Source-linked AI summary
Prompt Minimization: Reducing Input Redundancy Without Sacrificing Output Fidelity
Marius F. R. Juston, Kevin A. Karim, Jonathan Gao, Kevin C. Li, Rudhi Bashambu
TL;DR
The paper asks how to find a minimal prompt that preserves a model’s output fidelity, a question tied to prompt redundancy and the costs and accuracy risks of long inputs. It proposes three low-training minimization approaches and finds substantially shorter prompts whose outputs remain close in meaning to the originals. The authors conclude that typical prompts contain substantial redundancy and that simple search can recover strong compressions.
Problem
The paper asks what minimal natural-language prompt preserves a model’s output fidelity for a given task and model, amid redundancy and the costs and accuracy risks of long inputs.
Source: §1 · pp. 1–2Method
The study proposes three task-agnostic, relatively low-training approaches: zero-shot search, in-context evolutionary search, and an RL-flavored variant.
Source: §3 · p. 2, §3.2 · p. 3, §5 · p. 5Results
Across diverse, long-form instructions, the procedures found substantially shorter prompts whose outputs remained close in meaning to the originals.
Source: §5 · p. 5Takeaways & Limitations
The results suggest that typical prompts contain significant redundancy and that simple, training-light search can recover strong compressions.
Source: §5 · p. 5Iterative search can be costly in time and tokens, similarity estimates may miss pragmatic nuances, and single-reference fidelity may not generalize to task perturbations or broader input distributions.
Source: §5.2 · p. 6
Abstract
from arXiv · showhide
Despite the growing capabilities of large language models (LLMs), prompt design remains largely heuristic and ad hoc. This project will explore $\textit{prompt minimization}$, the process of reducing prompts to their smallest, most information-dense form while preserving output fidelity. Practically, shorter prompts reduce computational overhead and inference latency, especially when large contexts, such as entire documents or codebases, are included unnecessarily. Further, longer prompts can damage LLM reasoning and accuracy. Theoretically, the existence of multiple prompts yielding equivalent outputs suggests a high degree of redundancy in the input space, raising fundamental questions about what information is essential to elicit specific model behaviors. We propose three variant frameworks to identify and evaluate minimal prompts and demonstrate that minimal prompts often produce outputs comparable to those of their longer counterparts. These findings suggest new directions for efficient prompt engineering and deepen our understanding of input compression in LLMs.
1 Introduction
The paper asks what minimal natural-language prompt preserves a model’s output fidelity for a given task and model. This question matters because long inputs add latency and computational overhead and can impair reasoning and accuracy.
1 Introduction
- Long inputs increase latency and computational overhead, creating deployment bottlenecks, especially at scale.Source: §1 · p. 1
- Lengthier prompts can degrade accuracy, even when extra tokens duplicate shorter prompts or add little distraction.Source: §1 · p. 1
- Finding shorter prompts with equivalent results may clarify which information models rely on and guide prompt design.Source: §1 · p. 2
- The central question is what minimal natural-language prompt preserves output fidelity for a given task and model.Source: §1 · p. 2
2 Related work
Prior prompt-optimization work includes gradient-based and gradient-free approaches, while prompt minimization has focused mainly on pruning and less-explored summarization. The paper positions its contribution as three task-agnostic, low-training techniques for a gap in direct prompt minimization.
2 Related work
- Gradient-based prompt optimization can be computationally infeasible and costly to scale, motivating gradient-free and genetic approaches.Source: §2 · p. 2
- Prompt-minimization work has focused mainly on pruning tokens or summarizing prompts, with summarization less explored.Source: §2 · p. 2
- Existing prompt summarizers have limits: task specificity, noisy-information removal rather than relevant-information minimization, or extensive training requirements.Source: §2 · p. 2
- The study proposes three task-agnostic prompt-minimization techniques requiring no or little training to address direct prompt minimization.Source: §2 · p. 2
3 Approach
The paper proposes three task-agnostic, relatively low-training approaches to minimize prompts and evaluate compression alongside output fidelity. They range from a single zero-shot reduction to iterative candidate search and an RL-trained compressor.
3 Approach
- All three proposed prompt-minimization approaches are task-agnostic and require relatively little training.Source: §3 · p. 2
3.1 Zero-Shot Learning
- The zero-shot baseline asks an LLM to condense the original prompt, then evaluates the candidate by output similarity and prompt compression.Source: §3.1 · pp. 2–3
3.2 In-Context Learning
- The in-context evolutionary method maintains a frontier of candidate prompts, sampling and mutating candidates to avoid local minima.Source: §3.2 · p. 3
- It scores new candidates using compression and BERTScore, retaining the global top-N prompts as seeds for later iterations.Source: §3.2 · p. 3
3.3 RL-Fine Tuning
- The RL variant uses PPO to fine-tune low-rank LoRA weights while freezing the pretrained model backbone.Source: §3.3 · p. 4, Figure 3 · p. 4
- Its policy rollout generates compressed prompts with evolving system instructions, and PPO updates use a reward balancing semantic fidelity and token reduction.Source: §3.3 · p. 4
4 Experiments
The experiments use generated long-form prompts and evaluate prompt minimization along two dimensions: semantic similarity of outputs and prompt-length compression. The measures assess meaning preservation and compression level.
