On the Consistency of Commonsense in Large Language Models
Authors: Guozheng Li, Peng Wang, Wenjun Ke, Zijie Xu, Jiajun Liu, Ziyu Shang
Accepted at: acl25

On the Consistency of Commonsense in Large Language Models

Guozheng Li, Peng Wang*, Wenjun Ke, Zijie Xu, Jiajun Liu, Ziyu Shang

Southeast University

Data: https://github.com/liguozheng/CoCo

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.

1 Introduction

Commonsense refers to widely shared basic knowledge, which LLMs are believed to encode significantly during pre-training (Madaan et al., 2022; Jain et al., 2023; Zhao et al., 2023b). Previous commonsense evaluations (Zhou et al., 2020; Li et al., 2022; Cheng et al., 2024) 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 (Huang et al., 2023b), and correct responses might result from memorization rather than true understanding and reasoning (Ji et al., 2023; Huang et al., 2023a; Wang et al., 2024b).

Recent knowledge evaluations (Yu et al., 2024; Wang et al., 2024a; Fei et al., 2024; Sun et al., 2024) have begun refining ability definitions and addressing data contamination. For instance, KoLA (Yu et al., 2024) introduces a cognitive ability taxonomy and diverse data sources to mitigate contamination, while CHARM (Sun et al., 2024) 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) (Speer et al., 2017; Sap et al., 2019a; Hwang et al., 2021). 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 (Yu et al., 2024) 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 (Yu et al., 2024; Wang et al., 2024a; Fei et al., 2024) that rely on existing disparate datasets, we leverage consistency and establish an intrinsic connection between memorization and other tasks, similar to CHARM (Sun et al., 2024). However, CHARM focuses solely
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 (Shen et al., 2023; Fang et al., 2024). 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 (Yu et al., 2024). For example, using standard Accuracy to evaluate reasoning can be affected by data contamination (Ji et al., 2023) and knowledge gaps (Sun et al., 2024). 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 (Wei et al., 2022) inspired by the knowledge storage and manipulation in LLMs (Allen-Zhu and Li, 2023), 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 (Achiam et al., 2023) on CoCo. We also analyze fine-grained errors to showcase the limitations of LLMs in commonsense knowledge and reasoning.

2 Preliminary

Commonsense Knowledge in CSKGs. Denote the commonsense knowledge triples in the CSKG as \( \mathcal{K} = \{ k = (h, r, t) | h \in \mathcal{H}, r \in \mathcal{R}, t \in \mathcal{T} \} \), where \( \mathcal{H} \), \( \mathcal{R} \), and \( \mathcal{T} \) are the set of heads, relations, and tails in the CSKG. Each element \( k \in \mathcal{K} \), 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 “\( PersonX \) 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.

Knowledge Memorization. Given an LLM denoted as \( \mathcal{M} \), we formulate that \( \mathcal{M} \) memorize com-

![Examples of CoCo. CoCo consists interconnected memorization, comprehension and application tasks.](page_186_153_1077_377.png)

Figure 1: Examples of CoCo. CoCo consists interconnected memorization, comprehension and application tasks.
monsense knowledge \( k = (h, r, t) \) if \( \mathcal{M} \) can correctly answer the corresponding question \( q_{k \setminus t} \):

\[
\mathcal{M}(q_{k \setminus t}) = t
\]

where \( t \in \mathcal{T} \), \( q_{k \setminus t} \) is a record about knowledge \( k \) that lacks pivot information \( t \). Taking Figure 1 as an example, \( q_{k \setminus t} \) is “What is the effect on PersonX after PersonX playing a football game?”. Then we drop \( \setminus t \) and use only \( q_k \) for simplicity. Formally, given an atomic knowledge triple \( k \), an LLM \( \mathcal{M} \) is expected to answer question \( q_k \) with \( \mathcal{M}(q_k) = t \).

Knowledge Comprehension. The triples sampled from CSKGs can be used to directly evaluate knowledge memorization. However, rote memorization does not necessarily mean comprehension. In Figure 1, understanding that playing football leads to feeling tired involves recognizing it as a physically demanding activity. If LLMs truly comprehend, they should generalize the knowledge to infer concepts like “Tiring events such as sports and exercise can make someone feel tired”. Thus the acquired abstract commonsense knowledge can be used to evaluate comprehension. Deriving such knowledge from CSKGs involves conceptualization (He et al., 2024). The objective of conceptualization is to form a conceptualized head event, denoted as \( h^c \), from the original head \( h \). This is achieved by linking a component \( o \subseteq h \) to a concept \( c \), forming \( h^c \) by replacing \( o \) with \( c \). Thus abstract knowledge is formed by combining the conceptualized head event with the original relation and tail, represented by \( k^c = (h^c, r, t) \). Formally, given an atomic knowledge triple \( k \), its conceptualized triple is denoted as \( k^c \). An LLM \( \mathcal{M} \) is expected to answer question \( q_{k^c} \) with \( \mathcal{M}(q_{k^c}) = t \). The prerequisite is the LLM memorizes \( k \).

Knowledge Application. For application evaluation, LLMs are expected to answer commonsense reasoning questions, provided that they have mastered all the necessary atomic knowledge to answer this question. We therefore leverage the concept of logical queries (Hamilton et al., 2018) to acquire large-scale complex reasoning data from CSKGs which requires minimum human efforts (Fang et al., 2024). The query structures (\( 2i, 2p, ip \) and \( pi \)) that we study in this work are introduced in Appendix A.3. Figure 1 illustrates an example of \( 2p \). If LLMs memorize two atomic knowledge triples (\( PersonX played a football game, xEffect, feel tired \)) and (\( PersonX feels tired, xWant, take a rest \)), we expect LLMs to correctly answer the reasoning question constructed by the logical query “What event or state is what PersonX wants to do after the effect on PersonX after playing a football game?”. Formally, given several atomic knowledge triples \( k_1, ..., k_n \), an LLM \( \mathcal{M} \) is expected to answer the question \( q_{(k_1,...,k_n)} \) with \( \mathcal{M}(q_{(k_1,...,k_n)}) = t \). The prerequisite is the LLM memorizes \( k_1, ..., k_n \).

3 CoCo Benchmark

Task Formulation. We formulate the proposed task in the form of Multiple Choice Question Answering (MCQA). Our dataset \( \mathcal{D} = \{ Q_i, A_i \}_{i=1}^N \) consists of \( N \) pairs, 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 \( \mathcal{M} : Q \mapsto A \), i.e., an LLM which maps natural language questions to the corresponding answers:

\[
\begin{align*}
Q_m &= \{ q_k \}, A_m = \{ a_k \} \\
Q_c &= \{ q_k, q_{k_1^c}, ..., q_{k_m^c} \}, A_c = \{ a_k, a_{k_1^c}, ..., a_{k_m^c} \} \\
Q_a &= \{ q_{(k_1,...,k_n)}, q_{k_1}, ..., q_{k_n} \}, \\
A_a &= \{ a_{(k_1,...,k_n)}, a_{k_1}, ..., a_{k_n} \}
\end{align*}
\]

where \( Q_m, Q_c, Q_a \) and \( A_m, A_c, A_a \) are question sets and answer sets for memorization, compre-
hension and application evaluation, respectively. Note \( m \) represents the number of conceptualization operations, and \( n \) represents the number of atomic knowledge involved in a reasoning question. The correct answer corresponding to the multiple choice option \( a_k \in \{ \mathbf{A}, \mathbf{B}, \mathbf{C}, \mathbf{D}, \mathbf{E} \} \) is \( t \).

Construction Process. We use ATOMIC (Sap et al., 2019a) 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 (Reimers and Gurevych, 2019) 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 (Su et al., 2023). 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 (Radford et al., 2019) model fine-tuned on ABSTRACTATOMIC (He et al., 2024), a corpus of abstract commonsense knowledge. Multiple candidates are filtered by Vera (Liu et al., 2023), a T5 (Raffel et al., 2020)-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 (Shen et al., 2023), 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 (Fang et al., 2024), and refined with Vera (Liu et al., 2023) to remove flawed samples.

Dataset Statistics. Our data generating procedure is able to algorithmically generate a vast number of questions. In practice, we pick a dataset size that is large enough to be representative, but not too large to be problematic given the expensive inference costs of LLMs. We set our dataset size to be 39K. The dataset roughly balances across the relation and query types, as shown in Table 1.

<table>
  <tr>
    <th>Task Type</th>
    <th>Aspects</th>
    <th>Question Type</th>
    <th># Sets</th>
    <th># Words / Set</th>
    <th># Questions / Set</th>
  </tr>
  <tr>
    <td rowspan="6">Memorization</td>
    <td>oEffect</td>
    <td>Single Test</td>
    <td>2,000</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>oReact</td>
    <td>Single Test</td>
    <td>2,000</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>oWant</td>
    <td>Single Test</td>
    <td>2,000</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>xAttr</td>
    <td>Single Test</td>
    <td>2,000</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>xEffect</td>
    <td>Single Test</td>
    <td>2,000</td>
    <td>66.29</td>
    <td>1.00</td>
  </tr>
  <tr>
    <td>xIntent</td>
    <td>Single Test</td>
    <td>2,000</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td rowspan="6">Comprehension</td>
    <td>xEffect</td>
    <td>i+3 Joint Test</td>
    <td>2,158</td>
    <td>268.68</td>
    <td>4.00</td>
  </tr>
  <tr>
    <td>xIntent</td>
    <td>i+3 Joint Test</td>
    <td>1,299</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>xNeed</td>
    <td>i+3 Joint Test</td>
    <td>1,040</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>xReact</td>
    <td>i+3 Joint Test</td>
    <td>1,104</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>xWant</td>
    <td>i+3 Joint Test</td>
    <td>2,894</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td rowspan="4">Application</td>
    <td>2i</td>
    <td>2+1 Joint Test</td>
    <td>2,490</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>2p</td>
    <td>2+1 Joint Test</td>
    <td>2,125</td>
    <td>258.05</td>
    <td>3.42</td>
  </tr>
  <tr>
    <td>ip</td>
    <td>3+1 Joint Test</td>
    <td>1,998</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>pi</td>
    <td>3+1 Joint Test</td>
    <td>1,329</td>
    <td></td>
    <td></td>
  </tr>
</table>

Table 1: Overview of CoCo. The sample (question set) numbers of memorization, comprehension and application tasks are 18,000, 13,060 and 7,942, respectively. Aspects include the relation types in ATOMIC and different query types. For comprehension, 1+3 Joint Test represents 1 memorization test and 3 conceptualization tests. For application, 2+1 (3+1) Joint Test represents 2 (3) memorization tests and 1 reasoning test.

