The paper "In-Context Language Learning: Architectures and Algorithms" by Ekin Akyürek, Bailin Wang, Yoon Kim, and Jacob Andreas from MIT CSAIL explores the concept of in-context language learning (ICLL) in large-scale neural language models (LMs). The authors introduce a new family of model problems called in-context language learning (ICLL), where LMs are presented with a set of strings from a formal language and must generate additional strings from the same language. This problem is designed to be simple enough to study in small-scale LMs but complex enough to capture the key features of ICL in large-scale LMs. The paper focuses on ICLL for regular languages generated by random finite automata. It evaluates a variety of neural sequence models, including RNNs, Transformers, and state-space model variants, on regular ICLL tasks. The main questions addressed are: (1) which model classes are capable of ICLL, (2) what algorithmic solutions do successful models implement, and (3) what architectural changes can improve ICLL in less performant models. Key findings include: 1. **Model Classes**: Transformers significantly outperform other neural sequence models, including recurrent and convolutional models, on ICLL tasks. 2. **Algorithmic Solutions**: Transformers develop specialized "n-gram heads" that compute input-conditional next-token distributions, which are crucial for their performance. 3. **Architectural Changes**: Hard-wiring n-gram heads into RNNs and convolutional models improves their performance on ICLL tasks, and these improvements extend to natural language modeling tasks, reducing perplexity by up to 6.7% on the SlimPajama dataset. The paper highlights the usefulness of ICLL as a tool for understanding ICL in natural text models and suggests that it can be extended to more expressive languages, offering insights into more complex ICL behaviors in real models.The paper "In-Context Language Learning: Architectures and Algorithms" by Ekin Akyürek, Bailin Wang, Yoon Kim, and Jacob Andreas from MIT CSAIL explores the concept of in-context language learning (ICLL) in large-scale neural language models (LMs). The authors introduce a new family of model problems called in-context language learning (ICLL), where LMs are presented with a set of strings from a formal language and must generate additional strings from the same language. This problem is designed to be simple enough to study in small-scale LMs but complex enough to capture the key features of ICL in large-scale LMs. The paper focuses on ICLL for regular languages generated by random finite automata. It evaluates a variety of neural sequence models, including RNNs, Transformers, and state-space model variants, on regular ICLL tasks. The main questions addressed are: (1) which model classes are capable of ICLL, (2) what algorithmic solutions do successful models implement, and (3) what architectural changes can improve ICLL in less performant models. Key findings include: 1. **Model Classes**: Transformers significantly outperform other neural sequence models, including recurrent and convolutional models, on ICLL tasks. 2. **Algorithmic Solutions**: Transformers develop specialized "n-gram heads" that compute input-conditional next-token distributions, which are crucial for their performance. 3. **Architectural Changes**: Hard-wiring n-gram heads into RNNs and convolutional models improves their performance on ICLL tasks, and these improvements extend to natural language modeling tasks, reducing perplexity by up to 6.7% on the SlimPajama dataset. The paper highlights the usefulness of ICLL as a tool for understanding ICL in natural text models and suggests that it can be extended to more expressive languages, offering insights into more complex ICL behaviors in real models.