Summary of Language Models Need Inductive Biases to Count Inductively, by Yingshan Chang and Yonatan Bisk
Language Models Need Inductive Biases to Count Inductively
by Yingshan Chang, Yonatan Bisk
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 A machine learning paper investigates the ability of various architectures to generalize counting, a fundamental example of inductive reasoning. The study focuses on transformer models and their reliance on positional embeddings for out-of-domain (OOD) counting. The authors experiment with different architectures, including RNNs, transformers, state-space models, and RWKV, and design task formats and auxiliary tasks to avoid limitations in generalization. The results show that traditional RNNs trivially achieve inductive counting, while transformers require positional embeddings for OOD counting. This finding has implications for the application scope of primitive functions defined in formal characterizations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Counting is a basic skill that involves learning to recognize and remember numbers in sequence. In this paper, researchers looked at how well different machine learning models can learn to count and generalize it to new situations. They used various models like RNNs (recurrent neural networks), transformers, and others to test their abilities. The results showed that some models are better than others at counting and generalizing it. This is important because it affects how we use these models in real-life applications. |
Keywords
» Artificial intelligence » Generalization » Machine learning » Transformer