Quality Check. Our dataset is generated algorithmically, which has the following potential benefits: formal correctness, zero human annotation cost, and, most importantly, controllability (e.g., for the question distribution, as well as for making it more unlikely that the data was previously seen by LLMs). However, since our dataset is different from common NLP datasets collected from human natural language writing, we also need to perform additional data quality checks. We therefore checked for a list of natural language properties. For grammatically, we ran a grammatical error check using LanguageTool (Naber et al., 2003), and got on average 1.47 grammatical errors per 100 words (i.e., 98.53% correctness), showing most of the language in CoCo follows English grammar. For human readability, we checked how comprehensible the questions are to average persons. We selected 100 questions from CoCo, and let an undergraduate student annotator go through the ques-
tions to judge whether they could understand or not, where 94% of the questions were deemed readable. Lastly, we conducted a sanity check where one author of this paper tried to solve a random sample of 100 questions from the dataset, and we recorded an accuracy of 87% on this task.

4 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:

\[
\begin{align*}
\mathcal{P}_m &= \{p_k\} \\
\mathcal{P}_c &= \{p_k, p_{k_1}, ..., p_{k_m}\} \\
\mathcal{P}_a &= \{p_{(k_1,...,k_n)}, p_{k_1}, ..., p_{k_n}\}
\end{align*}
\]

where we set \( m = 3, n \in \{2, 3\} \). We evaluate LLMs by comparing \( \mathcal{P}_m, \mathcal{P}_c, \mathcal{P}_a \) and \( \mathcal{A}_m, \mathcal{A}_c, \mathcal{A}_a \).

Memorization. Accuracy is used as the evaluation metric for the memorization task. Let \( \mathcal{X} \subset \mathcal{D} \) be the memorization subset, and \( \mathcal{M} \) be an LLM to be evaluated. Consider a response \( \mathcal{P}_m = \mathcal{M}(Q_m) \) for \( (Q_m, \mathcal{A}_m) \in \mathcal{X} \), the MEMSCORE of \( \mathcal{M} \) is:

\[
\text{MEMSCORE}(\mathcal{M}) = \frac{1}{|\mathcal{X}|} \sum_{(Q_m, \mathcal{A}_m) \in \mathcal{X}} \mathbb{1}_{p_k = a_k}
\]

where \( |\mathcal{X}| \) is the number of samples in the dataset. MEMSCORE simply describes the capabilities of LLMs to memorize atomic knowledge.

Comprehension. Let \( \mathcal{Y} \subset \mathcal{D} \) be the comprehension subset, and \( \mathcal{M} \) be an LLM to be evaluated. Consider a response \( \mathcal{P}_c = \mathcal{M}(Q_c) \) for \( (Q_c, \mathcal{A}_c) \in \mathcal{Y} \), 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 \( \mathcal{M} \) is:

\[
\text{COMSCORE}(\mathcal{M}) =
\frac{1}{\sum_{(Q_c, \mathcal{A}_c) \in \mathcal{Y}} \mathbb{1}_{p_k = a_k}} \sum_{(Q_c, \mathcal{A}_c) \in \mathcal{Y}} \frac{\mathbb{1}_{p_k = a_k} \sum_{s=1}^m \mathbb{1}_{p_{k_s} = a_{k_s}}}{m}
\]

where \( |\mathcal{Y}| \) is the dataset size, and \( m \) denotes conceptualization operations. The first term reflects the extent of atomic knowledge memorized by \( \mathcal{M} \), while the second measures its mastery of related conceptualizations. COMSCORE evaluates LLMs’ capabilities to comprehend abstract concepts.

Application. Let \( \mathcal{Z} \subset \mathcal{D} \) be the application subset, and \( \mathcal{M} \) be an LLM to be evaluated. Consider a response \( \mathcal{P}_a = \mathcal{M}(Q_a) \) for \( (Q_a, \mathcal{A}_a) \in \mathcal{Z} \), we consider two conditions: (1) the LLM answers the question correctly (i.e., \( p_{(k_1,...,k_n)} = a_{(k_1,...,k_n)} \)); (2) the LLM memorizes all the atomic knowledge (i.e., \( p_{k_s} = a_{k_s}, \forall s \in [1, n] \)). Generally, the overall reasoning performance of \( \mathcal{M} \) is defined as follows:

\[
\text{REASCORE}(\mathcal{M}) = \frac{1}{|\mathcal{Z}|} \sum_{(Q_a, \mathcal{A}_a) \in \mathcal{Z}} \mathbb{1}_{p_{(k_1,...,k_n)} = a_{(k_1,...,k_n)}}
\]

where \( |\mathcal{Z}| \) is the dataset size. While REASCORE assesses overall performance, it cannot evaluate an LLM’s ability to avoid hallucination or utilize knowledge. For the first case, a correct answer with partial atomic knowledge may result from data contamination or hallucination. Thus we define the FAISCORE to measure the faithfulness of \( \mathcal{M} \):

\[
\text{FAISCORE}(\mathcal{M}) =
\frac{\sum_{(Q_a, \mathcal{A}_a) \in \mathcal{Z}} \mathbb{1}_{p_{(k_1,...,k_n)} = a_{(k_1,...,k_n)}} \cdot \mathbb{1}_{p_{k_s} = a_{k_s}, \forall s \in [1,n]}}{\sum_{(Q_a, \mathcal{A}_a) \in \mathcal{Z}} \mathbb{1}_{p_{(k_1,...,k_n)} = a_{(k_1,...,k_n)}}}
\]

where the denominator represents the number of questions correctly answered by \( \mathcal{M} \), while the numerator counts those correctly answered whose required atomic knowledge are memorized. For the second case, we define another metric APPSCORE:

\[
\text{APPSCORE}(\mathcal{M}) =
\frac{\sum_{(Q_a, \mathcal{A}_a) \in \mathcal{Z}} \mathbb{1}_{p_{k_s} = a_{k_s}, \forall s \in [1,n]} \cdot \mathbb{1}_{p_{(k_1,...,k_n)} = a_{(k_1,...,k_n)}}}{\sum_{(Q_a, \mathcal{A}_a) \in \mathcal{Z}} \mathbb{1}_{p_{k_s} = a_{k_s}, \forall s \in [1,n]}}
\]

where the denominator represents the number of samples with all atomic knowledge memorized by \( \mathcal{M} \), while the numerator counts questions correctly answered by \( \mathcal{M} \). APPSCORE reflects an LLM’s ability to answer reasoning questions using all required atomic knowledge, with a higher score indicating stronger knowledge utilization.

5 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: \( l = (s_1, s_2, s_3) \) 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.
Guidance: Address the question by following the steps below:
Step 1 Extract the core event.
The core event is “PersonX plays a football game”.
Step 2 Recall the relevant knowledge.
(PersonX plays a football game, causes, fatigue)
Step 3 Deduce the final result.
After PersonX playing a football game, it often leads to fatigue, and the final event most likely to be “PersonX wants to take a rest”.
Based on all the reasoning above, select the correct option to answer the initial question. A: PersonX wants to take a rest.

Figure 3: Illustration of KnowCoT. Compared with directly prompting the LLMs questions, we impose an inductive bias upon LLMs by explicitly recalling relevant knowledge, thus improving the comprehension and application.

steps. As the model \( f_{LLM} : s_i \mapsto r_i \) produces responses \( r_1, r_2, r_3 \) sequentially corresponding to the three steps, we concatenate all the above before asking the final question “Based on all the reasoning above, select the correct option to answer the initial question.” See the complete prompt in Appendix B.

6 Experiments

6.1 Experimental Setup

We use popular API-based and open-source LLMs as baselines, including Mistral (Jiang et al., 2023), Llama (Dubey et al., 2024), Qwen (Yang et al., 2024), GPT-3 (Brown et al., 2020) and GPT-4 (Achiam et al., 2023), with various parameter sizes. Besides the vanilla evaluation, we also evaluate LLMs using popular zero-shot CoT (Kojima et al., 2022) 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 Table 2 and experimental details can be found in Appendix C.

<table>
  <tr>
    <th>Models</th>
    <th>Is Open</th>
    <th>Main Language</th>
    <th>Size</th>
  </tr>
  <tr>
    <td>Mistral-7B-Instruct-v0.3</td>
    <td>✓</td>
    <td>en</td>
    <td>7B</td>
  </tr>
  <tr>
    <td>Llama-3-BB-Instruct</td>
    <td>✓</td>
    <td>en</td>
    <td>8B</td>
  </tr>
  <tr>
    <td>Qwen2.5-7B-Instruct</td>
    <td>✓</td>
    <td>zh</td>
    <td>7B</td>
  </tr>
  <tr>
    <td>Llama2-13B-Chat</td>
    <td>✓</td>
    <td>en</td>
    <td>13B</td>
  </tr>
  <tr>
    <td>Qwen2.5-14B-Instruct</td>
    <td>✓</td>
    <td>zh</td>
    <td>14B</td>
  </tr>
  <tr>
    <td>GPT-3.5-turbo</td>
    <td>✓</td>
    <td>en</td>
    <td>175B</td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>✓</td>
    <td>en</td>
    <td>>175B</td>
  </tr>
</table>

Table 2: LLMs evaluated in our experiments.

6.2 Main Results

Table 3 presents the main results of LLMs on CoCo, where we have the following findings.

Overall, CoCo presents a significant challenge for all LLMs. GPT-4 achieves the highest performance across five dimensions. However, despite its advancements, a substantial performance gap of 17.7% still exists between the most capable LLM and human performance. Notably, the gap widens to 38.5% on FA1SCORE, indicating that LLMs struggle significantly with maintaining internal consistency. This suggests that while LLMs excel in reasoning tasks, they still face fundamental limitations in aligning their responses with coherent and logically consistent knowledge structures.

As model scale decreases, its knowledge reservoir shrinks, leading to gradual performance degradation. In memorization, GPT-4 falls behind human performance by only 9.8%, highlighting its extensive internal knowledge retention. This suggests that larger-scale models can store and retrieve commonsense knowledge more effectively. In contrast, Mistral-7B and Qwen2.5-7B exhibit the weakest performance in knowledge memorization, reflecting the limitations of smaller models in capturing and recalling vast amounts of knowledge.

