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Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?
Abhilasha Ravichander, Yonatan Belinkov, Eduard Hovy
TL;DR
The paper examines whether information recoverable by probing sentence representations was actually used to perform the training task. Using controlled NLI datasets, embedding comparisons, and synthetic tasks, it finds that unnecessary properties can be encoded and that pretrained embeddings contribute substantially to this pattern. These findings limit what probing accuracy alone can establish about model reliance.
Problem
It is unclear whether linguistic information recoverable from sentence representations by probing was actually used by the model to perform its task.
Method
The paper uses controlled NLI datasets, controls for lexical memorization and task relevance, compares pretrained and scratch-trained embeddings, and tests controlled synthetic scenarios.
Results
Models reliably encode linguistic properties even when those properties are unnecessary for the task, with pretrained word embeddings contributing to this encoding and synthetic random-noise properties remaining extractable.
Takeaways & Limitations
Probing accuracy alone cannot determine whether a property is needed for a task or identify the information a model relies on.
Takeaways & Limitations
The exact linguistic competencies required by most real-world datasets, and the extent to which they are required, are difficult to isolate and quantify.
Abstract
from arXiv · showhide
Although neural models have achieved impressive results on several NLP benchmarks, little is understood about the mechanisms they use to perform language tasks. Thus, much recent attention has been devoted to analyzing the sentence representations learned by neural encoders, through the lens of `probing' tasks. However, to what extent was the information encoded in sentence representations, as discovered through a probe, actually used by the model to perform its task? In this work, we examine this probing paradigm through a case study in Natural Language Inference, showing that models can learn to encode linguistic properties even if they are not needed for the task on which the model was trained. We further identify that pretrained word embeddings play a considerable role in encoding these properties rather than the training task itself, highlighting the importance of careful controls when designing probing experiments. Finally, through a set of controlled synthetic tasks, we demonstrate models can encode these properties considerably above chance-level even when distributed in the data as random noise, calling into question the interpretation of absolute claims on probing tasks.
1 Introduction
The paper asks whether probing accuracy shows that a linguistic property is used for the task, and tests this question with controlled datasets and synthetic scenarios. It finds that properties can be encoded even when unnecessary, with pretrained embeddings contributing to this effect.
- Probing examines whether linguistic properties can be recovered from frozen neural sentence representations using a separate classifier.High probing performance is commonly treated as evidence that the property is encoded in the representation.
- Controlled task versions show that linguistic properties can be reliably recovered even when models cannot use them to perform the task.The control datasets make the entailment decision independent of the selected linguistic property.
- Pretrained word embeddings contribute to encoding some linguistic properties, potentially conflating embedding training with the main task in probing experiments.The paper identifies the interaction between these two training sources as a confound.
- Even with embeddings trained from scratch, models encode linguistic properties that are not required for the task.This result indicates that incidental encoding is not limited to pretrained embeddings.
- In controlled synthetic settings, models can encode information distributed as random noise with respect to the training task.The paper uses this scenario to motivate caution when interpreting absolute probing results.
2 Background and Related Work
The background situates probing among methods for testing whether models represent linguistic competencies, while emphasizing that extractability does not establish task use. The paper targets this interpretive limitation as distinct from debates about probe expressivity.
- Benchmark datasets serve as proxies for practical tasks and intended competencies, but their quality as proxies is difficult to estimate.This motivates analyses of what linguistic information models represent and use.
- Diagnostic examples measure task accuracy on linguistically annotated samples, but models may succeed through spurious correlations rather than the intended competency.This limits what diagnostic accuracy alone can establish.
- Probing instead tests whether learned representations contain linguistic properties through auxiliary prediction tasks or diagnostic classifiers.Prior work has applied this approach across tasks including machine translation and natural language inference.
- This paper argues that probing cannot reliably indicate which information models rely on, because properties may be incidentally encoded even when unnecessary.The argument addresses interpretation of model reasoning rather than the expressivity of probing classifiers.
3 Methodology
The study constructs controlled NLI and probing settings to test whether linguistic properties are encoded when they are not needed for the main task. It uses fixed-property datasets, multiple encoder architectures, and lexical controls to isolate probing behavior.
- Control datasets: Control datasets fix one linguistic property across every example, making that property non-discriminative of the task label in isolation.The procedure partitions existing datasets by tense or subject/object number while preserving a large training set where possible.
- Auxiliary tasks: The experiments probe TENSE, SUBJECT NUMBER, and OBJECT NUMBER using binary sentence-level prediction tasks.These properties concern the main verb, the subject of the main clause, and the direct object, respectively.
- Auxiliary tasks: Lexical items associated with probing targets are excluded across train, development, and test splits to control for memorization of target word types.The procedure follows the study’s control of lexical memorization in probing datasets.
- Control datasets: Premise-hypothesis pairs are partitioned into MultiNLI-PastTense, MultiNLI-SingularSubject, and MultiNLI-SingularObject control tasks.Each dataset fixes the property value yielding the maximum number of training instances: past tense, singular subject, or singular object.
- Models: Models use CBOW and BiLSTM sentence encoders, with BiLSTM representations formed from the last, average, or maximum hidden states.NLI models combine premise and hypothesis vectors before classification, and trained sentence vectors are subsequently probed.
