On the Consistency of Commonsense in Large Language Models (2025)

Abstract: Commonsense, humans’ implicit understanding of everyday situations, is crucial for large language models (LLMs). Existing commonsense evaluations for LLMs primarily focus on downstream knowledge tasks, failing to probe whether LLMs truly understand and utilize knowledge or merely memorize it. They also rely heavily on human annotation and lack automated large-scale data generation. To address this, we propose to automatically construct a large benchmark named CoCo (Consistency of Commonsense) comprising 39K samples derived from commonsense knowledge graphs (CSKGs), paired with symbolic questions and ground-truth answers, which systematically assesses LLMs’ knowledge memorization, comprehension, and application and examines the consistency between these tasks. To enhance our evaluation, we also propose novel metrics and prompting strategies. Experimental results on multiple LLMs reveal that CoCo presents significant challenges, and our detailed analysis provides deeper insights into the strengths and limitations of LLMs’ commonsense abilities.

## introduction commonsense refers to widely shared basic knowledge, which llms are believed to encode significantly during pre-training @xcite @xcite . previous commonsense evaluations @xcite @xcite only focus on commonsense assessment in llms using public benchmarks. while these evaluations rank overall performance, they lack clear definitions and divisions of evaluated abilities. additionally, they inevitably face data contamination and hallucination risks-public benchmarks may leak into pre-training @xcite , and correct responses might result from memorization rather than * corresponding author true understanding and reasoning @xcite @xcite . recent knowledge evaluations @xcite @xcite have begun refining ability definitions and addressing data contamination. for instance, kola @xcite introduces a cognitive ability taxonomy and diverse data sources to mitigate contamination, while charm @xcite examines the link between memorization and reasoning. although knowledge memorization is separated from higher-level abilities, its vague definition and granularity make llms' reasoning errors hard to explain. moreover, these methods heavily rely on human annotation and lack scalable dataset generation. to this end, we propose to automatically generate large-scale evaluation datasets based on the structured knowledge in commonsense knowledge graphs (cskgs) @xcite @xcite . using cskgs as an evaluation data source offers unique advantages. they support hierarchical tasks like knowledge retrieval and multi-hop reasoning, enabling different abilities assessment while reducing data leakage bias. cskgs also facilitate automated multi-level data generation through logical queries and help track whether llms follow correct reasoning paths by comparing them with golden chains, aiding error analysis. we follow kola @xcite to design our benchmark considering three key factors: ability modeling, data and evaluation criteria. for ability modeling, we evaluate commonsense knowledge of llms and divide our commonsense evaluation task with three subtasks, memorization, comprehension, and application, as shown in figure 1 . unlike previous benchmarks @xcite @xcite that rely on existing disparate datasets, we leverage consistency and establish an intrinsic connection between memorization and other tasks, similar to charm @xcite . however, charm focuses solely head: personx played a football game relation: xeffect tail: feel tired head: personx feels tired relation: xwant tail: take a rest head: personx is on vocation relation: xeffect tail: feel relaxed commonsense knowledge graph question: what is the effect on personx after personx playing a football game? memorization task question: what is the effect on personx after personx playing tiring events? comprehension task question: what event or state is what personx wants to do after the effect on personx after playing a football game? application task subquestion: what is the effect on personx after personx playing a football game? playing a football @xmath0: what is the effect on personx after personx playing a football game? subquestion: what does personx want to do after personx feeling tired? on the link between reasoning and memorization, while its manual annotation of required knowledge for reasoning questions is labor-intensive and often incomplete, as multiple solutions may exist, introducing biases in knowledge-reasoning correlation analysis. in contrast, we start by testing memorization with atomic knowledge from cskgs, then evaluate comprehension and application, effectively reversing charm's process and providing the foundation for assessing higher-level abilities. for data, for reducing manual annotation, we introduce the coco (consistency of commonsense) dataset. its specificity is that commonsense questions posed in natural language are grounded in cskgs. by sampling triples from cskgs, our symbolic questions and answers are then verbalized to natural language @xcite . we compose more than 39k commonsense questions across three rungs, giving rise to scenarios which require different commonsense abilities. moreover, instead of eliminating data contamination and hallucination, which is challenging or impossible, evaluating the consistency of commonsense in llms mitigates their effects by aligning memorized samples with internal knowledge. for evaluation criteria, we design a consistent evaluation system with specialized metrics for the three tasks, guided by the principle of consistency. traditional benchmarks report absolute metrics for each task separately, overlooking their interconnections and mutual influences @xcite . for example, using standard accuracy to evaluate reasoning can be affected by data contamination @xcite and knowledge gaps @xcite . and charm only shows the overall correlation between knowledge and reasoning results but lacks specific metrics to evaluate individual samples. we therefore propose new metrics to measure comprehension and application based on memorization. we perform extensive experiments on seven llms and discover that coco is in general very challenging for