LLMs that achieve good performance in memorization and comprehension may exhibit performance degradation in application. For instance, LLaMA outperforms Mistral by an absolute aver-

<table>
  <tr>
    <th>Models</th>
    <th>Methods</th>
    <th>Mem.</th>
    <th>CoCo.</th>
    <th>Rea.</th>
    <th>FAt.</th>
    <th>Ave.</th>
    <th>Average</th>
  </tr>
  <tr>
    <td rowspan="3">Human</td>
    <td><i>Sampling Test</i></td>
    <td>90.53</td>
    <td>94.43</td>
    <td>87.80</td>
    <td>90.25</td>
    <td>93.46</td>
    <td>91.29</td>
  </tr>
  <tr>
    <td><i>Vanilla</i></td>
    <td>60.88</td>
    <td>78.67</td>
    <td>52.35</td>
    <td>42.94</td>
    <td>68.18</td>
    <td>60.60</td>
  </tr>
  <tr>
    <td><i>Cot</i></td>
    <td>62.77</td>
    <td>79.35</td>
    <td>52.82</td>
    <td>44.34</td>
    <td>69.65</td>
    <td>61.79</td>
  </tr>
  <tr>
    <td rowspan="3">Mistral-7B</td>
    <td><i>Vanilla</i></td>
    <td>61.55</td>
    <td>79.93</td>
    <td>54.32</td>
    <td>45.91</td>
    <td>71.48</td>
    <td>62.64</td>
  </tr>
  <tr>
    <td><i>Cot</i></td>
    <td>63.74</td>
    <td>80.66</td>
    <td>50.53</td>
    <td>40.37</td>
    <td>61.21</td>
    <td>59.30</td>
  </tr>
  <tr>
    <td><i>KnowCoT</i></td>
    <td>64.26</td>
    <td>80.89</td>
    <td>50.88</td>
    <td>42.29</td>
    <td>62.89</td>
    <td>60.24</td>
  </tr>
  <tr>
    <td rowspan="3">Llama3-8B</td>
    <td><i>Vanilla</i></td>
    <td>63.88</td>
    <td>81.26</td>
    <td>52.87</td>
    <td>43.81</td>
    <td>64.59</td>
    <td>61.28</td>
  </tr>
  <tr>
    <td><i>Cot</i></td>
    <td>59.52</td>
    <td>79.37</td>
    <td>48.76</td>
    <td>40.18</td>
    <td>58.52</td>
    <td>57.27</td>
  </tr>
  <tr>
    <td><i>KnowCoT</i></td>
    <td>58.35</td>
    <td>80.24</td>
    <td>48.95</td>
    <td>41.16</td>
    <td>58.97</td>
    <td>57.53</td>
  </tr>
  <tr>
    <td rowspan="3">Qwen2.5-7B</td>
    <td><i>Vanilla</i></td>
    <td>60.87</td>
    <td>80.90</td>
    <td>51.37</td>
    <td>43.06</td>
    <td>60.94</td>
    <td>59.43</td>
  </tr>
  <tr>
    <td><i>Cot</i></td>
    <td>66.48</td>
    <td>82.74</td>
    <td>56.16</td>
    <td>44.00</td>
    <td>68.26</td>
    <td>63.53</td>
  </tr>
  <tr>
    <td><i>KnowCoT</i></td>
    <td>65.25</td>
    <td>82.86</td>
    <td>57.00</td>
    <td>45.32</td>
    <td>70.75</td>
    <td>64.24</td>
  </tr>
  <tr>
    <td rowspan="3">Llama2-13B</td>
    <td><i>Vanilla</i></td>
    <td>66.42</td>
    <td>83.52</td>
    <td>58.73</td>
    <td>46.97</td>
    <td>71.43</td>
    <td>65.41</td>
  </tr>
  <tr>
    <td><i>Cot</i></td>
    <td>67.83</td>
    <td>81.22</td>
    <td>56.99</td>
    <td>44.10</td>
    <td>69.36</td>
    <td>63.90</td>
  </tr>
  <tr>
    <td><i>KnowCoT</i></td>
    <td>68.00</td>
    <td>81.88</td>
    <td>58.20</td>
    <td>45.53</td>
    <td>70.52</td>
    <td>64.83</td>
  </tr>
  <tr>
    <td rowspan="3">Qwen2.5-14B</td>
    <td><i>Vanilla</i></td>
    <td>68.95</td>
    <td>82.31</td>
    <td>59.87</td>
    <td>48.87</td>
    <td>73.04</td>
    <td>66.70</td>
  </tr>
  <tr>
    <td><i>Cot</i></td>
    <td>75.25</td>
    <td>85.96</td>
    <td>62.87</td>
    <td>46.97</td>
    <td>72.38</td>
    <td>68.69</td>
  </tr>
  <tr>
    <td><i>KnowCoT</i></td>
    <td>77.62</td>
    <td>85.20</td>
    <td>65.78</td>
    <td>49.52</td>
    <td>74.96</td>
    <td>70.62</td>
  </tr>
  <tr>
    <td rowspan="3">GPT-3.5-turbo</td>
    <td><i>Vanilla</i></td>
    <td>75.78</td>
    <td>86.25</td>
    <td>67.19</td>
    <td>51.63</td>
    <td>76.07</td>
    <td>71.38</td>
  </tr>
  <tr>
    <td><i>Cot</i></td>
    <td>79.81</td>
    <td>87.37</td>
    <td>65.16</td>
    <td>49.68</td>
    <td>75.12</td>
    <td>71.43</td>
  </tr>
  <tr>
    <td><i>KnowCoT</i></td>
    <td>81.54</td>
    <td>88.63</td>
    <td>66.64</td>
    <td>52.57</td>
    <td>76.17</td>
    <td>73.11</td>
  </tr>
  <tr>
    <td rowspan="3">GPT-4o</td>
    <td><i>Vanilla</i></td>
    <td>81.66</td>
    <td>89.18</td>
    <td>68.84</td>
    <td>55.49</td>
    <td>80.44</td>
    <td>75.12</td>
  </tr>
</table>

Table 3: Main Results. Global top-3 results are bold.
<table>
  <tr>
    <th rowspan="2">Models</th>
    <th colspan="8">MEMSCORE</th>
    <th colspan="8">COMSCORE</th>
  </tr>
  <tr>
    <th>eEffect</th><th>eReact</th><th>oWant</th><th>xAttr</th><th>eEffect</th><th>xIntent</th><th>xNeed</th><th>xReact</th><th>eEffect</th><th>eReact</th><th>oWant</th><th>xAttr</th><th>eEffect</th><th>xIntent</th><th>xNeed</th><th>xReact</th><th>xWant</th>
  </tr>
  <tr>
    <td>Mistral-7B</td><td>52.1</td><td>65.7</td><td>51.9</td><td>37.4</td><td>55.9</td><td>78.6</td><td>68.7</td><td>64.5</td><td>55.2</td><td>77.2</td><td>81.0</td><td>70.5</td><td>84.4</td><td>75.9</td><td>87.7</td><td>76.8</td><td>83.9</td><td>74.2</td>
  </tr>
  <tr>
    <td>Llama3-8B</td><td>55.9</td><td>60.8</td><td>57.1</td><td>63.4</td><td>58.8</td><td>81.8</td><td>70.0</td><td>66.7</td><td>59.7</td><td>72.2</td><td>78.5</td><td>76.1</td><td>85.3</td><td>77.5</td><td>87.9</td><td>80.1</td><td>85.2</td><td>77.4</td>
  </tr>
  <tr>
    <td>Qwen2.5-7B</td><td>51.2</td><td>56.8</td><td>54.1</td><td>60.1</td><td>55.8</td><td>78.1</td><td>66.7</td><td>62.6</td><td>50.2</td><td>72.3</td><td>78.1</td><td>77.3</td><td>87.2</td><td>77.8</td><td>88.4</td><td>77.3</td><td>81.3</td><td>73.4</td>
  </tr>
  <tr>
    <td>Llama2-13B</td><td>59.5</td><td>64.7</td><td>60.1</td><td>66.0</td><td>61.6</td><td>85.2</td><td>72.8</td><td>70.1</td><td>58.3</td><td>76.7</td><td>84.6</td><td>76.2</td><td>88.5</td><td>79.6</td><td>89.7</td><td>80.7</td><td>86.3</td><td>78.5</td>
  </tr>
  <tr>
    <td>Qwen2.5-14B</td><td>60.8</td><td>66.7</td><td>60.3</td><td>68.4</td><td>61.9</td><td>87.3</td><td>74.4</td><td>70.2</td><td>60.5</td><td>75.2</td><td>82.6</td><td>76.3</td><td>86.7</td><td>78.0</td><td>89.6</td><td>76.3</td><td>86.5</td><td>79.9</td>
  </tr>
  <tr>
    <td>GPT-3.5-turbo</td><td>65.4</td><td>78.4</td><td>72.8</td><td>76.4</td><td>72.8</td><td>87.8</td><td>76.4</td><td>74.0</td><td>65.4</td><td>80.6</td><td>85.2</td><td>80.6</td><td>88.4</td><td>82.2</td><td>89.4</td><td>82.2</td><td>88.7</td><td>80.7</td>
  </tr>
  <tr>
    <td>GPT-4o</td><td>82.8</td><td>71.8</td><td>76.3</td><td>75.7</td><td>94.0</td><td>84.3</td><td>82.9</td><td>79.9</td><td>81.6</td><td>88.2</td><td>83.1</td><td>93.3</td><td>84.2</td><td>96.0</td><td>81.7</td><td>92.4</td><td>85.4</td>
  </tr>
</table>

Table 4: Results of each relation. ↓ and ↑ represent the performance is lower or higher than its average performance.

