Summary of A Mechanistic Interpretation Of Syllogistic Reasoning in Auto-regressive Language Models, by Geonhee Kim et al.
A Mechanistic Interpretation of Syllogistic Reasoning in Auto-Regressive Language Models
by Geonhee Kim, Marco Valentino, André Freitas
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The paper explores how Language Models (LMs) reason logically and whether they learn systematic principles or simply exploit patterns in training data. The authors propose a methodology for discovering circuits that interpret content-independent reasoning mechanisms within LMs. They demonstrate two intervention methods, uncovering a middle-term suppression circuit responsible for deriving valid conclusions from premises. Additionally, the study examines belief biases in syllogistic reasoning and finds evidence of partial contamination from attention heads encoding commonsense knowledge. The authors investigate generalization across various schemes, model sizes, and architectures, concluding that LMs learn transferable content-independent reasoning mechanisms, but these are susceptible to contamination by world knowledge acquired during pre-training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language Models (LMs) can reason logically, but how do they do it? This paper helps us understand the internal workings of LMs. The authors find a special “circuit” inside LMs that makes them good at drawing conclusions from premises. They also show that this circuit is not perfect and can be influenced by what the model learned during training. Overall, this study shows that LMs are capable of logical reasoning, but they don’t necessarily use the same rules we do. |
Keywords
» Artificial intelligence » Attention » Generalization
