Large Language Models: An Applied Econometric Framework (2412.07031v2)

Abstract: How can we use the novel capacities of LLMs in empirical research? And how can we do so while accounting for their limitations, which are themselves only poorly understood? We develop an econometric framework to answer this question that distinguishes between two types of empirical tasks. Using LLMs for prediction problems (including hypothesis generation) is valid under one condition: no ``leakage'' between the LLM's training dataset and the researcher's sample. No leakage can be ensured by using open-source LLMs with documented training data and published weights. Using LLM outputs for estimation problems to automate the measurement of some economic concept (expressed either by some text or from human subjects) requires the researcher to collect at least some validation data: without such data, the errors of the LLM's automation cannot be assessed and accounted for. As long as these steps are taken, LLM outputs can be used in empirical research with the familiar econometric guarantees we desire. Using two illustrative applications to finance and political economy, we find that these requirements are stringent; when they are violated, the limitations of LLMs now result in unreliable empirical estimates. Our results suggest the excitement around the empirical uses of LLMs is warranted -- they allow researchers to effectively use even small amounts of language data for both prediction and estimation -- but only with these safeguards in place.

• The paper introduces a robust econometric framework for applying large language models in prediction and estimation tasks.
• It highlights the risk of training leakage in prediction tasks and advocates for using open-source models with documented training data.
• It addresses measurement error in LLM outputs and recommends collecting validation data to ensure the accuracy of economic estimates.

Insights from "LLMs: An Applied Econometric Framework"

The paper by Ludwig, Mullainathan, and Rambachan explores the application of LLMs in economic research through an econometric lens. It delivers a critical framework assessing the conditions under which LLMs can be effectively and accurately employed for prediction and estimation tasks within empirical research. This framework intricately catalogues two central empiric tasks: prediction and estimation, and provides a detailed examination of constraints such as training leakage and measurement error, which could potentially impede the validity of these tasks.

Prediction: A Condition of No Training Leakage

For using LLMs in prediction tasks, the paper presents a pivotal condition termed "no training leakage." The significance of training leakage, particularly when examining the verity of predictions generated by LLMs against benchmark datasets, is underscored through empirical investigations applied in finance and political economy domains. The paper illustrates that training leakage is a real threat due to common public benchmarks often being included in the training datasets of commercial LLMs. This contamination can significantly skew the perceived predictive accuracy, leading to misleading research conclusions.

To circumvent these issues, the authors advocate for researchers to employ open-source LLMs with documented training data and clear timestamp demarcations. Primarily, the paper stresses avoiding closed models like the GPT series from OpenAI, as they often lack transparency regarding their training datasets and temporal scope, which poses inherent risks of training leakage.

Estimation: The Dilemma of Measurement Error

In the context of estimation, the paper identifies measurement error as a formidable obstacle when LLM-generated labels are viewed as substitutes for gold-standard data labels. A critical premise here is that LLM outputs must exactly match the true underlying data to preclude bias in resulting parameter estimates. The assumption of no measurement error in LLM outputs is found to be unrealistic, given the documented brittleness of LLM performance across various tasks. Thus, empirical evidence suggests that LLM outputs as substitutes for gold-standard measurements can lead to substantial variability in estimated economic parameters, thereby affecting result reliability.

The authors propose a robust solution, advocating for the collection of validation data. This data can be used to model and correct any measurement errors associated with LLM outputs. This methodology aligns with traditional econometric practices dealing with data accuracy, supporting the notion that while LLMs can amplify available data, they should not replace rigorous empirical verification.

Grounding Novel Uses within Existing Frameworks

Beyond traditional prediction and estimation tasks, the paper expands its framework to cover novel uses of LLMs, such as hypothesis generation and simulating human responses. It positions hypothesis generation as akin to prediction problems, necessitating scrutiny for training leakage. For simulations of human responses, a parallel is drawn to estimation problems, emphasizing the necessity of validating LLM-generated outputs against real human responses.

Implications and Future Directions

This econometric framework systematically presents conditions essential for the valid execution of empirical research tasks employing LLMs. Its value lies in setting rigorous standards for using LLM outputs in econometrics, ensuring research integrity and mitigating risks of procedural errors stemming from opaque model functionalities and dataset overlaps.

Moving forward, the implications are significant for the integration of AI technologies in econometrics. Practical applications necessitate a thorough understanding of the underlying risks and methodologies for mitigating errors. As AI continues to evolve, econometricians must be adept at devising comparable frameworks to integrate future AI models responsibly and effectively into empirical research, maintaining the fidelity of research outcomes in sociopolitical and economic inquiries. This aligns with an overarching imperative for accepting AI tools as complementary rather than standalone analytical solutions within empirical economics.