<table>
  <tr>
    <th rowspan="2">Models</th>
    <th rowspan="2">Methods</th>
    <th colspan="3">2r</th>
    <th colspan="3">2p</th>
    <th colspan="3">ip</th>
    <th colspan="3">ps</th>
  </tr>
  <tr>
    <th>REA.</th><th>FAI.</th><th>APP.</th><th>REA.</th><th>FAI.</th><th>APP.</th><th>REA.</th><th>FAI.</th><th>APP.</th><th>REA.</th><th>FAI.</th><th>APP.</th>
  </tr>
  <tr>
    <td rowspan="3">Vanilla</td><td>Mistral-7B</td><td>67.8</td><td>47.0</td><td>82.0</td><td>65.6</td><td>40.0</td><td>56.2</td><td>52.7</td><td>49.6</td><td>42.1</td><td>35.7</td><td>61.3</td><td>46.3</td>
  </tr>
  <tr>
    <td>CoT</td><td>66.8</td><td>49.8</td><td>83.6</td><td>66.4</td><td>41.6</td><td>56.3</td><td>53.0</td><td>50.3</td><td>42.1</td><td>37.1</td><td>63.8</td><td>47.7</td>
  </tr>
  <tr>
    <td>KnowCoT</td><td>69.7</td><td>49.5</td><td>84.5</td><td>67.9</td><td>42.4</td><td>59.9</td><td>54.8</td><td>52.4</td><td>43.4</td><td>38.1</td><td>65.9</td><td>49.1</td>
  </tr>
  <tr>
    <td rowspan="3">Vanilla</td><td>Llama3-8B</td><td>62.0</td><td>47.5</td><td>87.3</td><td>61.7</td><td>39.6</td><td>51.7</td><td>47.9</td><td>46.5</td><td>46.5</td><td>33.6</td><td>56.9</td><td>45.7</td>
  </tr>
  <tr>
    <td>CoT</td><td>61.1</td><td>48.9</td><td>87.9</td><td>62.0</td><td>38.6</td><td>54.3</td><td>48.7</td><td>47.2</td><td>47.8</td><td>36.3</td><td>58.2</td><td>47.4</td>
  </tr>
  <tr>
    <td>KnowCoT</td><td>64.0</td><td>50.8</td><td>87.1</td><td>64.0</td><td>40.7</td><td>55.1</td><td>51.1</td><td>49.5</td><td>48.7</td><td>36.8</td><td>60.0</td><td>48.5</td>
  </tr>
  <tr>
    <td rowspan="3">Vanilla</td><td>Qwen2.5-7B</td><td>62.0</td><td>48.0</td><td>89.7</td><td>58.7</td><td>36.7</td><td>53.6</td><td>43.0</td><td>44.4</td><td>44.2</td><td>31.9</td><td>55.5</td><td>43.9</td>
  </tr>
  <tr>
    <td>CoT</td><td>62.0</td><td>47.7</td><td>70.2</td><td>59.8</td><td>36.5</td><td>55.2</td><td>42.6</td><td>44.8</td><td>45.6</td><td>32.8</td><td>57.0</td><td>45.2</td>
  </tr>
  <tr>
    <td>KnowCoT</td><td>64.5</td><td>51.7</td><td>71.5</td><td>62.2</td><td>39.4</td><td>56.8</td><td>45.4</td><td>47.2</td><td>47.2</td><td>34.5</td><td>58.0</td><td>46.6</td>
  </tr>
  <tr>
    <td rowspan="3">Vanilla</td><td>Llama2-13B</td><td>70.6</td><td>47.4</td><td>80.4</td><td>66.1</td><td>40.6</td><td>56.4</td><td>50.6</td><td>49.2</td><td>48.5</td><td>37.8</td><td>63.5</td><td>50.0</td>
  </tr>
  <tr>
    <td>CoT</td><td>71.6</td><td>48.2</td><td>82.6</td><td>67.5</td><td>40.8</td><td>57.1</td><td>53.6</td><td>50.5</td><td>49.9</td><td>39.7</td><td>65.9</td><td>51.8</td>
  </tr>
  <tr>
    <td>KnowCoT</td><td>73.9</td><td>49.6</td><td>83.7</td><td>69.7</td><td>43.4</td><td>59.7</td><td>54.4</td><td>52.5</td><td>50.7</td><td>40.2</td><td>66.9</td><td>52.6</td>
  </tr>
  <tr>
    <td rowspan="3">Vanilla</td><td>Qwen2.5-14B</td><td>71.1</td><td>48.9</td><td>81.5</td><td>67.2</td><td>42.4</td><td>57.3</td><td>51.7</td><td>50.5</td><td>48.1</td><td>36.4</td><td>64.8</td><td>49.8</td>
  </tr>
  <tr>
    <td>CoT</td><td>72.0</td><td>51.4</td><td>81.8</td><td>68.4</td><td>44.3</td><td>59.7</td><td>54.0</td><td>52.7</td><td>49.7</td><td>36.8</td><td>66.6</td><td>51.0</td>
  </tr>
  <tr>
    <td>KnowCoT</td><td>73.1</td><td>54.1</td><td>85.6</td><td>70.9</td><td>44.9</td><td>62.6</td><td>55.9</td><td>54.8</td><td>50.8</td><td>41.2</td><td>69.3</td><td>53.8</td>
  </tr>
  <tr>
    <td rowspan="3">Vanilla</td><td>GPT-3.5-turbo</td><td>79.4</td><td>50.0</td><td>87.1</td><td>72.2</td><td>43.1</td><td>59.9</td><td>53.5</td><td>52.2</td><td>55.3</td><td>41.3</td><td>68.9</td><td>55.2</td>
  </tr>
  <tr>
    <td>CoT</td><td>82.3</td><td>52.6</td><td>88.4</td><td>74.8</td><td>45.8</td><td>62.5</td><td>56.9</td><td>55.1</td><td>57.4</td><td>42.2</td><td>71.0</td><td>57.5</td>
  </tr>
  <tr>
    <td>KnowCoT</td><td>86.5</td><td>56.5</td><td>89.5</td><td>75.5</td><td>47.5</td><td>65.7</td><td>61.2</td><td>57.2</td><td>61.2</td><td>45.8</td><td>72.4</td><td>61.7</td>
  </tr>
  <tr>
    <td rowspan="3">Vanilla</td><td>GPT-4o</td><td>81.7</td><td>54.7</td><td>90.9</td><td>75.8</td><td>48.3</td><td>63.7</td><td>53.9</td><td>54.2</td><td>57.0</td><td>43.3</td><td>72.0</td><td>57.7</td>
  </tr>
  <tr>
    <td>CoT</td><td>82.6</td><td>58.7</td><td>91.6</td><td>77.7</td><td>46.3</td><td>57.1</td><td>55.3</td><td>55.7</td><td>59.1</td><td>46.3</td><td>72.9</td><td>59.6</td>
  </tr>
  <tr>
    <td>KnowCoT</td><td>83.5</td><td>60.1</td><td>94.6</td><td>79.4</td><td>53.0</td><td>69.5</td><td>60.0</td><td>60.8</td><td>60.5</td><td>49.4</td><td>78.8</td><td>62.9</td>
  </tr>
</table>

Table 5: Results of each query. ↓ and ↑ represent the performance is lower or higher than its average performance.

age of 1.92% in MEMSCORE and COMSCORE, yet it experiences a significant decline of 6.87% in APPSCORE. This suggests that while certain LLMs excel at storing and retrieving knowledge, they may face challenges in applying that knowledge to reasoning-intensive tasks.

KnowCoT consistently improves the LLMs’ performance especially on application. Our KnowCoT achieves the highest performance of 75.12%, which is substantially better than the vanilla GPT-4 by 3.69 points on average. And there is also an absolute gain of 14.81% on REASCORE, FAISCORE and APPSCORE. The impact of CoT across tasks can be found in Appendix D.

LLM’s faithfulness to knowledge is closely tied to its comprehension, whereas its overall reasoning ability is determined by both memorization and application. This conclusion is supported by the strong correlation between MEMSCORE × COMSCORE and FAISCORE, showing that knowledge faithfulness depends more on comprehension than memorization. Likewise, the correlation between MEMSCORE × APPSCORE and REASCORE confirms that effective reasoning requires both memorization and application, not just knowledge recall. These findings validate the proposed metrics and demonstrate their effectiveness in distinguishing and characterizing different LLM capabilities. See the detailed results in Appendix E.

6.3 Challenges in Commonsense

LLMs excel or fall short in different aspects of commonsense knowledge. We analyze LLMs’ performance across various commonsense aspects, as shown in Table 4. We regard the LLMs’ average performance in memorization and comprehension as the baseline. If the LLM outperforms the baseline in a specific aspect, it suggests greater proficiency in this relation type of knowledge, and vice versa. The findings indicate LLMs generally demonstrate good knowledge of xIntent and xReact. However, their proficiency of o/xEffect and o/xWant is relatively weaker. The uneven mastery of knowledge significantly affects the LLMs’s reasoning performance, especially when dealing with complex questions that involve multiple types of knowledge. Moreover, showing good memoriza-
tion results in certain aspects does not necessarily mean good comprehension, and vice versa. For example, LLMs generally good at memorize xNeed knowledge but the comprehension is below average level, while xAttr knowledge is hard for LLMs to memorize but shows better comprehension results.

LLMs underperform in (multi-hop) commonsense reasoning. We analyze LLMs’ reasoning ability across different query types, as shown in Table 5. The performance of all LLMs in commonsense reasoning is unsatisfactory. An intuitive conclusion is higher FAiSCORE for query types involving less atomic knowledge (e.g., 2i and 2p). For different reasoning results, a noticeable decrease is observed in 2p, ip and pi query types compared to 2i. This is because these three tasks necessitate a two-step reasoning step. They contain multi-hop projection which involves inferring hidden reasoning contexts. In contrast, the 2i task only requires intersection operations that can be completed with a single reasoning step. For intersection and projection results, LLMs are more struggle with the projection cases. The APPSCORE of 2p and ip is much lower than others, because the corresponding query structure are overly a projection structure, while 2i and pi require reasoning about complex intersections between event. In summary, LLMs struggle with commonsense reasoning, especially in multi-step reasoning scenarios.

6.4 Error Analysis

We manually analyze 100 error cases in REASCORE, FAiSCORE and APPSCORE by Mistral, Llama and GPT-4, as shown in Figure 4.

REASCORE. We divide errors into: (a) No Answer: The model fails to provide a final answer. (b) Reasoning Error: The model encounters reasoning errors. (c) Hallucination: The model’s prediction does not exist in the options. (d) Metric Limit: The model’s prediction is correct, but the metric is limited by the evaluation criteria. We observe that GPT-4 have a higher No Answer rate, while Mistral and Llama are always able to provide answers. This discrepancy can be attributed to two factors: (1) the LLMs may lack the necessary commonsense knowledge to formulate an answer; (2) advanced LLMs abstain from answering questions beyond their knowledge scope, while weaker LLMs often attempt to answer, regardless of reliability.

FAiSCORE. We divide errors into: (a) No Knowledge: The model answers the reasoning question correctly but has no atomic knowledge. (b) Partial Knowledge: The model answers the reasoning question correctly but has partial atomic knowledge. (c) Metric Limit: The model’s prediction is correct, but the metric is limited by the evaluation criteria. (d) Evaluation Failure: The model answers the reasoning question correctly but does not use annotated atomic knowledge. The first two cases are due to the hallucination of LLMs. Moreover, there are very few cases of No Knowledge and in most cases LLMs have partial knowledge, which indicates that LLMs are relatively easy to obtain the final answer through partial knowledge. However, in some cases, evaluation by FAiSCORE fails. We manually check these reasoning chains and find that LLMs can deduce the final answer using other atomic knowledge. Although we strictly construct reasoning questions through queries based on atomic knowledge, it is inevitable that other knowledge can also lead to the correct answer. But our error analysis also shows that this situation is rare and mastering the required knowledge is still necessary, demonstrating the rationality of FAiSCORE.

