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Efficient Prompting Methods for Large Language Models: A Survey
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Prompting is a mainstream paradigm for adapting large language models to specific natural language processing tasks without modifying internal parameters. Therefore, detailed supplementary knowledge needs to be integrated into external prompts, which inevitably brings extra human efforts and computational burdens for practical applications. As an effective solution to mitigate resource consumption, Efficient Prompting Methods have attracted a wide range of attention. We provide mathematical expressions at a high level to deeply discuss Automatic Prompt Engineering for different prompt components and Prompt Compression in continuous and discrete spaces. Finally, we highlight promising future directions to inspire researchers interested in this field.
Forward citations
Cited by 13 Pith papers
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Knowing How to Edit: Reliable Evaluation Signals for Diagnosing and Optimizing Prompts at Query Level
An execution-free evaluator that predicts prompt-quality metrics guides per-query prompt rewriting, but the reported consistent gains are not supported by the paper's own tables.
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A Modular Taxonomy for Hate Speech Definitions and Its Impact on Zero-Shot LLM Classification Performance
A taxonomy of 14 hate speech definition components plus evidence that zero-shot LLM hate speech classification is sensitive to which definition is inserted into the prompt, with model-dependent effects.
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Improved Representation Steering for Language Models
RePS, a reference-free bidirectional preference optimization objective, improves representation steering and suppression for Gemma models, outperforming language-modeling objectives and approaching prompting performance.
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Beyond Hard and Soft: Hybrid Context Compression for Balancing Local and Global Information Retention
HyCo2 combines soft global compression with hard local token selection, reporting QA performance near uncompressed retrieval while cutting context tokens by about 88.8%.
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When Dimensionality Hurts: The Role of LLM Embedding Compression for Noisy Regression Tasks
Compressing LLM text embeddings with an autoencoder to about 8 dimensions improves stock return prediction, but this benefit disappears on high-signal tasks, and sentiment features seem to work mainly because of compression.
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Towards Efficient and Robust Linguistic Emotion Diagnosis for Mental Health via Multi-Agent Instruction Refinement
A multi-agent prompt-rewriting loop is claimed to improve LLM emotion diagnosis accuracy, but its evaluation appears to optimize on the test set and lacks replication details.
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Step-level Verifier-guided Hybrid Test-Time Scaling for Large Language Models
A step-level verifier-guided hybrid of Best-of-N sampling, Monte Carlo tree search, and conditional self-refinement improves reasoning in small instruction-tuned LLMs, claiming up to 28.6-point gains.
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SI-Agent: An Agentic Framework for Feedback-Driven Generation and Tuning of Human-Readable System Instructions for Large Language Models
The paper proposes a multi-agent loop (instructor, follower, feedback) to auto-generate human-readable system prompts, claiming good benchmark performance and readability, but the supporting experiments are not reprod...
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MAARTA:Multi-Agentic Adaptive Radiology Teaching Assistant
MAARTA, a multi-agent LLM framework comparing expert and student gaze graphs, reports higher accuracy than single-agent baselines on simulated perceptual errors in chest X-ray interpretation.
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Balancing Content Size in RAG-Text2SQL System
Adding more schema descriptions and examples to retrieved documents improves table retrieval but increases SQL query errors in a Text2SQL model, with the best balance at medium document richness.
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Eliciting Causal Abilities in Large Language Models for Reasoning Tasks
The paper introduces SCIE, a prompt optimization method that uses LLM-generated data and estimated proxy-feature effects to produce enhanced reasoning instructions; observed gains are marginal and unstable.
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Knowledge Graphs are all you need: Leveraging KGs in Physics Question Answering
Using LLM-generated knowledge graphs to guide question decomposition modestly improves GPT-4's success rate on 100 high-school physics questions, but the evidence is informal and the dataset is not released.
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Foundations of Large Language Models
A textbook-style review of core LLM concepts, drawn from the authors' existing NLPBook, with no new experimental or theoretical results.
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