LLM Inference: A New Triangulation Method for Quantitative Text Research in Management

This article introduces the open-source tool llm-inference, which integrates the large language model (LLM) inference framework into quantitative text research in management. It provides a six-step workflow to achieve triangulation validation between traditional dictionary analysis and LLM inference, aiming to enhance the reliability and validity of quantitative text research. This tool is accompanied by the paper by Tim Hubbard et al., offering researchers a standardized integration path.

Research Background and Motivation

In management and organizational behavior research, traditional dictionary counting methods (such as Loughran-McDonald, LIWC) are interpretable and reproducible, but they struggle to capture complex constructs. While LLMs have strong text comprehension capabilities, they lack a standardized framework for systematic integration into academic research.

A recent special issue on methods in the Journal of Management Studies included a related paper that proposes a six-step workflow to integrate LLM inference with traditional methods, and the accompanying open-source tool llm-inference has been released.

Six-Step Workflow Framework

1. Theoretical Elaboration: Clarify construct definitions, operationalization, scale anchors, hypotheses, and pre-registration metadata to establish a theoretical framework.
2. Data Preparation: Upload/connect the corpus and check data quality via a validity dashboard.
3. Traditional Analysis: Generate baseline measurement indicators using dictionary counting.
4. LLM Micro-Inference: Core innovation—let LLMs score based on constructs, including subsample review, full corpus automatic scoring, and bias analysis.
5. LLM Macro-Inference: Inductively discover potential signals and generate candidate variables for exploratory regression.
6. Integration and Reporting: Integrate results for joint regression and generate reproducible packages (data, tables, appendices, etc.).

Technical Architecture and Design Philosophy

Tech Stack: Frontend Next.js 15 App Router; backend FastAPI-style Python deployed on Vercel.

Design Philosophy:

• Privacy First: Corpus is processed only in memory, and results are streamed directly to the local device.
• Bring Your Own Key (BYOK): Users provide API keys, ensuring flexibility and data protection.
• Reproducibility: Generate a reproducibility checklist including model information, parameters, prompts, etc.
• Extensibility: Monorepo structure under MIT license, supporting plugin extensions.

Implications for Research Practice

The tool promotes the triangulation paradigm, helping researchers:

• Evaluate the accuracy and bias of LLMs in measuring specific constructs
• Identify semantic dimensions missed by traditional methods
• Enhance the robustness and credibility of research findings
• Meet the requirements of top journals for methodological transparency and reproducibility

It provides management scholars with an entry point to LLM inference, lowering the threshold for methodological innovation.

Deployment and Usage

• Project Management: pnpm workspace, supporting local development and one-click deployment on Vercel.
• Local Requirements: Node 20+, pnpm 9+, Python 3.12+; database uses Neon Postgres (GDPR-compliant in EU regions).
• Demo Version: Reviewers can unlock server-side API keys via password (approx. $2 limit per session), allowing them to test core functions without bringing their own keys.

Conclusion

llm-inference represents an important milestone in the evolution of computational social science methodologies. It does not replace traditional methods but provides a systematic integration framework. As LLMs are increasingly applied in academia, tools that emphasize triangulation validation, transparency, and reproducibility will become key infrastructure for improving research quality.