APPSCORE. We divide errors into four groups as same as REASCORE. Compared to REASCORE, the No Answer and Hallucination rates are decreased, which is intuitive because the premise of all error cases is all atomic knowledge has been memorized. It can be observed that more error cases stem from reasoning errors and metric lim-

![Three bar charts showing error analysis for ReaScore, FaiScore, and AppScore for Mistral, Llama, and GPT-4](page_97_84_1293_246.png)

Figure 4: Error analysis for fine-grained commonsense reasoning results. We select 100 error cases from each subtask (i.e., REASCORE, FAiSCORE and APPSCORE) for Mistral, Llama and GPT-4, respectively.
itation. Despite the imperfect metric calculation, LLMs still have flaws in grounding atomic knowledge from reasoning questions and perform consistent commonsense reasoning.

7 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.

Limitations

CoCo not fully encompass dimensions such as temporal reasoning, causality, or broader contextual adaptability. The use of predefined templates for verbalizing queries and knowledge triples, while practical, might not fully represent the diversity of natural language expressions. The reliance on resources like ATOMIC provides a strong foundation but may not entirely reflect performance across more diverse or unseen commonsense domains. These observations highlight areas for further exploration, such as expanding task diversity, enhancing adaptability to real-world scenarios, and broadening the scope of knowledge.

Acknowledgement

This work was supported by National Science Foundation of China (Grant Nos.62376057), the Start-up Research Fund of Southeast University (RF1028623234) and SEU Innovation Capability Enhancement Plan for Doctoral Students (No. CXJH_SEU 25031).

References

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A Dataset Construction

A.1 Construction of Memorization Task

Atomic Knowledge Sampling. The first step of our data generating process is to sample a set of atomic knowledge triples from the CSKG. Because ATOMIC contains 877K textual descriptions of triples, triple sampling is required for the memorization task data construction. For 9 relations in ATOMIC, we sample 2K diverse and representative (Su et al., 2023) triples for each relation. We first compute a vector representation for each triple using Sentence-BERT (Reimers and Gurevych, 2019) by averaging the resulting vectors over the text input words. We then use the embedding vectors to create a directed graph \( \mathcal{G} = (\mathcal{V}, \mathcal{E}) \) where the vertices \( \mathcal{V} \) are the embedded triples as defined above. For each vertex \( v \in \mathcal{V} \), we create an edge to its \( k \) nearest vertices in terms of the cosine similarity between the embeddings. Let \( \mathcal{D} \) and \( \mathcal{U} \) denote the sets of already chosen and remaining samples, respectively. Initially, \( \mathcal{D} = \emptyset \). Every vertex \( u \in \mathcal{U} \) is scored by a modified degree:

\[
\text{score}(u) = \sum_{v \in \{v|(v,u)\in\mathcal{E},v\in\mathcal{U}\}} s(v),
\]
where \( s(v) = \rho^{-|\{c\in\mathcal{D}|(v,c)\in\mathcal{E}\}|} \), \( \rho > 1 \)

(9)

where \( s \) discounts \( v \) that is close to the already selected vertices, thereby encouraging diversity. We take \( \arg\max_{u \in \mathcal{U}} \text{score}(u) \) and move it from \( \mathcal{U} \) to \( \mathcal{D} \) in every iteration. We set \( k \) to 150, \( \rho \) to 10, and run 2K of these iterations for each relation, where the current \( \mathcal{D} \) has 18K triples.

Verbalization. After obtaining the representative triple set \( \mathcal{D} \) of the entire CSKG, the commonsense relation within each triple is verbalized into human-readable text (Fang et al., 2021b), as shown in Table 6. For each question \( q_k \) verbalized by \( k = (h, r, t) \in \mathcal{D} \), we sample 4 additional distractors for the answer \( t \), where 2 of them are randomly sampled across the whole CSKG, and others are sampled from the neighbors of \( k \) but not the answers, represented as adversarial negative samples.

<table>
  <tr>
    <th>Relation</th>
    <th>Human Readable Text</th>
  </tr>
  <tr>
    <td>oEffect</td>
    <td>What is the effect on PersonY after</td>
  </tr>
  <tr>
    <td>oReact</td>
    <td>What does PersonY feel after</td>
  </tr>
  <tr>
    <td>oWant</td>
    <td>What does PersonY want to do after</td>
  </tr>
  <tr>
    <td>xAttr</td>
    <td>What is PersonX seen as given</td>
  </tr>
  <tr>
    <td>xEffect</td>
    <td>What is the effect on PersonX after</td>
  </tr>
  <tr>
    <td>xIntent</td>
    <td>What is the intention of PersonX before</td>
  </tr>
  <tr>
    <td>xNeed</td>
    <td>What does PersonX need to do before</td>
  </tr>
  <tr>
    <td>xReact</td>
    <td>What does PersonX feel after</td>
  </tr>
  <tr>
    <td>xWant</td>
    <td>What does PersonX want to do after</td>
  </tr>
</table>

Table 6: Textual prompt for commonsense relations. Commonsense triple \( (h, r, t) \) is translated to human language "[prompt] h", and the answer is t.

context throughout the process, where low-quality generations are eliminated by filtering models.

Concept Generation. Due to the knowledge abstraction process only involves head events, we compute a vector representation for each head event and then sample 20K head events in total from ATOMIC via Equation 9. For each head event, we sample a concrete triple and utilize language models to collect conceptualizations in a one-step inference manner. Specifically, we train a GPT-2 (Radford et al., 2019) based concept generator using ABSTRACTATOMIC (He et al., 2024) as abstract knowledge corpus. We generate possible concepts for the candidate in a way similar to COMET (Bosselut et al., 2019). Each sample \( (h_i, h_i^c) \) in ABSTRACTATOMIC is formed as a sequence of tokens \( t_i = [h_i; [EOS]; h_i^c] \), with ; indicated the concatenation operation. The standard causal language model loss on \( h_i^c \) is used. Suppose \( h_i \) plus [EOS] correspond to first \( m \) tokens in \( t_i \) with total \( n \) tokens, the loss is:

\[
L = - \sum_{t_i} \sum_{j=m+1}^{n} \log P(t_{i,j}|t_{i,<j})
\]

(10)

Then we utilize this fine-tuned model to sample five candidate conceptualizations for each event.

Conceptualization Verification Finally, we feed the possible event conceptualizations into a neural model as a gatekeeper to filter out those not matching the context. For all conceptualizations generated, we use an existing plausibility scorer Vera (Liu et al., 2023), a T5 (Raffel et al., 2020) based scorer, to score every triple in terms of plausibility of commonsense (between 0 and 1). We filter out triples with a plausibility score less than 0.5. For all remaining triples, we retain head events with more than 3 conceptualizations, resulting in
<table>
  <tr>
    <th>Relation</th>
    <th>Mapping Rules</th>
  </tr>
  <tr>
    <td>oEffect</td>
    <td>Add PersonY in front of the tail</td>
  </tr>
  <tr>
    <td>oReact</td>
    <td>Add PersonY and “is” in front of the tail</td>
  </tr>
  <tr>
    <td>oWant</td>
    <td>Add PersonY in front of the tail and remove the initial “to”</td>
  </tr>
  <tr>
    <td>xAttr</td>
    <td>Add PersonX and “is” in front of the tail</td>
  </tr>
  <tr>
    <td>xEffect</td>
    <td>Add PersonX in front of the tail</td>
  </tr>
  <tr>
    <td>xIntent</td>
    <td>Add PersonX in front of the tail and remove the initial “to”</td>
  </tr>
  <tr>
    <td>xNeed</td>
    <td>Add PersonX in front of the tail and remove the initial “to”</td>
  </tr>
  <tr>
    <td>xReact</td>
    <td>Add PersonX and “is” in front of the tail</td>
  </tr>
  <tr>
    <td>xWant</td>
    <td>Add PersonX in front of the tail and remove the initial “to”</td>
  </tr>
</table>

Table 7: Normalization rules for ATOMIC tail events.

13K original triples with their 39K conceptualized triples after random sampling. In other words, we provide 3 sets of corresponding abstract knowledge triples for each atomic knowledge triple.

Verbalization. We add 13K samples in comprehension task to \( \mathcal{D} \), and now \( \mathcal{D} \) has 31K samples. Similar to memorization task, each original triple and conceptualized triple is verbalized into a question answering pair. For questions \( q_k \) and \( q_{k^c} \), we sample 4 additional distractors for each question to construct MCQA samples, respectively.

A.3 Construction of Application Task

We construct the application task based on multiple pieces of atomic knowledge, involving reasoning on unobserved edges and multiple events in CSKGs. Following previous works (Ren et al., 2020; Fang et al., 2024), we use basic projections \( 2p \), intersections \( 2i \) and complex queries \( ip \) and \( pi \) as evaluation queries. In this formulation, multi-hop projection involves inferring hidden reasoning contexts, while intersection operations require reasoning about complex interactions between events.

Query Structures. The specific query structures that we study in this work are visualized in Figure 5. For \( Ip \), it can be simply instantiated by an atomic knowledge triple, such as \( q[t] = t : xEffect(PersonX is on vacation, t) \). Following previous works (Ren et al., 2020; Fang et al., 2024), we use basic projections \( 2p \), intersections \( 2i \) and complex queries \( ip \) and \( pi \). The logical expressions for these four queries are as follows:

\[
\begin{align*}
2i : q[t] &= t : r_1(h_1, t) \land r_2(h_2, t) \\
2p : q[t] &= t : r_1(h_1, V) \land r_2(V, t) \\
ip : q[t] &= t : r_1(h_1, V) \land r_2(h_2, V) \land r_3(V, t) \\
pi : q[t] &= t : r_1(h_1, V) \land r_2(h_2, t) \land r_3(V, t)
\end{align*}
\]

where \( V \) denotes the free variable.

![Visualization of query structures. The anchor entities and relations are specified to instantiate the query. "p" and "i" represent projection and intersection, and the number ahead of p and i indicates the number of anchor entities and free variables.](page_1016_1012_388_246.png)

Figure 5: Visualization of query structures. The anchor entities and relations are specified to instantiate the query. “p” and “i” represent projection and intersection, and the number ahead of p and i indicates the number of anchor entities and free variables.