- Scope: The study notes that its English-specific heuristics and MultiNLI setting leave recent transformer-based architectures for future work.The authors nevertheless present the study as evidence that probing performance does not correlate with task importance.
4 Probing the Probing Paradigm
Controlled NLI experiments show that linguistic properties can remain highly probeable even when the training task does not require them. Pretrained embeddings contribute substantially to this information, but above-baseline encoding persists with random initialization.
- 4.1 Probing with Linguistic Controls: Up to ∼80% probing accuracy is observed when models train on tasks that do not require the probed linguistic property.Task-controlled models perform comparably to models trained on downsampled MultiNLI when evaluated on the corresponding controlled probing data.
- 4.1 Probing with Linguistic Controls: Lexical splitting prevents lexical memorization from explaining why linguistic properties remain encoded in sentence representations.The probing data separates lexical targets across partitions.
- 4.2 Effect of Word Embeddings: The study compares pretrained and randomly initialized embeddings to separate embedding pretraining from the main task’s contribution to representations.Both embedding types are updated during task-specific training.
- 4.2 Effect of Word Embeddings: Probing performance decreases sharply when pretrained word embeddings are replaced with randomly initialized embeddings.This comparison indicates that pretrained embeddings contribute considerably to probing performance beyond what the task-specific training learns.
- 4.3 How do models encode linguistic properties?: Even with randomly initialized embeddings, linguistic properties remain encoded to a similar extent and above the majority baseline in control and downsampled tasks.These results indicate that encoding is not eliminated when the contribution of pretrained embeddings is neutralized.
5 A Synthetic Experiment: Analyzing Encoding Dynamics
The synthetic experiment tests whether models encode a task-irrelevant property under controlled correlations, finding above-chance probing even when the property is random noise. It also examines how representation and probe capacity affect this incidental encoding.
- Models encode linguistic properties even when those properties are not required for the main task.
- Synthetic task: The experiment uses synthetic string-classification tasks in which character c is inserted under noise, uncorrelated, partial, or full correlations with task decisions.
- Results: Probing classifiers predict the presence of c considerably above chance across all four datasets, including when c is distributed as random noise.
- Representation capacity: Lower-capacity representations reduce probing accuracy while leaving main-task accuracy consistent across representation sizes.
- Probe capacity: Higher-capacity probing classifiers do not necessarily produce higher probing accuracy, while above-chance probing persists across classifier capacities.
6 Discussion
The discussion argues that probing accuracy cannot establish which information a model uses for a task and emphasizes controls, baselines, and controlled tests. It also identifies limits in measuring task requirements and in treating datasets as complete task proxies.
- Probing detects encoded information but cannot determine whether the probed property is needed for the task or isolate the precise correlated property.
- Careful controls and baselines: High probing accuracy may reflect incidental encoding or interactions between training tasks rather than information attributable directly to the training task.
- Task requirements: Real-world datasets lack gold-standard measurements of which linguistic competencies tasks require and to what extent.
- Dataset proxies: Datasets may omit task competencies and contain unwanted biases or correlations because they are imperfect proxies for tasks.
- What probes are good for: When representations are used downstream, probing can provide insight into encoded information regardless of how that encoding arose.
7 Conclusion
The paper concludes that probing is useful for studying representations but cannot by itself identify the information models rely on to perform tasks. It highlights future work on task competencies, model-used information, and dataset adequacy.
- Controlled experiments show that linguistic properties can be encoded incidentally even when they are unnecessary for the main task.
- The paper calls for methods to identify required dataset competencies, the exact information models use, and how encoded information is used downstream.
A Adversarial Learning Framework
The framework uses adversarial training to suppress task-irrelevant linguistic information while preserving main-task performance, then tests whether attackers can still recover that information. It also examines how adversary capacity affects suppression and reports persistent recoverability, including for random noise.
- The synthetic setup targets the presence of ‘c’ as task-irrelevant information and evaluates whether suppression preserves task performance while reducing recoverability.The synthetic entailment rule depends on whether premise and hypothesis begin with the same letter, not on the presence of ‘c’.
- Adversarial learning trains sentence encoders to perform NLI without using selected linguistic-property labels, while an adversarial classifier predicts those properties from representations.A gradient reversal layer preserves the forward pass but reverses and scales gradients during backpropagation.
- The evaluation compares model task accuracy, adversarial-classifier accuracy, and attacker accuracy across noise, uncorrelated, partial, and full synthetic tasks.The attacker is evaluated on held-out data, with examples unseen during main-task training to prevent leakage.
- Attackers can extract irrelevant properties despite adversarial suppression, and no adversary weight λ suppresses the attribute when it is distributed as random noise.The parameter λ controls the extent of attempted property suppression.
- Varying adversarial-classifier capacity can decrease attacker accuracy, but the effective capacity depends on the experimental setup.The comparison uses one- and two-layer MLP adversaries with dimensions from 100 to 10,000 units.
- The authors caution that comparable task accuracy may be unattainable in practice, so failure to suppress a property cannot establish that the property is relevant.Other constraints include limited probing-classifier recoverability and the fact that adversarial learning does not remove all representational information.