Adaptive Contextual Attention Gating (ACAG) for Large Language Models

Author(s): [Your Name or Org]
License: MIT
Status: Research Prototype — Contributions Welcome 🚀

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📌 Overview

🚀 Adaptive Contextual Attention Gating (ACAG)

Context‑Aware, Efficient Attention for Long‑Context & Few‑Shot Transformers

ACAG is a novel attention mechanism for Transformer‑based Large Language Models (LLMs) that dynamically adapts to context length, attention weight distribution, and task type.
It’s built to boost efficiency, accuracy, and scalability in:

• Long‑context LLMs (legal docs, scientific papers, codebases)
• Few‑shot reasoning and prompt‑based learning
• Memory‑efficient Transformer architectures

🔹 Why ACAG?
Standard Transformer attention treats all tokens equally — wasting compute on irrelevant spans.
ACAG introduces a context‑length‑aware gating function that:

• Prioritizes salient long‑range dependencies
• Reduces FLOPs & memory footprint
• Improves few‑shot performance without retraining from scratch

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🎯 Motivation

Modern Transformer attention mechanisms process all tokens with equal weight, regardless of their relevance to the task.
This becomes inefficient — and sometimes harmful — in scenarios such as:

• Long‑sequence modeling (e.g., legal documents, scientific literature, large codebases) where only a fraction of the context is truly important.
• Few‑shot prompts where critical information is sparsely distributed across the input.
• High‑latency or memory‑constrained environments where every FLOP and MB of VRAM counts.

Adaptive Contextual Attention Gating (ACAG) addresses these challenges by:

• Measuring context length and attention weight distribution per attention head in real time.
• Dynamically scaling attention outputs to prioritize salient, long‑range dependencies while suppressing noise.
• Reducing unnecessary computation for irrelevant context spans, improving both speed and memory efficiency.
• Preserving or improving few‑shot reasoning accuracy without retraining the entire model.

By making attention context‑aware and resource‑efficient, ACAG enables scalable Transformer architectures that can handle 8K+ token sequences and complex reasoning tasks without prohibitive compute costs.

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🧠 Key Features

• Context‑Aware Gating — Learns to adjust attention strength dynamically based on sequence length and attention weight distribution.
• Head‑Specific Control — Each attention head has independent gating parameters for fine‑grained optimization.
• Plug‑and‑Play Integration — Drop‑in replacement for MultiHeadAttention in Hugging Face Transformers, GPT‑Neo, and other PyTorch architectures.
• Few‑Shot Optimization — Improves performance on sparse, high‑value context retrieval tasks without additional fine‑tuning.
• Scalable to Long Contexts — Efficiently handles sequences of 8K, 16K, or more tokens without prohibitive compute costs.
• Memory‑Efficient — Reduces GPU/TPU memory usage, enabling larger batch sizes or longer sequences on the same hardware.
• Research‑Ready — Modular design for experimentation with gating functions, scaling laws, and attention head specialization.

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📐 Architecture

The gating function ( g(c, h) ) takes:

• ( c ): normalized context length.
• ( \mu_A, \sigma_A ): mean and std of attention weights for the head.

[ g(c, h) = \sigma(W_h \cdot [c, \mu_A, \sigma_A] + b_h) ]

Final gated output: [ O' = g(c, h) \odot O ]

Where:

• ( O ) = standard attention output.
• ( \odot ) = elementwise multiplication.

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💻 Installation

git clone https://github.com/yourusername/adaptive-contextual-attention-gating.git
cd adaptive-contextual-attention-gating
pip install -r requirements.txt

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🚀 Quick Start

import torch
from acag import AdaptiveContextualAttention

# Initialize ACAG attention
attn = AdaptiveContextualAttention(d_model=768, num_heads=12, max_len=2048)

# Example input: batch=2, seq_len=1024, hidden_dim=768
x = torch.randn(2, 1024, 768)
out = attn(x)

print("Output shape:", out.shape)

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📊 Benchmarks (Planned)

We will evaluate ACAG on:

• Long‑context QA: NarrativeQA, GovReport.
• Few‑shot reasoning: BIG‑Bench, MMLU.
• Efficiency metrics: FLOPs, latency, memory footprint.

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🧪 Prototype Plan

1. Start with EleutherAI/gpt-neo as baseline.
2. Replace vanilla attention with ACAG.
3. Fine‑tune on mixed‑length datasets.
4. Compare against Longformer, BigBird, and vanilla GPT‑Neo.

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📈 Roadmap

• Implement head‑wise and element‑wise gating variants.
• Add sparse MoE integration.
• Release pre‑trained ACAG‑LLM checkpoints.
• Publish arXiv paper with results.

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🤝 Contributing

We welcome:

• Pull requests for new gating strategies.
• Benchmark results on additional datasets.
• Visualization tools for gated attention maps.

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📚 References

• Vaswani et al., Attention Is All You Need — arXiv:1706.03762
• Brown et al., Language Models are Few‑Shot Learners — arXiv:2005.14165

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