llms. exploiting coco, we also introduce a method to elicit consistent commonsense reasoning in llms. specifically, we develop knowcot, a chain-of-thought prompting strategy @xcite inspired by the knowledge storage and manipulation in llms @xcite , which prompts the llm to recall relevant knowledge, and perform consistent commonsense reasoning. our experiments indicate that knowcot substantially improves the consistency performance of llms especially gpt-4 @xcite on coco. we also analyze fine-grained errors to showcase the limitations of llms in commonsense knowledge and reasoning. ## preliminary commonsense knowledge in cskgs. denote the commonsense knowledge triples in the cskg as @xmath1, r, t) | @xmath2, @xmath3, @xmath4, where h, r, and t are the set of heads, relations, and tails in the cskg. each element @xmath5, e.g., (personx is on vacation, xeffect, feel relaxed), is a specific piece of knowledge, which can be expressed by various records, e.g., a text record "per-sonx is on vacation, as a result, personx will feel relaxed." we term such triple as a piece of atomic knowledge which is the foundation for abstract knowledge acquisition and multi-hop reasoning. ## coco benchmark task formulation. we formulate the proposed task in the form of multiple choice question answering (mcqa). our dataset @xmath6, a i } n @xmath7, each containing a question set q i , and an answer set a i . our main task is to test the accuracy of the prediction function m : @xmath8, i.e., an llm which maps natural language questions to the corresponding answers: construction process. we use atomic @xcite as the atomic knowledge source. the generation pipeline of coco is shown in figure 2 . here we briefly introduce this construction process. please refer to appendix a for more details. the memorization task involves sampling and representing the atomic knowledge triples from atomic. this is achieved by selecting 2k diverse triples for each of the nine relations in atomic. diversity is ensured by embedding triples using sentence-bert @xcite and constructing a graph where nodes represent triples, and edges connect the most similar ones based on cosine similarity. a scoring mechanism prioritizes diversity by penalizing overly similar triples @xcite . during verbalization, each triple is transformed into mcqa form with four distractors: two random ones from the cskg and two adversarial ones sampled from related triples. the comprehension task extends atomic knowledge by abstracting it into higher-level concepts through conceptualization. using the same diversity mechanism as before, 20k head events are sampled. conceptualizations for each head event are generated by a gpt-2 @xcite model fine-tuned on abstractatomic @xcite , a corpus of abstract commonsense knowledge. multiple candidates are filtered by vera @xcite , a t5 @xcite )-based plausibility scorer, which removes low-plausibility triples. head events with at least three valid conceptualizations are retained, yielding 39k conceptualized triples for 13k head events. these triples are then verbalized into mcqa pairs. the application task broadens reasoning by requiring inferences over multiple pieces of atomic knowledge. logical queries are sampled from normalized atomic @xcite , with tail entities adjusted for consistency. for each query type (i.e., 2i, 2p, ip and pi), 3k diverse instances are sampled, avoiding over representation of high-degree nodes. distractors are carefully designed to challenge reasoning without ambiguity. queries and answers are verbalized using templates @xcite , and refined with vera @xcite remove flawed samples. ## evaluation methods evaluation setup. we instruct llms to answer questions from three sets: q m , q c , and q a , with their predicted answer sets defined as: memorization. accuracy is used as the evaluation metric for the memorization task. let x ⊂ d be the memorization subset, and m be an llm to be evaluated. consider a response comprehension. let y ⊂ d be the comprehension subset, and m be an llm to be evaluated. consider a response p @xmath9, a c ) ∈ y, we define a new metric for comprehension evaluation. the key idea is that if an llm comprehends a certain atomic knowledge, then it is likely to master the corresponding conceptualizations. the comscore of m is: application. let z ⊂ d be the application subset, and m be an llm to be evaluated. consider a response p @xmath10, a a ) ∈ z, we consider two conditions: (1) the llm answers the question correctly (i.e., p (k 1 ,...,kn) = a (k 1 ,...,kn) ); ## knowcot prompting in order to guide llms in correctly answering the questions in coco and improve their consistency of commonsense knowledge, we develop knowcot, a multi-step chain-of-thought prompt in figure 3 . given a commonsense question q, we provide the llm a list of instructions: @xmath11, s 2 , s 3 ) consisting of the detailed descriptions of the three question: what event or state is what personx wants to do after the effect on personx after playing a football game? a: personx wants to take a rest. b: personx wants to eat. c: personx feels sad. d: personx gets hurt. e: personx leaves. extract the core event. guidance: address the question by following the steps below: recall the relevant knowledge. ## experiments 6.1 experimental setup we use popular api-based and open-source llms as baselines, including mistral (jiang et al., 2023), llama @xcite , qwen @xcite , gpt-3 @xcite and gpt-4 @xcite , with various parameter sizes. besides the vanilla evaluation, we also evaluate llms using popular zero-shot cot @xcite and our knowcot prompting strategies. note that we do not conduct few-shot experiments due to the bias of sample selection on the final evaluation results. the complete list of model versions is shown in ## conclusion we introduce coco, a large-scale benchmark for commonsense consistency, featuring automatic construction, novel evaluation metrics, and prompting methods. extensive experiments on current llms assess their performance in memorization, comprehension, and application of commonsense knowledge. our findings show a significant gap between llms and humans, and we provide a detailed analysis of the challenges llms face and potential improvement directions.