Counterfactually Probing Language Identity in Multilingual Models

• URL: http://arxiv.org/abs/2310.18862v1
• Date: Sun, 29 Oct 2023 01:21:36 GMT
• Title: Counterfactually Probing Language Identity in Multilingual Models
• Authors: Anirudh Srinivasan, Venkata S Govindarajan, Kyle Mahowald
• Abstract summary: We use AlterRep, a method of counterfactual probing, to explore the internal structure of multilingual models. We find that, given a template in Language X, pushing towards Language Y systematically increases the probability of Language Y words.
• Score: 15.260518230218414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Techniques in causal analysis of language models illuminate how linguistic information is organized in LLMs. We use one such technique, AlterRep, a method of counterfactual probing, to explore the internal structure of multilingual models (mBERT and XLM-R). We train a linear classifier on a binary language identity task, to classify tokens between Language X and Language Y. Applying a counterfactual probing procedure, we use the classifier weights to project the embeddings into the null space and push the resulting embeddings either in the direction of Language X or Language Y. Then we evaluate on a masked language modeling task. We find that, given a template in Language X, pushing towards Language Y systematically increases the probability of Language Y words, above and beyond a third-party control language. But it does not specifically push the model towards translation-equivalent words in Language Y. Pushing towards Language X (the same direction as the template) has a minimal effect, but somewhat degrades these models. Overall, we take these results as further evidence of the rich structure of massive multilingual language models, which include both a language-specific and language-general component. And we show that counterfactual probing can be fruitfully applied to multilingual models.

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