4.1 Dataset Curation
- The dataset contains 60 generated open-ended prompts, 60–232 words long and averaging 128 words, across diverse domains.Source: §4.1 · p. 4
4.2 Evaluation Metrics
- The evaluation focuses on semantic similarity and length compression.Source: §4.2 · p. 5
4.2.1 Semantic similarity
- BERTScore measures semantic similarity to assess whether outputs retain the same meaning despite wording or sentence-structure differences.Source: §4.2.1 · p. 5
4.2.2 Compression
- Compression is measured as the ratio of compressed-prompt length to original-prompt length, with string length counted in characters.Source: §4.2.2 · p. 5
5 Conclusions
The conclusion reports three search procedures and a two-term semantic-fidelity/compression objective, with substantially shorter prompts retaining outputs close in meaning to the originals. It highlights early gains, model-dependent compression, and limitations in cost, similarity measurement, and generalization.
5 Conclusions
- Across diverse long-form instructions, the procedures found substantially shorter prompts whose outputs remained close in meaning to the originals.The objective trades off semantic fidelity and compression; the score trajectory improves sharply in the next one to three iterations, then shows diminishing returns.Source: §5 · p. 5
5.1 Takeaways
- After iteration 0, total loss typically drops steeply within a few steps before plateauing across models and methods.Figures 4 and 5 show best-score trajectories for 60 long-context prompts per model, weighting BERTScore and compression equally.Source: §5.1 · p. 5, Figure 4 · p. 5, Figure 5 · p. 6
- Under matched settings, Llama-3.1-8B achieves stronger compression than Qwen-2.5-32B at similar BERTScore.The authors also report that Llama reaches lower compression ratios while keeping BERTScore effectively on par; Table 1 aggregates algorithm and model statistics.Source: §5.1 · pp. 5–6, Table 1 · p. 6, Figure 6 · p. 6
- Keeping a small frontier and resampling around it produced more stable progress than pure zero-shot proposals.Source: §5.1 · p. 5
- Compression and similarity clearly shaped the discovered frontier: heavier emphasis on similarity preserves structure, whereas heavier emphasis on compression pushes toward terser prompts at the risk of meaning drift.Source: §5.1 · p. 6
5.2 Limitations
- Multi-candidate iterative search can be costly; embedding/BERT similarity may misjudge paraphrases, and single-output matches may not generalize to perturbed tasks or broader distributions.Several long prompts were machine-generated, so outcomes may differ for expert-authored prompts with strict correctness constraints.Source: §5.2 · p. 6
5.3 Future Work
- Future work proposes preference studies, a Pareto frontier over similarity, compression, cost, and safety, richer fidelity measures, adaptive stopping, and task- or model-conditioned minimization.Source: §5.3 · p. 7
5.4 Code availability
- The code is available in the GitHub repository jontgao/546-prompt-minimization, and output logs and runs are available in the HuggingFace repository SuperComputer/PromptMinimization.Source: §5.4 · p. 7
A Result examples
The examples trace prompts becoming shorter across tasks and models, from detailed requests to compact instructions that retain selected task requirements. Figures show trajectories for zero-shot, in-context, and RL fine-tuning approaches.
A Result examples
- Figures 8–13 show Qwen-32B and Llama-3.1-8B minimization trajectories across zero-shot, in-context, and RL fine-tuning, tracking score, BERT-score, and compression ratio.Source: Figure 8 · p. 9, Figure 9 · p. 10, Figure 10 · p. 11
- At Iteration 14, the photosynthesis prompt was “Photosynthesis in 5 steps” (Score 0.0786; Bert 0.8893; Comp 0.0465), versus Iteration 1’s detailed step-by-step request (Score 0.1817; Bert 0.8819; Comp 0.2454).Source: A Result examples · p. 12
- At Iteration 13, the Qwen AI prompt still names safeguards, regulations, and ethics alongside human judgment and machine autonomy.Source: A Result examples · p. 13
- The Llama AI example contracts from asking about AI integration in daily life to the balance between human judgment and machine autonomy by Iteration 14.Source: A Result examples · p. 13
- Qwen’s photosynthesis examples retain the requested biological processes and global disruptions while varying how explicitly they describe energy conversion and stomata.Source: A Result examples · p. 14