From Prompt Engineering to Prompt Science With Human in the Loop

• URL: http://arxiv.org/abs/2401.04122v3
• Date: Fri, 10 May 2024 03:50:26 GMT
• Title: From Prompt Engineering to Prompt Science With Human in the Loop
• Authors: Chirag Shah,
• Abstract summary: This article presents a new methodology inspired by codebook construction through qualitative methods to address that. We show how a set of researchers can work through a rigorous process of labeling, deliberating, and documenting to remove subjectivity and bring transparency and replicability to prompt generation process.
• Score: 12.230632679443364
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As LLMs make their way into many aspects of our lives, one place that warrants increased scrutiny with LLM usage is scientific research. Using LLMs for generating or analyzing data for research purposes is gaining popularity. But when such application is marred with ad-hoc decisions and engineering solutions, we need to be concerned about how it may affect that research, its findings, or any future works based on that research. We need a more scientific approach to using LLMs in our research. While there are several active efforts to support more systematic construction of prompts, they are often focused more on achieving desirable outcomes rather than producing replicable and generalizable knowledge with sufficient transparency, objectivity, or rigor. This article presents a new methodology inspired by codebook construction through qualitative methods to address that. Using humans in the loop and a multi-phase verification processes, this methodology lays a foundation for more systematic, objective, and trustworthy way of applying LLMs for analyzing data. Specifically, we show how a set of researchers can work through a rigorous process of labeling, deliberating, and documenting to remove subjectivity and bring transparency and replicability to prompt generation process. A set of experiments are presented to show how this methodology can be put in practice.

Related papers

• EVOLvE: Evaluating and Optimizing LLMs For Exploration [76.66831821738927]
  Large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty. We measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications. Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs.
  arXiv  Detail & Related papers  (2024-10-08T17:54:03Z)
• What is the Role of Large Language Models in the Evolution of Astronomy Research? [0.0]
  ChatGPT and other state-of-the-art large language models (LLMs) are rapidly transforming multiple fields. These models, commonly trained on vast datasets, exhibit human-like text generation capabilities.
  arXiv  Detail & Related papers  (2024-09-30T12:42:25Z)
• Towards Efficient Large Language Models for Scientific Text: A Review [4.376712802685017]
  Large language models (LLMs) have ushered in a new era for processing complex information in various fields, including science. Due to the power of LLMs, they require extremely expensive computational resources, intense amounts of data, and training time. In recent years, researchers have proposed various methodologies to make scientific LLMs more affordable.
  arXiv  Detail & Related papers  (2024-08-20T10:57:34Z)
• LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing [106.45895712717612]
  Large language models (LLMs) have shown remarkable versatility in various generative tasks. This study focuses on the topic of LLMs assist NLP Researchers. To our knowledge, this is the first work to provide such a comprehensive analysis.
  arXiv  Detail & Related papers  (2024-06-24T01:30:22Z)
• A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery [68.48094108571432]
  Large language models (LLMs) have revolutionized the way text and other modalities of data are handled. We aim to provide a more holistic view of the research landscape by unveiling cross-field and cross-modal connections between scientific LLMs.
  arXiv  Detail & Related papers  (2024-06-16T08:03:24Z)
• LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
  Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model. This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
  arXiv  Detail & Related papers  (2024-02-26T07:33:05Z)
• Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs [60.40396361115776]
  This paper introduces a novel collaborative approach, namely SlimPLM, that detects missing knowledge in large language models (LLMs) with a slim proxy model. We employ a proxy model which has far fewer parameters, and take its answers as answers. Heuristic answers are then utilized to predict the knowledge required to answer the user question, as well as the known and unknown knowledge within the LLM.
  arXiv  Detail & Related papers  (2024-02-19T11:11:08Z)
• A Reliable Knowledge Processing Framework for Combustion Science using Foundation Models [0.0]
  The study introduces an approach to process diverse combustion research data, spanning experimental studies, simulations, and literature. The developed approach minimizes computational and economic expenses while optimizing data privacy and accuracy. The framework consistently delivers accurate domain-specific responses with minimal human oversight.
  arXiv  Detail & Related papers  (2023-12-31T17:15:25Z)
• LLMs for Science: Usage for Code Generation and Data Analysis [0.07499722271664144]
  Large language models (LLMs) have been touted to enable increased productivity in many areas of today's work life. It is still unclear how the potential of LLMs will materialise in research practice.
  arXiv  Detail & Related papers  (2023-11-28T12:29:33Z)
• Towards an Understanding of Large Language Models in Software Engineering Tasks [29.30433406449331]
  Large Language Models (LLMs) have drawn widespread attention and research due to their astounding performance in text generation and reasoning tasks. The evaluation and optimization of LLMs in software engineering tasks, such as code generation, have become a research focus. This paper comprehensively investigate and collate the research and products combining LLMs with software engineering.
  arXiv  Detail & Related papers  (2023-08-22T12:37:29Z)
• A Comprehensive Overview of Large Language Models [68.22178313875618]
  Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks. This article provides an overview of the existing literature on a broad range of LLM-related concepts.
  arXiv  Detail & Related papers  (2023-07-12T20:01:52Z)

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.