R e CE val: Evaluating Reasoning Chains via Correctness and Informativeness (2023)

Abstract: Multi-step reasoning ability is fundamental to many natural language tasks, yet it is unclear what constitutes a good reasoning chain and how to evaluate them. Most existing methods focus solely on whether the reasoning chain leads to the correct conclusion, but this answer-oriented view may confound reasoning quality with other spurious shortcuts to predict the answer. To bridge this gap, we evaluate reasoning chains by viewing them as informal proofs that derive the final answer. Specifically, we propose ReCEval (Reasoning Chain Evaluation), a framework that evaluates reasoning chains via two key properties: (1) correctness, i.e., each step makes a valid inference based on information contained within the step, preceding steps, and input context, and (2) informativeness, i.e., each step provides new information that is helpful towards deriving the generated answer. We evaluate these properties by developing metrics using natural language inference models and \mathcal{V} -Information. On multiple datasets, we show that ReCEval effectively identifies various error types and yields notable improvements compared to prior methods. We analyze the impact of step boundaries, and previous steps on evaluating correctness and demonstrate that our informativeness metric captures the expected flow of information in high-quality reasoning chains. Finally, we show that scoring reasoning chains based on ReCEval improves downstream task performance.

## introduction the ability to reason is fundamental to many natural language processing tasks @xcite . a reasoning chain composes multiple reasoning steps together to accomplish an end task such as solving complex reasoning problems involving commonsense @xcite @xcite and arithmetic @xcite . question: what keeps the moon orbiting earth? context: the moon is a kind of moon. earth is a kind of planet. moons orbit planets. gravity causes orbits. ## reasoning chains: preliminaries in this section, we formally define the concepts of reasoning chains, rcus, and v-information. ## properties of good reasoning chains reasoning chains are informal proofs leading to the final answer. we propose evaluating their quality based on correctness and informativeness. correctness. for a reasoning chain to be correct, every step must be correct. further, we say a step s ( next, we describe the technical details of our metrics that evaluate every reasoning step by itself (intra-step correctness), how it relates to the input and prior steps (inter-step correctness), and how it aids in solving the problem (informativeness). ## receval: evaluation metrics we now introduce our receval (reasoning chain evaluation) framework that builds upon the desired properties of reasoning chains. ## meta-evaluation setup we evaluate a metric's ability to detect errors in reasoning chains using the meta-evaluation framework used by @xcite . for each error category, we compute the correlation between ground-truth annotations ( §5.1) and metrics ( §5.2). ## related work traditional text generation evaluation metrics use n-gram overlap @xcite @xcite , embeddings @xcite @xcite , information alignment @xcite , paraphrases @xcite , or textgeneration models @xcite , and are suitable for comparing machinegenerated text to target text in tasks like summarization and machine translation. however, they are inadequate for evaluating reasoning chains with a coherent sequence of steps leading to the final answer. additionally, relying on references makes them unsuitable for reference-free evaluation. some prior works on evaluating reasoning chains propose metrics based on specific construction and domain of datasets, making them less generalizable. for example, folio @xcite and prontoqa (saparov and he, 2023) use a fixed grammar to convert natural language reasoning chains to symbolic proofs that are evaluated using gold proofs. @xcite compare model-generated reasoning trees to gold reasoning trees. closest to our work, @xcite proposed roscoe, a suite of reference-free and reference-based metrics that measure semantic alignment, similarity, and logical inference in reasoning chains. building upon their work, we first formally define desired properties of good reasoning chains (i.e., correctness and informativeness) and then propose reference-free metrics (using rcus and v-information) that outperform roscoe across datasets. ## conclusion we present receval, a framework for evaluating reasoning chains based on correctness and informativeness. we propose reference-free metrics for measuring these properties that are based on entailment and pvi, leveraging granular claims in reasoning chains called reasoning content units (rcus). our approach considerably outperforms previous baseline metrics, as shown by meta-evaluation on multiple datasets. we also perform detailed analysis of our metrics and demonstrate that receval is effective in various settings, and leads to downstream improvement in task performance.