Summary of Latent Causal Probing: a Formal Perspective on Probing with Causal Models Of Data, by Charles Jin et al.
Latent Causal Probing: A Formal Perspective on Probing with Causal Models of Data
by Charles Jin, Martin Rinard
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Probing classifiers have become a crucial technique to understand how language models (LMs) work, particularly with their increasing performance on various NLP tasks. The typical setup involves defining an auxiliary task with labeled data and training small classifiers to predict the labels from the representations of a pre-trained LM as it processes the dataset. A high probing accuracy indicates that the LM has learned to perform the auxiliary task as an unsupervised byproduct of its original pre-training objective. Despite their widespread use, however, designing and analyzing probing experiments remains challenging. This paper develops a formal perspective on probing using structural causal models (SCM), framing the central hypothesis as whether the LM has learned to represent the latent variables of the SCM. Empirically, it extends a recent study of LMs in a synthetic grid-world navigation task, demonstrating the ability of LMs to induce latent concepts underlying text. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about understanding how language models work. They’re special computers that can understand and generate human-like language. To figure out what they’ve learned, researchers use something called “probing” – it’s like asking a question to see if the model knows the answer. The problem is that it’s hard to design good experiments to test this. This paper uses a new way of thinking about probing, called structural causal models (SCMs). It shows that language models can learn to understand complex concepts just by being trained on lots of text. |
Keywords
» Artificial intelligence » Nlp » Unsupervised