<table>
  <tr>
    <th>Relation</th>
    <th>Prompt Template</th>
  </tr>
  <tr>
    <td>oEffect</td>
    <td>the effect on PersonY after</td>
  </tr>
  <tr>
    <td>oReact</td>
    <td>what PersonY feels after</td>
  </tr>
  <tr>
    <td>oWant</td>
    <td>what PersonY wants to do after</td>
  </tr>
  <tr>
    <td>xAttr</td>
    <td>what PersonX is seen as given</td>
  </tr>
  <tr>
    <td>xEffect</td>
    <td>the effect on PersonX after</td>
  </tr>
  <tr>
    <td>xIntent</td>
    <td>the intention of PersonX before</td>
  </tr>
  <tr>
    <td>xNeed</td>
    <td>what PersonX needed to do before</td>
  </tr>
  <tr>
    <td>xReact</td>
    <td>what PersonX feels after</td>
  </tr>
  <tr>
    <td>xWant</td>
    <td>what PersonX wants to do after</td>
  </tr>
</table>

Table 8: Templates for verbalizing relations in queries.

Nodes Normalization. Before sampling queries, we first normalize the tail entities with simple rules following previous works (Fang et al., 2021a; Shen et al., 2023). In ATOMIC, heads are pre-defined complete sentences (e.g., “PersonX says sorry”) while tails are usually short phrases without a subject (e.g., “to say sorry”). We develop simple rules to add “PersonX” or “PersonY” in front of the tails to make them a complete sentence, as shown in Table 7. This allows the head and tail nodes to merge, enabling the query sampling from ATOMIC.

Query Sampling. Given a query structure, we use pre-order traversal to sample free variables and anchor events starting from an answer event. We sample predecessors uniformly based on (relation, event) pairs. For 4 query types, we sample 3K
<table>
  <tr>
    <th>Query Type</th>
    <th>Question Template</th>
  </tr>
  <tr>
    <td>2i</td>
    <td>What event or state is both Prompt (r1) [V1] and also Prompt (r2) [V2]?</td>
  </tr>
  <tr>
    <td>2p</td>
    <td>What event or state is Prompt (r1) Prompt (r2) [V1]</td>
  </tr>
  <tr>
    <td>ip</td>
    <td>What event or state is Prompt (r3) both Prompt (r1) [V1], and also Prompt (r2) [V2]?</td>
  </tr>
  <tr>
    <td>pi</td>
    <td>What event or state is both Prompt (r1) Prompt (r3) [V3], and also Prompt (r2) [V2]?</td>
  </tr>
</table>

Table 9: Templates for verbalizing four query types.

Q: [question from the dataset]

Guidance: Address the question by following the steps below:

Step 1) Extract the core event: Identify the core event in the question. The event should simply consists of its subject, predicate, and object.

Step 2) Recall the relevant knowledge: Recall the relevant knowledge triple of the core event implied by the question. The knowledge triple should simply consists of its head event, relation and tail event.

Step 3) Deduce the final result: Given all the information above, deduce the final result and answer step by step.

A: [LLM previous response]

Q: Based on all the reasoning above, select the correct option to answer the initial question.

A: [LLM final answer]

Figure 6: Details of our KnowCoT prompting strategy.

instances for each type. During sampling, to avoid over-sampling on nodes with high degree, we only sample from top 10 neighbors of a node scored by Equation 9. Besides, 4 additional distractors for each query are also sampled. We also conduct a post-order traversal starting from the anchor events to find all the answers of the query, ensuring that the sampled distractor is not the correct answer.

Verbalization. The sampled logical queries and answers are verbalized into human-readable text using a rule-based verbalizer (Fang et al., 2024). We use conversion rules and pre-defined templates to compose questions based on the relations in the queries. Based on the definition of each commonsense relation (Sap et al., 2019a; Hwang et al., 2021), we use the templates in Table 8 to verbalize each relation. In terms of logical queries, we use the conversion rules in Table 9 to convert the query to a question. Then we use Vera (Liu et al., 2023) to filter out low quality queries and answers. Specifically, we ask Vera for the correctness of the 5 options in MCQA. If all options are incorrect or multiple answers are correct, then this sample will be filtered out. Finally, around 8K samples are retained in application task, and \( \mathcal{D} \) is expanded to 39K samples in total. Besides the reasoning question \( q_{(k_1,...,k_n)} \), all the atomic knowledge it contains is also verbalized into MCQA pairs.

B  KnowCoT Prompting

B.1 Prompt Details

Compared with the standard strategy of directly prompting the LLMs a question, we impose an *inductive bias* upon LLMs by explicitly recalling atomic knowledge, thus improving the knowledge comprehension and application. In this way, we enhance the consistent commonsense reasoning abilities of LLMs via recalling relevant knowledge. The complete prompt is shown in Figure 6. In the end, we extract and obtain the answer \( a \in \{ \text{A, B, C, D, E} \} \) as the final prediction.

B.2 Prompt Examples

In order to verify the rationality of KnowCoT prompting, which guides the LLMs to extract core events, recall relevant important knowledge, and ultimately solve reasoning problems, we illustrate the KnowCoT prompting results of Mistral-7B-Instruct-v0.3 and GPT-4o as examples, as shown in Figure 7 and Figure 8. The reasoning question is the same as the question in Figure 3. It can be observed that although Mistral is difficult to follow instructions to generate knowledge in triple form, it describes the same meaning in natural language,
Step 1) Extract the core event: The core event in the question is PersonX plays a football game.

Step 2) Recall the relevant knowledge: After playing a sport like football, the body can become tired and in need of rest to recover from the exertion.

Step 3) Deduce the final result: To recover from the physical fatigue, PersonX wants to take a rest.

Final answer: PersonX wants to take a rest.

Figure 7: An example of KnowCoT prompting response in Mistral-7B.

Step 1) Extract the core event:
The core event is PersonX playing a football game.

Step 2) Recall the relevant knowledge:
• Playing a football game is a physically demanding activity, so it often leads to fatigue. Knowledge triple: (Playing a football game, causes, fatigue).
• After physical exertion, common responses include resting or eating to recover energy. Knowledge triple: (Fatigue, motivates, taking a rest or eating).
• Emotional outcomes like sadness or injury depend on context (e.g., losing the game or an accident). However, they are not universal.

Step 3) Deduce the final result:
• Option A: “PersonX wants to take a rest” is highly plausible, as playing a football game often causes fatigue.
• Option B: “PersonX wants to eat” is also plausible but secondary, as eating might follow after resting or only occur if hunger is specifically felt.
• Option C: “PersonX feels sad” is unlikely unless there is additional context indicating a disappointing result of the game.
• Option D: “PersonX gets hurt” is possible, but injury is situational and not implied by the question.
• Option E: “PersonX leaves” is neutral and does not address the core cause-and-effect relationship.

Final Answer: A: PersonX wants to take a rest.

Figure 8: An example of KnowCoT prompting response in GPT-4o.

while the powerful GPT-4o can generate triples that express the same meaning with atomic knowledge.

C Experimental Details

Following the standard practice of prompting LLMs for QA (Robinson et al., 2023), we initialize a prompt that takes “[Instruction] [Question] [Options]” as the input and ask the model to output the correct option in the QA pair as the prediction. The input prompt template is shown in Figure 9.

We access proprietary models through official API. For open-source models, we use the checkpoints from Huggingface’s Transformer library (Wolf et al., 2019). We set the temperature to zero for greedy decoding in all experiments. Zero-shot CoT (Kojima et al., 2022) simply concatenates the input prompt with “Let’s think step by step.” After obtaining the reasoning intermediate results, we use “Therefore, among A through E, the answer is” for MCQA. And KnowCoT follows the similar idea as shown in Figure 6. For the correct or incorrect judgment of the final output answer of LLMs, we match the first option that appears in the final output answer of the LLMs as the predicted option.

D Chain-of-Thought Results

D.1 CoT Analysis

The Chain-of-Thought (CoT) prompting method demonstrates slight improvements over the vanilla baseline across all tasks. For memorization, CoT achieves a small gain (68.26 vs. 67.64), suggesting that CoT provides marginal benefits in this task but does not significantly enhance the model’s ability to recall atomic knowledge. For comprehension, CoT scores 82.72, slightly higher than the vanilla score of 82.28, indicating its limited contribution to improving reasoning over abstract concepts. For application, CoT achieves 57.38 compared to the vanilla score of 55.91, showing a more noticeable improvement in tasks requiring multi-step reasoning, though the gap remains modest. These results suggest that while CoT reasoning aids in structuring the reasoning process, its influence on memorization and fundamental comprehension remains minimal. However, its impact becomes more pronounced in complex reasoning tasks, particularly those requiring higher-order thinking, such as application and problem-solving.

D.2 KnowCoT Analysis

The KnowCoT prompting method outperforms both the vanilla and CoT approaches across all tasks. For memorization, KnowCoT achieves the highest score (68.44), slightly better than CoT (68.26) and vanilla (67.64), indicating an enhanced ability to retrieve and represent atomic knowledge. For comprehension, KnowCoT reaches 83.34, showing incremental improvements over CoT (82.72) and vanilla (82.28), demonstrating its effectiveness in reasoning over abstract conceptual knowledge. For application, KnowCoT achieves a noticeable improvement (59.4) compared to CoT (57.38) and vanilla (55.91), highlighting its superior performance in handling complex reason-
Answer this commonsense reasoning question, where you are supposed to handle a multiple-choice question answering task to select the correct answer. Select one correct answer from A to E.

Question: [Question]

A: [Option A]. B: [Option B]. C: [Option C]. D: [Option D]. E: [Option E].

Answer:

Figure 9: The input prompt template for multiple-choice question answering.

![Bar chart comparing performance of Vanilla, CoT, and KnowCoT prompting across Memorization, Comprehension, and Application tasks](page_352_613_484_246.png)

Figure 10: Performance gap with and without CoT and KnowCoT prompting. The results are averaged from all LLMs evaluated in Table 2.

ing tasks involving multiple pieces of knowledge. Overall, KnowCoT consistently outperforms CoT, especially in tasks requiring multi-step reasoning (application), suggesting its potential as a more robust prompting method for leveraging LLMs’ commonsense reasoning capabilities.

E Correlation of Metrics

To provide a deeper understanding of our metrics, we evaluate the correlation between these metrics, as shown in Figure 11. First, MEMSCORE is not strictly linearly correlated with COMSCORE or APPSCORE, indicating that memorization alone does not directly translate to other abilities. However, the product of MEMSCORE and COMSCORE exhibits an almost perfect linear correlation with FAISCORE, suggesting that LLMs’ faithfulness to knowledge is closely linked to comprehension. Similarly, the product of MEMSCORE and APPSCORE shows a near-perfect linear correlation with REASCORE, implying that knowledge memorization and application together determine overall reasoning performance. These findings validate our metrics and highlight their effectiveness in distinguishing and characterizing different abilities.

