Technology Category
Natural Language Processing: Prompt Engineering / PromptingMachine Learning: Large Multimodal Models (LMMs)Search and Optimization: Metareasoning and Metaheuristics
AI-Driven Scholarly Peer Review via Persistent Workflow Prompting, Meta-Prompting, and Meta-Reasoning
🤖 AI Summary
Large language models (LLMs) face severe data scarcity and difficulty in modeling expert reasoning for peer reviewing scientific manuscripts. Method: This paper proposes the Persistent Workflow Prompting (PWP) framework—a novel paradigm that encodes domain-specific tacit knowledge via meta-prompts and meta-reasoning, enabling systematic, conversation-level review from a single input. Built on standard chat interfaces, PWP integrates structured Markdown prompts, multimodal (text/figure/table) joint parsing, quantitative feasibility verification, and prior-based plausibility assessment. Designed specifically for experimental chemistry papers, it requires no coding and is API-agnostic. Contribution/Results: Experiments demonstrate PWP’s robust capability in identifying critical methodological flaws. It significantly mitigates input bias in high-order tasks—including claim-evidence differentiation, parameter reverse inference, and cross-modal consistency validation—yielding reliable, reproducible performance.
Technology Category
Application Category
📝 Abstract
Critical peer review of scientific manuscripts presents a significant challenge for Large Language Models (LLMs), partly due to data limitations and the complexity of expert reasoning. This report introduces Persistent Workflow Prompting (PWP), a potentially broadly applicable prompt engineering methodology designed to bridge this gap using standard LLM chat interfaces (zero-code, no APIs). We present a proof-of-concept PWP prompt for the critical analysis of experimental chemistry manuscripts, featuring a hierarchical, modular architecture (structured via Markdown) that defines detailed analysis workflows. We develop this PWP prompt through iterative application of meta-prompting techniques and meta-reasoning aimed at systematically codifying expert review workflows, including tacit knowledge. Submitted once at the start of a session, this PWP prompt equips the LLM with persistent workflows triggered by subsequent queries, guiding modern reasoning LLMs through systematic, multimodal evaluations. Demonstrations show the PWP-guided LLM identifying major methodological flaws in a test case while mitigating LLM input bias and performing complex tasks, including distinguishing claims from evidence, integrating text/photo/figure analysis to infer parameters, executing quantitative feasibility checks, comparing estimates against claims, and assessing a priori plausibility. To ensure transparency and facilitate replication, we provide full prompts, detailed demonstration analyses, and logs of interactive chats as supplementary resources. Beyond the specific application, this work offers insights into the meta-development process itself, highlighting the potential of PWP, informed by detailed workflow formalization, to enable sophisticated analysis using readily available LLMs for complex scientific tasks.
Problem
Research questions and friction points this paper is trying to address.
Addressing LLM limitations in expert peer review due to data constraints
Developing Persistent Workflow Prompting for systematic manuscript analysis
Enhancing LLM capability to evaluate scientific claims and evidence
Innovation
Methods, ideas, or system contributions that make the work stand out.
Persistent Workflow Prompting for systematic reviews
Meta-prompting to codify expert workflows
Hierarchical modular architecture via Markdown
🔎 Similar Papers
2024-02-23Citations: 3
