\(\texttt{accord}\): Closing The Commonsense Measurability Gap
Roewer-després François, Feng Jinyue, Zhu Zining, Rudzicz Frank. Arxiv 2024
[Paper]
We present \(\texttt{ACCORD}\), a framework and benchmark suite for disentangling the commonsense grounding and reasoning abilities of large language models (LLMs) through controlled, multi-hop counterfactuals. \(\texttt{ACCORD}\) introduces formal elements to commonsense reasoning to explicitly control and quantify reasoning complexity beyond the typical 1 or 2 hops. Uniquely, \(\texttt{ACCORD}\) can automatically generate benchmarks of arbitrary reasoning complexity, and so it scales with future LLM improvements. Benchmarking state-of-the-art LLMs – including GPT-4o (2024-05-13), Llama-3-70B-Instruct, and Mixtral-8x22B-Instruct-v0.1 – shows performance degrading to random chance with only moderate scaling, leaving substantial headroom for improvement. We release a leaderboard of the benchmark suite tested in this work, as well as code for automatically generating more complex benchmarks.
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