SPELL: Semantic Prompt Evolution based on a LLM

• URL: http://arxiv.org/abs/2310.01260v1
• Date: Mon, 2 Oct 2023 14:51:16 GMT
• Title: SPELL: Semantic Prompt Evolution based on a LLM
• Authors: Yujian Betterest Li, Kai Wu
• Abstract summary: Large language models (LLMs) have powerful ability of generating coherent texts token by token. We propose a black-box evolution algorithm for automatically optimizing texts, namely SPELL. Experimental results show that SPELL could rapidly improve the prompts indeed.
• Score: 5.983194751474721
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompt engineering is a new paradigm for enhancing the performance of trained neural network models. For optimizing text-style prompts, existing methods usually individually operate small portions of a text step by step, which either breaks the fluency or could not globally adjust a prompt. Since large language models (LLMs) have powerful ability of generating coherent texts token by token, can we utilize LLMs for improving prompts? Based on this motivation, in this paper, considering a trained LLM as a text generator, we attempt to design a black-box evolution algorithm for automatically optimizing texts, namely SPELL (Semantic Prompt Evolution based on a LLM). The proposed method is evaluated with different LLMs and evolution parameters in different text tasks. Experimental results show that SPELL could rapidly improve the prompts indeed. We further explore the evolution process and discuss on the limitations, potential possibilities and future work.

Related papers

• IPO: Interpretable Prompt Optimization for Vision-Language Models [40.83071220530289]
  This paper introduces a simple but interpretable prompt (IPO) IPO utilizes large language models (LLMs) to generate textual prompts dynamically. We incorporate a large multimodal model (LMM) to condition on visual content by generating image descriptions.
  arXiv  Detail & Related papers  (2024-10-20T14:10:22Z)
• Self-Instructed Derived Prompt Generation Meets In-Context Learning: Unlocking New Potential of Black-Box LLMs [30.333277284839053]
  Large language models (LLMs) have shown success in generating high-quality responses. Existing methods to enhance response quality often involve a prompt refinement model. We introduce a self-instructed in-context learning framework that empowers LLMs to deliver more effective responses.
  arXiv  Detail & Related papers  (2024-09-03T02:42:39Z)
• MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization [73.7779735046424]
  We show that different prompts should be adapted to different Large Language Models (LLM) to enhance their capabilities across various downstream tasks in NLP. We then propose a model-adaptive prompt (MAPO) method that optimize the original prompts for each specific LLM in downstream tasks.
  arXiv  Detail & Related papers  (2024-07-04T18:39:59Z)
• Efficient Prompting Methods for Large Language Models: A Survey [50.171011917404485]
  Prompting has become a mainstream paradigm for adapting large language models (LLMs) to specific natural language processing tasks. This approach brings the additional computational burden of model inference and human effort to guide and control the behavior of LLMs. We present the basic concepts of prompting, review the advances for efficient prompting, and highlight future research directions.
  arXiv  Detail & Related papers  (2024-04-01T12:19:08Z)
• Prompt Highlighter: Interactive Control for Multi-Modal LLMs [50.830448437285355]
  This study targets a critical aspect of multi-modal LLMs' (LLMs&VLMs) inference: explicit controllable text generation. We introduce a novel inference method, Prompt Highlighter, which enables users to highlight specific prompt spans to interactively control the focus during generation. We find that, during inference, guiding the models with highlighted tokens through the attention weights leads to more desired outputs.
  arXiv  Detail & Related papers  (2023-12-07T13:53:29Z)
• A Survey on Prompting Techniques in LLMs [0.0]
  Autoregressive Large Language Models have transformed the landscape of Natural Language Processing. We present a taxonomy of existing literature on prompting techniques and provide a concise survey based on this taxonomy. We identify some open problems in the realm of prompting in autoregressive LLMs which could serve as a direction for future research.
  arXiv  Detail & Related papers  (2023-11-28T17:56:34Z)
• AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations [52.43593893122206]
  Alignedcot is an in-context learning technique for invoking Large Language Models. It achieves consistent and correct step-wise prompts in zero-shot scenarios. We conduct experiments on mathematical reasoning and commonsense reasoning.
  arXiv  Detail & Related papers  (2023-11-22T17:24:21Z)
• Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers [70.18534453485849]
  EvoPrompt is a framework for discrete prompt optimization. It borrows the idea of evolutionary algorithms (EAs) as they exhibit good performance and fast convergence. It significantly outperforms human-engineered prompts and existing methods for automatic prompt generation.
  arXiv  Detail & Related papers  (2023-09-15T16:50:09Z)
• RLPrompt: Optimizing Discrete Text Prompts With Reinforcement Learning [84.75064077323098]
  This paper proposes RLPrompt, an efficient discrete prompt optimization approach with reinforcement learning (RL) RLPrompt is flexibly applicable to different types of LMs, such as masked gibberish (e.g., grammaBERT) and left-to-right models (e.g., GPTs) Experiments on few-shot classification and unsupervised text style transfer show superior performance over a wide range of existing finetuning or prompting methods.
  arXiv  Detail & Related papers  (2022-05-25T07:50:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.