Task Facet Learning: A Structured Approach to Prompt Optimization

• URL: http://arxiv.org/abs/2406.10504v1
• Date: Sat, 15 Jun 2024 04:54:26 GMT
• Title: Task Facet Learning: A Structured Approach to Prompt Optimization
• Authors: Gurusha Juneja, Nagarajan Natarajan, Hua Li, Jian Jiao, Amit Sharma,
• Abstract summary: We propose an algorithm that learns multiple facets of a task from a set of training examples. The resulting algorithm, UniPrompt, consists of a generative model to generate initial candidates for each prompt section. Empirical evaluation on multiple datasets and a real-world task shows that prompts generated using UniPrompt obtain higher accuracy than human-tuned prompts.
• Score: 14.223730629357178
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Given a task in the form of a basic description and its training examples, prompt optimization is the problem of synthesizing the given information into a text prompt for a large language model (LLM). Humans solve this problem by also considering the different facets that define a task (e.g., counter-examples, explanations, analogies) and including them in the prompt. However, it is unclear whether existing algorithmic approaches, based on iteratively editing a given prompt or automatically selecting a few in-context examples, can cover the multiple facets required to solve a complex task. In this work, we view prompt optimization as that of learning multiple facets of a task from a set of training examples. We identify and exploit structure in the prompt optimization problem -- first, we find that prompts can be broken down into loosely coupled semantic sections that have a relatively independent effect on the prompt's performance; second, we cluster the input space and use clustered batches so that the optimization procedure can learn the different facets of a task across batches. The resulting algorithm, UniPrompt, consists of a generative model to generate initial candidates for each prompt section; and a feedback mechanism that aggregates suggested edits from multiple mini-batches into a conceptual description for the section. Empirical evaluation on multiple datasets and a real-world task shows that prompts generated using UniPrompt obtain higher accuracy than human-tuned prompts and those from state-of-the-art methods. In particular, our algorithm can generate long, complex prompts that existing methods are unable to generate. Code for UniPrompt will be available at \url{https://aka.ms/uniprompt}.

Related papers

• Vector Quantization Prompting for Continual Learning [23.26682439914273]
  Continual learning requires to overcome catastrophic forgetting when training a single model on a sequence of tasks. Recent top-performing approaches are prompt-based methods that utilize a set of learnable parameters to encode task knowledge. We propose VQ-Prompt, a prompt-based continual learning method that incorporates Vector Quantization into end-to-end training of a set of discrete prompts.
  arXiv  Detail & Related papers  (2024-10-27T13:43:53Z)
• QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning [58.767866109043055]
  We introduce Query-dependent Prompt Optimization (QPO), which iteratively fine-tune a small pretrained language model to generate optimal prompts tailored to the input queries. We derive insights from offline prompting demonstration data, which already exists in large quantities as a by-product of benchmarking diverse prompts on open-sourced tasks. Experiments on various LLM scales and diverse NLP and math tasks demonstrate the efficacy and cost-efficiency of our method in both zero-shot and few-shot scenarios.
  arXiv  Detail & Related papers  (2024-08-20T03:06:48Z)
• Automatic Prompt Selection for Large Language Models [22.73421169410049]
  We propose an effective approach to automatically select the optimal prompt for a given input from a finite set of synthetic candidate prompts. Our approach balances prompt generality-specificity and eliminates the need for resource-intensive training and inference. It demonstrates competitive performance on zero-shot question-answering datasets: GSM8K, MultiArithm, and AQuA.
  arXiv  Detail & Related papers  (2024-04-03T13:20:24Z)
• 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 Space Optimizing Few-shot Reasoning Success with Large Language Models [7.453926835095568]
  Prompt engineering enables large language models (LLMs) to excel in various tasks, such as arithmetic reasoning, question answering, summarization, relation extraction, machine translation, and sentiment analysis. Current approaches lack a solid mathematical solution for determining optimal prompts. Our methodology utilizes text embeddings to obtain basis vectors by matrix decomposition, and then constructs a space for representing all prompts.
  arXiv  Detail & Related papers  (2023-06-06T15:43:16Z)
• TEMPERA: Test-Time Prompting via Reinforcement Learning [57.48657629588436]
  We propose Test-time Prompt Editing using Reinforcement learning (TEMPERA) In contrast to prior prompt generation methods, TEMPERA can efficiently leverage prior knowledge. Our method achieves 5.33x on average improvement in sample efficiency when compared to the traditional fine-tuning methods.
  arXiv  Detail & Related papers  (2022-11-21T22:38:20Z)
• Decomposed Prompting: A Modular Approach for Solving Complex Tasks [55.42850359286304]
  We propose Decomposed Prompting to solve complex tasks by decomposing them (via prompting) into simpler sub-tasks. This modular structure allows each prompt to be optimized for its specific sub-task. We show that the flexibility and modularity of Decomposed Prompting allows it to outperform prior work on few-shot prompting.
  arXiv  Detail & Related papers  (2022-10-05T17:28:20Z)
• 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)
• IDPG: An Instance-Dependent Prompt Generation Method [58.45110542003139]
  Prompt tuning is a new, efficient NLP transfer learning paradigm that adds a task-specific prompt in each input instance during the model training stage. We propose a conditional prompt generation method to generate prompts for each input instance.
  arXiv  Detail & Related papers  (2022-04-09T15:45:27Z)

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.