Recent causal probing literature reveals when language models and syntactic probes use similar representations . Such techniques may yield " false negative " causality results : models may use representations of syntax , but probes may have learned to use redundant encodings of the same syntactic information . We demonstrate that models do encode syntactic information redundantly and introduce a new probe design that guides probes to consider all syntactic information present in embeddings . Using these probes , we find evidence for the use of syntax in models where prior methods did not , allowing us to boost model performance by injecting syntactic information into representations . Here , we identify a limitation of prior causal probing art in which redundant information in embeddings could lead to probes and models using different representations of the same information , which in turn could lead to uninformative causal analysis results . We propose a new probe architecture that addresses this limitation by encouraging probes to use all sources of information in embeddings . Recent large neural models like BERT and GPT-3 exhibit impressive performance on a large variety of linguistic tasks , from sentiment analysis to question-answering ( Devlin et al . , 2019 ; Brown et al . , 2020 ) . Given the models ' impressive performance , but also their complexity , researchers have developed tools to understand what patterns models have learned . In probing literature , researchers develop " probes : " models designed to extract information from the representations of trained models ( Linzen et al . , 2016 ; Conneau et al . , 2018 ; Hall Maudslay et al . , 2020 ) . For example , Hewitt and Manning ( 2019 ) demonstrated that one can train accurate linear classifiers to predict syntactic structure from BERT or ELMO embeddings . These probes reveal what information is present in model embeddings but not how or if models use that information ( Belinkov , 2021 ) . To address this gap , new research in causal analysis seeks to understand how aspects of models ' representations affect their behavior ( Elazar et al . , 2020 ; Ravfogel et al . , 2020 ; Giulianelli et al . , 2018 ; Tucker et al . , 2021 ; Feder et al . , 2021 ) . Typically , these techniques create counterfactual representations that differ from the original according to some Figure 1 : In a 2D embedding space , a model might redundantly encode syntactic representations of a sentence like " the girl saw the boy with the telescope . " Redundant encodings could cause misalignment between the model 's decision boundary ( blue ) and a probe 's ( red ) . We introduce dropout probes ( green ) to use all informative dimensions . property ( e.g. , syntactic interpretation of the sentence ) . Researchers then compare outputs when using original and counterfactual embeddings to assess whether a property encoded in the representation is causally related to model behavior . Unfortunately , negative results -wherein researchers report that models do not appear to use a property causally -are difficult to interpret . Such failures can be attributed to a model truly not using the property ( true negatives ) , or to a failure of the technique ( false negatives ) . For example , as depicted in Figure 1 , if a language model encodes syntactic information redundantly ( here illustrated in two-dimensions ) , the model and probe may differentiate among parses along orthogonal dimensions . When creating counterfactual representations with such probes , researchers could incorrectly conclude that the model does not use syntactic information . In this work , we present new evidence for the causal use of syntactic representations on task performance in BERT , using newly-designed probes that take into account the potential redundancy in a model 's internal representation . First , we find evidence for representational redundancy in BERTbased models . Based on these findings , we propose a new probe design that encourages the probe to use all relevant representations of syntax in model embeddings . These probes are then used to assess if language models use representations of syntax causally , and , unlike prior art , we find that some fine-tuned models do exhibit signatures of causal use of syntactic information . Lastly , having found that these models causally use representations of syntax , we used our probes to boost a questionanswering model 's performance by " injecting " syntactic information at test time.1 2 Related Work In this work , we designed and evaluated " dropout probes , " a new neural probing architecture for generating useful causal analysis of trained language models . Our technical contribution -adding a dropout layer before probes -was inspired by a theory of redundant syntactic encodings in models . Our results fit within three categories : we showed that 1 ) models encoded syntactic information redundantly , 2 ) dropout probes , unlike standard probes , revealed that QA models used syntactic representations causally , and 3 ) by injecting syntactic information at test time in syntacticallychallenging domains , we could increase model performance without retraining . Despite our step towards better understanding of pretrained models , future work remains . Natural extensions include studying pretrained models beyond those considered in this work , further research into redundancy in embeddings , more investigation into inserting symbolic knowledge into neural representations , and new methods for training models to respond appropriately to interventions .

Challenge: Redundant encodings of syntactic information can make existing probing techniques for causal use of syntactic knowledge by language models provide misleading false negative results.
Approach: They first show that models redundantly encode information and propose a dropout-based probing technique that considers all syntactic information in embeddings.
Outcome: They find evidence of models using syntactic information not identified by previous methods by using the proposed technique and further improve the QA model's performance.