Summary of Teaching Transformers Causal Reasoning Through Axiomatic Training, by Aniket Vashishtha et al.
Teaching Transformers Causal Reasoning through Axiomatic Training
by Aniket Vashishtha, Abhinav Kumar, Abbavaram Gowtham Reddy, Vineeth N Balasubramanian, Amit Sharma
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 Medium Difficulty Summary: This paper explores the capabilities of text-based AI systems in performing causal reasoning tasks. The authors investigate whether an agent can learn this skill from passive data without actively generating interventional data. They introduce a novel axiomatic training setup, where the agent learns from multiple demonstrations of causal axioms or rules, rather than incorporating them as biases or inferring them from data values. The paper demonstrates that a transformer model, trained on axiom demonstrations for small graphs, can generalize to larger graphs and even more complex scenarios. For instance, the model is capable of applying transitivity over longer causal chains and branching graphs without explicit training. The results are comparable to those of larger language models like GPT-4, Gemini Pro, and Phi-3. This axiomatic training framework offers a new paradigm for learning causal reasoning from passive data, enabling the acquisition of arbitrary axioms as long as sufficient demonstrations can be generated. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: Imagine AI systems that can understand cause-and-effect relationships in the real world. Right now, making these systems is difficult because we don’t have enough data to teach them. This paper shows how AI agents can learn to reason about causes and effects without needing a lot of special training data. Instead, they can learn from examples of rules that explain what happens when one thing affects another. The authors test their idea with a powerful language model that can apply these rules to new situations, even if it hasn’t seen them before. This is important because it could help AI systems make more informed decisions and interact better with the world. |
Keywords
» Artificial intelligence » Gemini » Gpt » Language model » Transformer
