Gating Gone Wild: Systematic Exploration of Anomalous Gating Effects Across Model Scales and Tasks

TL;DR: What if gating doesn’t always behave as expected? Let’s systematically hunt for weird or inconsistent effects of sigmoid gates in attention—especially as models get bigger or tasks change—and see what surprises emerge. An initial experiment could compare the variance in gating-induced attention patterns and performance across a sweep of model sizes (from small to massive) and across a diverse set of benchmarks (e.g., long-context retrieval, reasoning, summarization).

Research Question: How do the effects of post-SDPA sigmoid gating vary across different model sizes, architectures, and task types, and are there unexpected anomalies or breakdowns in their purported benefits (e.g., mitigating attention sinks) in particular regimes?

Hypothesis: While gating consistently helps in most settings, there exist “regimes of anomaly”—specific model scales, architectural choices, or task types—where gating can backfire, induce new biases, or interact non-trivially with attention sinks and long-context performance.

Experiment Plan: - Construct a grid of experiments varying model size (hundreds of millions to tens of billions), architecture (dense, MoE, encoder-only, encoder-decoder), and task type (retrieval, summarization, code generation, citation tracking).

• For each configuration, systematically measure: attention distributions (especially for ‘sink’ tokens), gating activation statistics, and performance on lost-in-the-middle and context faithfulness benchmarks (see Hsieh et al., 2024; Tang et al., 2024).
• Analyze for outliers and deviations: Are there settings where gating reduces performance, increases bias, or fails to mitigate attention sinks?
• Explore possible causes (e.g., interaction with model depth, training dynamics, or data domain).

References:

• Hsieh, C.-Y., Chuang, Y.-S., Li, C.-L., Wang, Z., Le, L. T., Kumar, A., Glass, J., Ratner, A., Lee, C.-Y., Krishna, R., & Pfister, T. (2024). Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization. Annual Meeting of the Association for Computational Linguistics.
• Tang, Z., Zhou, K., Li, J., Ji, B., Hou, J., & Zhang, M. (2024). L-CiteEval: Do Long-Context Models Truly Leverage Context for Responding? arXiv.org.
• Zhang, H. (2024). SinkLoRA: Enhanced Efficiency and Chat Capabilities for Long-Context Large Language Models. arXiv.org.