F Related Work

F.1 Large Language Models

In recent years, there has been rapid progress in the research of large language models (LLMs) (Zhao et al., 2023a). They exhibit outstanding performance across a multitude of tasks without the need for fine-tuning (Brown et al., 2020; Wei et al., 2022; Kojima et al., 2022). Furthermore, they have achieved astonishing results in complex reasoning tasks, such as mathematical reasoning (Cobbe et al., 2021; Mishra et al., 2022) and logical reasoning (Yu et al., 2020; Teng et al., 2023). Moreover, some studies suggest that the chain-of-thought prompting (Wei et al., 2022) can further enhance the model’s capabilities in complex reasoning scenarios (Zhang et al., 2023; Chu et al., 2024). While LLMs are believe to encode various knowledge during pre-training (Madaan et al., 2022; Jain et al., 2023; Zhao et al., 2023b), existing commonsense evaluation works (Zhou et al., 2020; Li et al., 2022; Cheng et al., 2024) focus on commonsense knowledge assessment in LLMs.

F.2 Commonsense Benchmarks

Commonsense knowledge spans many categories, such as physical commonsense (e.g., a car is heavier than an apple), social commonsense (e.g., a person will feel happy after receiving gifts), and temporal commonsense (e.g., cooking an egg takes less time than baking a cake). Given this diverse nature of commonsense knowledge, various benchmarks have been proposed to test these different types of knowledge. Commonsense benchmarks broadly consist of two tasks: (a) multiple-choice evaluation (Zellers et al., 2019; Sakaguchi et al., 2021; Sap et al., 2019b; Bisk et al., 2020), where a model needs to choose the correct answer from a list of plausible answers; (b) generative evaluation (Boratko et al., 2020; Lin et al., 2020, 2021), which requires a model to generate an answer given a question and some additional context. In this study, we focus on multiple-choice benchmarks, since they provide a more reliable automatic metric (i.e., accuracy), whereas automated metrics used to evaluate language generation (e.g., BLEU (Papineni et al., 2002)) do not correlate perfectly with
human judgment (Liu et al., 2016; Novikova et al., 2017). However, unlike the previous works (Zhou et al., 2020; Li et al., 2022) that used existing commonsense benchmarks to directly evaluate LLMs, we reduce the impact of data contamination and hallucination by assessing consistency between commonsense knowledge and reasoning.

F.3 Evaluation for LLMs

Our work may be seen as part of the literature aimed at evaluating the performance of current LLMs (Brown et al., 2020; Jiang et al., 2023; Achiham et al., 2023; Yang et al., 2023; Riviere et al., 2024; Dubey et al., 2024; Yang et al., 2024), focusing on understanding their strengths and weaknesses. Various studies into the capabilities of LLMs (Bubeck et al., 2023; Ignat et al., 2023; Qin et al., 2023; Li et al., 2023) change people’s perception of domains such as education (Rudolph et al., 2023), medicine (Singhal et al., 2023), law (Katz et al., 2024), and computational social science (Ziems et al., 2024). However, most work evaluates new models on existing datasets from previously curated large-scale benchmarks (Wang et al., 2019; Srivastava et al., 2022; Wang et al., 2022), or human exams (Jin et al., 2022; Katz et al., 2024) which is becoming increasingly unreliable due to data contamination. Our work alleviates the impact of data contamination and hallucination by evaluating LLM’s knowledge memorization, comprehension, and application capabilities from a new perspective of consistency.

F.4 Correlations of Memorization and Others

There are benchmarks which assess both the knowledge memorization and reasoning capabilities of the LLMs within specific domains. For instance, KoLA (Yu et al., 2024) with its focus on world knowledge, includes tasks related to knowledge memorization, understanding, applying and creating. SeaEval (Wang et al., 2024a) emphasizing cross-language consistency and multicultural reasoning, involves tasks for cultural understanding and complex reasoning. CHARM (Sun et al., 2024) is built for comprehensive and in-depth evaluation of LLMs in Chinese commonsense reasoning and revealing the intrinsic correlation between memorization and reasoning. There are also benchmarks aimed at specialized fields, like LawBench (Fei et al., 2024), which include tasks for both memorization and application. However, these methods heavily rely on human annotation and lack scalable dataset generation. Due to the vague definition and granularity of basic knowledge memorization, it is hard to explain LLM’s reasoning errors and provide in-depth insights for evaluation processes. Compared to other methods, CSKG-based LLM evaluation is more structured, fine-grained, and interpretable. It distinguishes memorization, comprehension, and application while reducing data contamination. Additionally, it enables automated and multi-level assessment, making it a powerful tool for evaluating commonsense abilities.

G Examples in CoCo

As illustrated in Table 1, the number of samples (question sets) for the memorization, comprehension, and application tasks are 18,000, 13,060, and 7,942, respectively. These tasks encompass various relation types from ATOMIC and different query structures. For the comprehension task, the 1+3 Joint Test consists of one memorization test and three conceptualization tests. Similarly, for the application task, the 2+1 (or 3+1) Joint Test includes two (or three) memorization tests along with one reasoning test. We present different task examples in CoCo with different relation types and query structures in Table 10, Table 11 and Table 12, respectively.

![Correlations of our metrics.](page_153_120_1142_312.png)

Figure 11: Correlations of our metrics.
<table>
  <tr>
    <th>Type</th>
    <th>Question</th>
    <th>Options</th>
    <th>Answer</th>
  </tr>
  <tr>
    <td>oEffect</td>
    <td>What is the effect on PersonY after PersonX throws stones at PersonY?</td>
    <td>A: happy. B: lucky. C: head bleeds. D: to block the sun from their eyes. E: become experiment subject.</td>
    <td>C</td>
  </tr>
  <tr>
    <td>oReact</td>
    <td>What does PersonY feel after PersonX plays PersonY’s guitar?</td>
    <td>A: thanks PersonX. B: to swallow the liquid in their mouth. C: the dog takes it. D: positive. E: interested.</td>
    <td>E</td>
  </tr>
  <tr>
    <td>oWant</td>
    <td>What does PersonY want to do after PersonX keeps PersonY’s promises?</td>
    <td>A: to understand the subject. B: accept what PersonX decides. C: to thank PersonX. D: good about themselves. E: knows where PersonY is.</td>
    <td>C</td>
  </tr>
  <tr>
    <td>xAttr</td>
    <td>What is PersonX seen as given PersonX watches youtube videos?</td>
    <td>A: looking for entertainment. B: PersonY goes to the ocean. C: sad. D: problem. E: gets a new job.</td>
    <td>A</td>
  </tr>
  <tr>
    <td>xEffect</td>
    <td>What is the effect on PersonX after PersonX sits quietly?</td>
    <td>A: poor. B: PersonY listens carefully. C: to prevent breakage. D: surprised. E: stays quiet.</td>
    <td>E</td>
  </tr>
  <tr>
    <td>xIntent</td>
    <td>What is the intention of PersonX before PersonX props up the bar?</td>
    <td>A: to increase income. B: to become intoxicated. C: To read the book. D: gains stained carpet. E: to be challenged.</td>
    <td>B</td>
  </tr>
  <tr>
    <td>xNeed</td>
    <td>What does PersonX need to do before PersonX climbs onto the bed?</td>
    <td>A: Learns a new skill. B: students receive homework. C: becomes nervous. D: to put on their pajamas. E: PersonX’s son thanks them.</td>
    <td>D</td>
  </tr>
  <tr>
    <td>xReact</td>
    <td>What does PersonX feel after PersonX begs PersonY to take?</td>
    <td>A: gets sentenced to 10 years. B: ambitious. C: PersonX gets a walking cast. D: helpful. E: studious.</td>
    <td>D</td>
  </tr>
  <tr>
    <td>xWant</td>
    <td>What does PersonX want to do after PersonX kicks the ball?</td>
    <td>A: to score a goal. B: finds something to cheer them up. C: to rest up. D: tumbles. E: to be noticed.</td>
    <td>A</td>
  </tr>
</table>

