction, while mathematical reasoning is highly sensitive to missing information.

5. Performance–Efficiency Trade-Offs

FrugalPrompt systematically explores the trade-off curve between input size and model performance:

• API Cost and Latency: Prompt reduction directly leads to lower token-based API costs and faster response times.
• Energy Consumption: Shorter inputs reduce the energy footprint of LLM inference.
• Task-Specific Retention: Optimal $k\%$ retention varies; for summarization and sentiment analysis, substantial prompt trimming is possible with little degradation, but for chain-of-thought or numerically-dense reasoning, truncation is detrimental.

The paper substantiates that LLMs can reconstruct context from high-salience cues for many tasks, supporting the principle that redundancy in input contexts can often be pruned in cost-sensitive scenarios.

6. Task Contamination and Model Behavior

An additional finding contradicts naive assumptions about input necessity: For some conventional NLP tasks, non-salient or randomly chosen tokens sometimes yielded above-chance performance. This asymmetric resilience possibly arises from task contamination—pretraining exposure to benchmark datasets or shallow memorization. Consequently, results on particular datasets may reflect memorized patterns rather than genuine contextual comprehension, especially when performance with bottom-$k\%$ tokens remains stable.

Such observations motivate more stringent evaluation protocols and suggest that FrugalPrompt could serve as a diagnostic for model reliance on true semantic cues versus memorized shortcuts.

7. Implications and Future Directions

FrugalPrompt presents clear implications for both research and deployment:

• Resource-Efficient LLM Applications: It provides a principled basis for lowering operational costs—and associated carbon emissions—across production deployments by reducing prompt length without retraining, applicable to most NLP workloads.
• Adaptive Compression: Future systems may dynamically adjust token retention at runtime based on task type, model confidence, or context length, offering an adaptive frugality mechanism.
• Prompt Engineering Insights: Token attribution analyses from FrugalPrompt yield actionable information about what constitutes decisive input content, informing improved human and machine prompt design.
• Evaluation Practices: The findings highlight the importance of contamination-free test sets, particularly if high accuracy is observed under trivial or dramatically reduced inputs.

In summary, FrugalPrompt establishes that a token attribution-guided, training-free approach to prompt compression can achieve substantial cost and efficiency gains without major loss of accuracy for most high-level language tasks. The limits of this approach—specifically its unsuitability for tasks demanding fine-grained sequential continuity—are shown to be intrinsic to the demands of logical and mathematical reasoning. The approach thus provides a useful paradigm for practical, frugal prompt engineering across the LLM ecosystem (Raiyan et al., 18 Oct 2025).