Causally Evaluating the Learnability of Formal Language Tasks

• URL: http://arxiv.org/abs/2606.09822v1
• Date: Mon, 08 Jun 2026 17:58:36 GMT
• Title: Causally Evaluating the Learnability of Formal Language Tasks
• Abstract summary: We rigorously investigate the relationship between data frequency and learnability.<n>We derive Kullback-Leibler divergence metrics to measure the learnability of specific sub-tasks.<n>Our experiments show that evaluating learnability without causal intervention leads to incorrect conclusions.
• Score: 59.73001113068827
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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, we 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. To enable causal analysis, we introduce the binning semiring, an algebraic object that lets us control how often a targeted property occurs in a sampled corpus. We formulate the experimental pipeline as a causal graphical model and derive decomposed Kullback-Leibler divergence metrics to measure the learnability of specific sub-tasks. Our experiments show that evaluating learnability without causal intervention leads to incorrect conclusions due to confounders in correlational analysis, and serve as a warning about correlational pitfalls in natural-language settings.

Related papers

• Investigation into In-Context Learning Capabilities of Transformers [14.14937947207076]
  Transformers have demonstrated a strong ability for in-context learning (ICL)<n>We conduct a systematic empirical study of in-context learning for binary classification tasks.<n>Our results highlight the critical role of dimensionality, signal strength, and contextual information in determining when in-context learning succeeds and when it fails.
  arXiv  Detail & Related papers  (2026-04-28T16:57:55Z)
• Provable Benefits of Task-Specific Prompts for In-context Learning [44.768199865867494]
  In this work, we consider a novel setting where the global task distribution can be partitioned into a union of conditional task distributions.<n>We then examine the use of task-specific prompts and prediction heads for learning the prior information associated with the conditional task distribution using a one-layer attention model.
  arXiv  Detail & Related papers  (2025-03-03T22:37:03Z)
• On Subjective Uncertainty Quantification and Calibration in Natural Language Generation [2.622066970118316]
  Large language models often involve the generation of free-form responses, in which case uncertainty quantification becomes challenging. This work addresses these challenges from a perspective of Bayesian decision theory. We discuss how this assumption enables principled quantification of the model's subjective uncertainty and its calibration. The proposed methods can be applied to black-box language models.
  arXiv  Detail & Related papers  (2024-06-07T18:54:40Z)
• A Unified Causal View of Instruction Tuning [76.1000380429553]
  We develop a meta Structural Causal Model (meta-SCM) to integrate different NLP tasks under a single causal structure of the data. Key idea is to learn task-required causal factors and only use those to make predictions for a given task.
  arXiv  Detail & Related papers  (2024-02-09T07:12:56Z)
• Why think step by step? Reasoning emerges from the locality of experience [35.99870462573641]
  We investigate why and how chain-of-thought reasoning is useful in language models. We show that reasoning is effective when training data consists of overlapping local clusters of variables.
  arXiv  Detail & Related papers  (2023-04-07T21:04:03Z)
• Stateful Offline Contextual Policy Evaluation and Learning [88.9134799076718]
  We study off-policy evaluation and learning from sequential data. We formalize the relevant causal structure of problems such as dynamic personalized pricing. We show improved out-of-sample policy performance in this class of relevant problems.
  arXiv  Detail & Related papers  (2021-10-19T16:15:56Z)
• Competency Problems: On Finding and Removing Artifacts in Language Data [50.09608320112584]
  We argue that for complex language understanding tasks, all simple feature correlations are spurious. We theoretically analyze the difficulty of creating data for competency problems when human bias is taken into account.
  arXiv  Detail & Related papers  (2021-04-17T21:34:10Z)
• An Investigation of Language Model Interpretability via Sentence Editing [5.492504126672887]
  We re-purpose a sentence editing dataset as a testbed for interpretability of pre-trained language models (PLMs) This enables us to conduct a systematic investigation on an array of questions regarding PLMs' interpretability. The investigation generates new insights, for example, contrary to the common understanding, we find that attention weights correlate well with human rationales.
  arXiv  Detail & Related papers  (2020-11-28T00:46:43Z)
• Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance? [27.64235687067883]
  We show that models can learn to encode linguistic properties even if they are not needed for the task on which the model was trained. We demonstrate models can encode these properties considerably above chance-level even when distributed in the data as random noise.
  arXiv  Detail & Related papers  (2020-05-02T06:19:20Z)
• Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision [57.14468881854616]
  We propose an auxiliary training objective that improves the generalization capabilities of neural networks. We use pairs of minimally-different examples with different labels, a.k.a counterfactual or contrasting examples, which provide a signal indicative of the underlying causal structure of the task. Models trained with this technique demonstrate improved performance on out-of-distribution test sets.
  arXiv  Detail & Related papers  (2020-04-20T02:47:49Z)
• Information-Theoretic Probing for Linguistic Structure [74.04862204427944]
  We propose an information-theoretic operationalization of probing as estimating mutual information. We evaluate on a set of ten typologically diverse languages often underrepresented in NLP research.
  arXiv  Detail & Related papers  (2020-04-07T01:06:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.