Project MPG: towards a generalized performance quotient for LLM intelligence
Keywords: evaluation, LLM, LMSys, benchmarks
TL;DR: We present a new, short benchmark spanning the space of existing benchmarks that correlates well with LMSys and produces meaningful model differentiation..
Abstract: There exists an extremely wide array of LLM benchmarking tasks, whereas oftentimes a single number is the most actionable for decision making, especially by non-experts. No such aggregation schema exists that is not Elo based, which could be costly or time consuming. Here we propose a method to aggregate performance across a general space of benchmarks, nicknamed Project “MPG”, here dubbed Model Performance and Goodness, in addition referencing a metric widely understood to be an important yet inaccurate and crude measure of car performance. Here, we create two numbers: an ``Goodness'' number (answer accuracy), and a “Fastness” number (cost or QPS). We compare models against each other and present a ranking according to our general metric as well as subdomains. We find significant agreement between the raw pearson correlation of our scores and thosee of LMSys, even improving on the correlation of the MMLU leaderboard to LMSys.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 12571
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