Omitted Variable Bias in Language Models Under Distribution Shift
arXiv:2602.16784v1 Announce Type: cross Abstract: Despite their impressive performance on a wide variety of tasks, modern language models remain susceptible to distribution shifts, exhibiting brittle behavior when evaluated on data that differs in distribution from their training data. In this paper, we describe how distribution shifts in language models can be separated into observable and unobservable components, and we discuss how established approaches for dealing with distribution shift address only the former. Importantly, we identify that the resulting omitted variable bias from unobserved variables can compromise both evaluation and optimization in language models. To address this challenge, we introduce a framework that maps the strength of the omitted variables to bounds on the worst-case generalization performance of language models under distribution shift. In empirical experiments, we show that using these bounds directly in language model evaluation and optimization prov
arXiv:2602.16784v1 Announce Type: cross Abstract: Despite their impressive performance on a wide variety of tasks, modern language models remain susceptible to distribution shifts, exhibiting brittle behavior when evaluated on data that differs in distribution from their training data. In this paper, we describe how distribution shifts in language models can be separated into observable and unobservable components, and we discuss how established approaches for dealing with distribution shift address only the former. Importantly, we identify that the resulting omitted variable bias from unobserved variables can compromise both evaluation and optimization in language models. To address this challenge, we introduce a framework that maps the strength of the omitted variables to bounds on the worst-case generalization performance of language models under distribution shift. In empirical experiments, we show that using these bounds directly in language model evaluation and optimization provides more principled measures of out-of-distribution performance, improves true out-of-distribution performance relative to standard distribution shift adjustment methods, and further enables inference about the strength of the omitted variables when target distribution labels are available.
Executive Summary
This article investigates the impact of distribution shifts on language models, highlighting the issue of omitted variable bias from unobserved variables. The authors propose a framework to quantify the strength of omitted variables and its effect on language model performance. Empirical experiments demonstrate the effectiveness of this approach in evaluating and optimizing language models under distribution shift, providing more accurate measures of out-of-distribution performance.
Key Points
- ▸ Distribution shifts in language models can be separated into observable and unobservable components
- ▸ Omitted variable bias from unobserved variables can compromise evaluation and optimization in language models
- ▸ A framework is introduced to map the strength of omitted variables to bounds on worst-case generalization performance
Merits
Methodological Contribution
The proposed framework provides a principled approach to addressing omitted variable bias in language models under distribution shift
Demerits
Limited Empirical Evaluation
The empirical experiments are limited to a specific set of language models and datasets, which may not be representative of all language model applications
Expert Commentary
The article makes a significant contribution to the field of natural language processing by highlighting the importance of addressing omitted variable bias in language models. The proposed framework provides a valuable tool for evaluating and optimizing language models under distribution shift, and has the potential to improve the robustness and reliability of language models in a wide range of applications. However, further research is needed to fully explore the implications of this work and to develop more comprehensive solutions to the problem of omitted variable bias.
Recommendations
- ✓ Future research should focus on extending the proposed framework to more diverse language models and datasets
- ✓ Practitioners should consider using the proposed framework to evaluate and optimize language models in real-world applications
