Researchers are exploring how the internal mechanisms of large language models, particularly attention functions, influence their behavior in novel contexts. One study proposes a "Mixture of Function Attention" (MoFA) that assigns fixed ratios of softmax and sigmoid heads, finding this ratio has minimal impact on in-distribution tasks but significantly affects out-of-distribution performance, separating text types based on the ratio. Another paper surveys the evolution of attention in LLMs, analyzing developments in contextual memory through lenses like memory representation, update, access, readout, and integration, highlighting how depth and heterogeneous architectures coordinate memory processing. A third study investigates how different language models redistribute attention-head activity under serial demand, revealing distinct patterns across models like Qwen2.5 and Llama, suggesting this distribution offers a new comparison metric. AI
IMPACT These studies offer insights into how attention mechanisms function and vary across models, potentially guiding future architectural improvements for better generalization and efficiency.
RANK_REASON Cluster consists of three research papers discussing different aspects of attention mechanisms in language models.
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- arXiv
- Dong Gyun Kang
- GPT-2
- Hugging Face
- large-language models
- llama
- Llama 3.1 70B
- Mixture of Function Attention
- OLMo-2
- Qwen2.5
- Qwen2.5-72B
- self-attention
- sigmoid function
- Softmax
- transformer
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