Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries

• URL: http://arxiv.org/abs/2409.12640v2
• Date: Fri, 20 Sep 2024 00:47:33 GMT
• Title: Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries
• Authors: Kiran Vodrahalli, Santiago Ontanon, Nilesh Tripuraneni, Kelvin Xu, Sanil Jain, Rakesh Shivanna, Jeffrey Hui, Nishanth Dikkala, Mehran Kazemi, Bahare Fatemi, Rohan Anil, Ethan Dyer, Siamak Shakeri, Roopali Vij, Harsh Mehta, Vinay Ramasesh, Quoc Le, Ed Chi, Yifeng Lu, Orhan Firat, Angeliki Lazaridou, Jean-Baptiste Lespiau, Nithya Attaluri, Kate Olszewska,
• Abstract summary: We introduce Michelangelo: a minimal, synthetic, and unleaked long-context reasoning evaluation for large language models. This evaluation is derived via a novel, unifying framework for evaluations over arbitrarily long contexts.
• Score: 54.325172923155414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Michelangelo: a minimal, synthetic, and unleaked long-context reasoning evaluation for large language models which is also easy to automatically score. This evaluation is derived via a novel, unifying framework for evaluations over arbitrarily long contexts which measure the model's ability to do more than retrieve a single piece of information from its context. The central idea of the Latent Structure Queries framework (LSQ) is to construct tasks which require a model to ``chisel away'' the irrelevant information in the context, revealing a latent structure in the context. To verify a model's understanding of this latent structure, we query the model for details of the structure. Using LSQ, we produce three diagnostic long-context evaluations across code and natural-language domains intended to provide a stronger signal of long-context language model capabilities. We perform evaluations on several state-of-the-art models and demonstrate both that a) the proposed evaluations are high-signal and b) that there is significant room for improvement in synthesizing long-context information.

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