Table 10: Examples of the memorization task.
<table>
  <tr>
    <th>Type</th>
    <th>Subquestion</th>
    <th>Question</th>
    <th>Options</th>
    <th>Answer</th>
  </tr>
  <tr>
    <td rowspan="3">oEffect</td>
    <td></td>
    <td>What is the effect on PersonY after PersonX asks family elders?</td>
    <td>A: cuddle PersonY. B: sympathetic. C: shows love for PersonX. D: leave their old job. E: responsive.</td>
    <td>C</td>
  </tr>
  <tr>
    <td></td>
    <td>What is the effect on PersonY after PersonX asks PersonY’s grandmother?</td>
    <td>A: to suffer their checklists. B: show love for PersonX. C: to be in tears. D: move on. E: angry.</td>
    <td>B</td>
  </tr>
  <tr>
    <td></td>
    <td>What is the effect on PersonY after PersonX asks family patriarch?</td>
    <td>A: mean. B: food burns. C: to go to the beach. D: to solve his problem. E: shows love for PersonX.</td>
    <td>E</td>
  </tr>
  <tr>
    <td rowspan="3">oReact</td>
    <td></td>
    <td>What does PersonY feel after PersonX argues with significant other of PersonY?</td>
    <td>A: States they don’t believe X. B: They can afford to buy more things. C: looks into getting a bullet proof vest. D: hurt. E: to wipe their face.</td>
    <td>D</td>
  </tr>
  <tr>
    <td></td>
    <td>What does PersonY feel after PersonX argues with significant partner of PersonY?</td>
    <td>A: to enjoy life. B: hurt. C: Hold arm together. D: permission. E: to calm down.</td>
    <td>B</td>
  </tr>
  <tr>
    <td></td>
    <td>What does PersonY feel after PersonX argues with PersonY’s best friend?</td>
    <td>A: Fraid and leave them. B: study. C: hurt. D: a job. E: catch personY doing something wrong.</td>
    <td>C</td>
  </tr>
  <tr>
    <td rowspan="3">oWant</td>
    <td></td>
    <td>What does PersonY want to do after PersonX achieves anticipated impact?</td>
    <td>A: PersonY is safe. B: to contact someone. C: arrested for littering. D: to paint the walls. E: to see.</td>
    <td>E</td>
  </tr>
  <tr>
    <td></td>
    <td>What does PersonY want to do after PersonX achieves desired consequence?</td>
    <td>A: updates. B: to figure out solution. C: to see. D: happy about decision. E: content.</td>
    <td>C</td>
  </tr>
  <tr>
    <td></td>
    <td>What does PersonY want to do after PersonX achieves intended result?</td>
    <td>A: well satisfied. B: to see. C: to go back home and sleep. D: walks away. E: to have a night cap at the bar.</td>
    <td>B</td>
  </tr>
  <tr>
    <td rowspan="3">xAttr</td>
    <td></td>
    <td>What is PersonX seen as given PersonX agrees to optimistic behavior?</td>
    <td>A: hopeful. B: happy. C: to learn Japanese. D: to ask a question. E: help accommodate others.</td>
    <td>A</td>
  </tr>
  <tr>
    <td></td>
    <td>What is PersonX seen as given PersonX agrees to positive social interaction?</td>
    <td>A: happy. B: hopeful. C: in need. D: gets a loan. E: saves.</td>
    <td>B</td>
  </tr>
  <tr>
    <td></td>
    <td>What is PersonX seen as given PersonX agrees to optimistic activity?</td>
    <td>A: gives someone a raise. B: to play at the park with the dog. C: soft-hearted. D: to get medical help. E: hopeful.</td>
    <td>E</td>
  </tr>
  <tr>
    <td rowspan="3">xEffect</td>
    <td></td>
    <td>What is the effect on PersonX after PersonX adopts loyal companion?</td>
    <td>A: helpful. B: excited. C: PersonX relaxes neck. D: PersonX names it. E: to call PersonY.</td>
    <td>D</td>
  </tr>
  <tr>
    <td></td>
    <td>What is the effect on PersonX after PersonX adopts four-legged friend?</td>
    <td>A: hopeful. B: to watch how he does. C: PersonX names it. D: strong. E: unite.</td>
    <td>C</td>
  </tr>
  <tr>
    <td></td>
    <td>What is the effect on PersonX after PersonX adopts faithful friend?</td>
    <td>A: give and use body to demonstrate skills. B: to have his own dog. C: blissful. D: Has no troubles. E: PersonX names it.</td>
    <td>E</td>
  </tr>
  <tr>
    <td rowspan="3">xIntent</td>
    <td></td>
    <td>What is the intention of PersonX before PersonX arranges job interview?</td>
    <td>A: try to get refund. B: to sign the petition. C: to analyze. D: to save money. E: opens the door.</td>
    <td>C</td>
  </tr>
  <tr>
    <td></td>
    <td>What is the intention of PersonX before PersonX arranges applicant evaluation?</td>
    <td>A: Waits for a response. B: Nosey. C: skinny. D: to analyze. E: to know what they want.</td>
    <td>D</td>
  </tr>
  <tr>
    <td></td>
    <td>What is the intention of PersonX before PersonX arranges interview process?</td>
    <td>A: to analyze. B: gets energized. C: PersonX sighs as PersonY’s dog barks loudly. D: bends down the body. E: to find someone to talk to.</td>
    <td>A</td>
  </tr>
  <tr>
    <td rowspan="3">xNeed</td>
    <td></td>
    <td>What does PersonX need to do before PersonX accepts into higher education institution?</td>
    <td>A: to apply to college. B: to see it succeed. C: to get a scissors. D: to defend their position. E: to congratulate PersonY.</td>
    <td>A</td>
  </tr>
  <tr>
    <td></td>
    <td>What does PersonX need to do before PersonX accepts into post-secondary institution?</td>
    <td>A: to apply to college. B: to help PersonY. C: very proud. D: Good letter. E: To be patient.</td>
    <td>A</td>
  </tr>
  <tr>
    <td></td>
    <td>What does PersonX need to do before PersonX accepts into university?</td>
    <td>A: to apply to college. B: donates to charity. C: to call his friend for playing. D: is no longer confused. E: good pay.</td>
    <td>A</td>
  </tr>
  <tr>
    <td rowspan="3">xReact</td>
    <td></td>
    <td>What does PersonX feel after PersonX asks medical expert?</td>
    <td>A: to avoid doing something. B: responsible. C: Goal setter. D: personX is snuggled by the cat. E: knowledgeable.</td>
    <td>E</td>
  </tr>
  <tr>
    <td></td>
    <td>What does PersonX feel after PersonX asks knowledgeable professional?</td>
    <td>A: gather materials. B: knowledgeable. C: to make friends. D: Wondering. E: remorseful.</td>
    <td>B</td>
  </tr>
  <tr>
    <td></td>
    <td>What does PersonX feel after PersonX asks trusted advisor?</td>
    <td>A: appreciative. B: elated that they have proved their client to be innocent. C: knowledgeable. D: to take some medicine. E: calls principal.</td>
    <td>C</td>
  </tr>
  <tr>
    <td rowspan="3">xWant</td>
    <td></td>
    <td>What does PersonX want to do after PersonX acts bizarre demeanor?</td>
    <td>A: to have looked at PersonY’s resume. B: to get to safety. C: Voters think about PersonX. D: finished. E: anxiou</td>
    <td>B</td>
  </tr>
  <tr>
    <td></td>
    <td>What does PersonX want to do after PersonX acts unusual behavior?</td>
    <td>A: to be dry. B: to be looked up to. C: Regretful. D: to go to the 19th hole for a drink. E: to get to safety.</td>
    <td>E</td>
  </tr>
  <tr>
    <td></td>
    <td>What does PersonX want to do after PersonX acts odd conduct?</td>
    <td>A: to get to safety. B: guilty. C: free-spirited. D: to reassure PersonY. E: to decide they like pizza.</td>
    <td>A</td>
  </tr>
</table>

Table 11: Examples of the comprehension task.
<table>
  <tr>
    <th>Type</th>
    <th>Subquestion</th>
    <th>Question</th>
    <th>Options</th>
    <th>Answer</th>
  </tr>
  <tr>
    <td rowspan="2">2t</td>
    <td>What is PersonX seen as given PersonX fills PersonY’s glass?</td>
    <td>What event or state is both what PersonX is seen as given PersonX fills PersonY’s glass and also what PersonX feels after PersonX gets beer?</td>
    <td>A: PersonX gets hit on. B: PersonX is happy. C: PersonX sees at school. D: PersonY goes to the beach. E: PersonX goes camping with PersonX’s friends.</td>
    <td>B</td>
  </tr>
  <tr>
    <td>What does PersonX feel after PersonX gets beer?</td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td rowspan="3">2p</td>
    <td>What is the effect on PersonX after PersonX does PersonX’s hair and makeup?</td>
    <td>What event or state is what PersonX needed to do before the effect on PersonX after PersonX does PersonX’s hair and makeup?</td>
    <td>A: PersonX is tired. B: PersonX curl hair. C: PersonX smiles. D: PersonX goes from bad to worse. E: PersonY communicate with PersonX.</td>
    <td>B</td>
  </tr>
  <tr>
    <td>What does PersonX need to do before PersonX looks pretty?</td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>What does PersonX need to do before PersonX work hard and well?</td>
    <td>What event or state is the effect on PersonX after both what PersonX needed to do before PersonX work hard and well, and also what PersonX is seen as given PersonX is deserving?</td>
    <td>A: PersonX finishes the movie. B: PersonX learns a new language. C: PersonX looks at persony. D: PersonX is sick. E: PersonX orders a cake.</td>
    <td>B</td>
  </tr>
  <tr>
    <td rowspan="3">ip</td>
    <td>What is PersonX seen as given PersonX is deserving?</td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>What is the effect on PersonX after PersonX gets promoted?</td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>What is the effect on PersonX after PersonX asks PersonY out on a date?</td>
    <td>What event or state is both what PersonX is seen as given the effect on PersonX after PersonX asks PersonY out on a date, and also the effect on PersonX after PersonX loses twenty pounds?</td>
    <td>A: PersonX is attractive. B: PersonX brews PersonX’s own beer. C: PersonX buys all the ingredients. D: PersonX speak out loud. E: PersonY is angry.</td>
    <td>A</td>
  </tr>
  <tr>
    <td rowspan="3">pi</td>
    <td>What is PersonX seen as given PersonX gets a date with PersonY?</td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>What is the effect on PersonX after PersonX loses twenty pounds?</td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
</table>

Table 12: Examples of the application task.

Reviewer paper summaries: This work establishes a large-scale benchmark named CoCo, which contains 39K samples from the Common Sense Knowledge Graph (CSKG), accompanied by symbolic questions and true answers, which can systematically evaluate the three subtasks of LLM: knowledge memory, comprehension, and application, and check the consistency between the three subtasks.
Summary of strengths: - This work divides the common sense evaluation task into three subtasks: memory, understanding, and application, and establishes a consistency evaluation system between these tasks.
- Extensive experiments are conducted on 7 LLMs, and a thought chain prompt strategy is proposed to induce consistent common sense reasoning in LLMs, which significantly improved the consistency score of LLMs on CoCo.
Summary of weaknesses: **Q1: Limited quality check.** Note that the quality check of the dataset is performed on only 100 selected cases and evaluated by only one annotator. For a dataset with a total size of 39K, such quality check may not be sufficient.

**Q2: Evaluation metrics.** The evaluation metrics "Application" introduced in sec4 may need to be combined with quantitative analysis of manual evaluation to prove the reliability of the metrics, such as how many samples actually correctly memorize atomic knowledge but are considered negative by the "Application" metrics.
Comments, suggestions and typos: Some tables, such as Table3, have fonts that are too small to be read.
Overall assessment scores: 3.5
Metareview score: 3
Metareview: This paper presents CoCo (Consistency of Commonsense), a new large-scale benchmark for systematically evaluating the inter-task consistency of commonsense knowledge in LLMs. Unlike previous benchmarks that assess memorization or reasoning in isolation, CoCo enforces cross-task consistency among three stages: memorization (of atomic facts), comprehension (through conceptual abstraction), and application (in complex multi-hop reasoning). The authors construct 39K samples from the ATOMIC commonsense knowledge graph and use a pipeline integrating Sentence-BERT sampling, plausibility filtering via Vera, and adversarial distractor generation to ensure both scale and quality without human annotation.

In addition, the paper proposes a novel prompting strategy, KnowCoT, to induce more consistent reasoning behavior in LLMs, and introduces a suite of metrics (MemScore, ComScore, ReaScore, FaiScore, AppScore) to quantify different aspects of knowledge flow and consistency. Experiments across seven models, including recent reasoning-optimized models like GPT-4o and DeepSeek R1, show that while LLMs may excel at knowledge memorization, faithful and consistent application remains a significant challenge. This benchmark aims to identify and quantify such weaknesses and offer tools to improve model design.
Summary of reasons to publish: - The paper introduces a conceptually novel evaluation framework that links memorization, comprehension, and application into a unified commonsense reasoning pipeline.
- The fully automated benchmark construction pipeline (with adversarial distractor generation and plausibility filtering) enables high-quality, scalable evaluation without manual annotations, while maintaining competitive coverage and difficulty.
- The proposed metrics provide interpretable, fine-grained insights into the failure modes of LLMs, especially regarding how memorized knowledge fails to translate into consistent reasoning in complex contexts.
- The authors conduct comprehensive empirical evaluation on a range of LLMs, including state-of-the-art and reasoning-specialized models, and supplement the findings with detailed human performance baselines and error analyses.