Overview
Research area: NLP Authors: Vesteinn Snaebjarnarson, Anej Svete, Josef Valvoda Published: 2025-06-06 arXiv: 2506.04844
Summary
Language models, as multi-task learners, acquire a wide range of abilities during training. A fundamental question is how much task-specific data is needed to learn a given task. Answering this for natural language is difficult: tasks are hard to delineate and can confound one another.
To rigorously investigate the relationship between data frequency and learnability, the authors turn to a controlled setting using formal languages induced from probabilistic finite automata. These serve as a methodological testbed to demonstrate that standard correlational evaluation practices are inherently flawed.
Key Contributions
- Binning semiring: a new algebraic object that lets researchers control how often a targeted property occurs in a sampled corpus, enabling causal analysis of data frequency.
- Causal formulation: the experimental pipeline is formalized as causal graphical models.
- Decomposed KL divergence: decomposed Kullback-Leibler divergence metrics are derived to measure the learnability of specific subtasks.
Findings
Experiments show that learnability evaluation without causal intervention produces erroneous conclusions due to confounding variables in correlational analysis. This serves as a warning about correlational pitfalls in natural language evaluation settings.
--- *Auto-collected on 2026-06-10*