Reimagining Attention: Why Contribution Weights Matter in LLMs

understanding the inner workings of large language models (LLMs), the spotlight often falls on attention weights. But is that the best metric we can use? The short answer is no. Traditional attention weights have significant blind spots, particularly in overlooking the geometric aspects of aggregated value vectors.

Introducing Contribution Weights

Enter Contribution Weights, a new player on the scene that promises to redefine how we interpret token importance. This projection-based metric doesn't just consider a token's attention weight. It takes into account its value magnitude and how well it's directionally aligned with the layer output. It's a more comprehensive measure, and the results aren't just theoretical. In practical tests, Contribution Weights consistently outperform attention-based metrics in pinpointing semantically important tokens across various decoder-only models, tasks, and datasets.

More Than Just a Metric

Why should we care about another metric in an already saturated field? Because it offers the potential to unlock new insights into how these models function. Take attention sinks, for example. Historically, these have been seen as passive repositories, places where excess attention goes to die. But with Contribution Weights, a different picture emerges. Attention sinks aren't just passive. They play an active role in managing information flow, suppressing noise, and stabilizing representations by counteracting semantic drift in low-confidence tokens. It's a revelation.

Implications and Questions

So what does this mean for the future of LLMs? Well, it challenges us to rethink how we evaluate token significance. If Contribution Weights can offer a more accurate picture, why aren't we using them as the standard? The intersection is real. Ninety percent of the projects aren't. But those that are, like this, demand attention.

this shift has broader implications. As we continue to develop models that are increasingly agentic, the need for precise interpretability grows. If the AI can hold a wallet, who writes the risk model? We need metrics that can keep up with the sophistication of emerging models.

In the end, Contribution Weights are more than just a metric. They're a step toward a deeper understanding of machine learning, offering insights that could guide the next wave of AI development. Show me the inference costs. Then we'll talk. Until then, Contribution Weights are a fascinating leap forward.