<ul><li>A new study titled 'Graph-KV' introduces a method to inject structural biases into large language models.</li><li>The Graph-KV approach leverages the KV-cache of text segments to allow for interaction governed by structural inductive biases, improving tasks like retrieval-augmented generation.</li><li>By selectively attending only to designated source segments, Graph-KV induces a graph-structured block mask, sparsifying attention and enabling a message-passing-like step within the language model.</li><li>Evaluated across various benchmarks and tasks, Graph-KV outperforms baseline methods by effectively reducing positional bias and utilizing structural inductive biases.</li></ul>

Graph-KV: Breaking Sequence via Injecting Structural Biases into Large Language Models

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