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\title{K-NLPers at SemEval-2026 Task 7: Multiple LLM Agent Debate System for Everyday Knowledge Across Diverse Languages and Cultures}

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\author{
 \textbf{Jiwoo Song\footnotemark[1]},
 \textbf{Sihyeong Yeom\footnotemark[1]} and
 \textbf{Harksoo Kim}
\\
\\
 Department of Artificial Intelligence, Konkuk University
\\
\\
 \small{
   \textbf{Correspondence:} \href{mailto:email@domain}{nlpdrkim@konkuk.ac.kr}
 }
}

\begin{document}
\maketitle

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\footnotetext{* These authors contributed equally to this work.}
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\begin{abstract}
This paper presents the K-NLPers system for SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures. The task extends the BLEnD benchmark to evaluate cultural understanding of language models across more than 30 language-country pairs. Although Large Language Models (LLMs) achieve strong overall performance, they exhibit performance disparities across cultural contexts and tend to produce regionally biased responses. To address this limitation, we propose a continent-based multi-agent debate framework that leverages culture-specific performance differences instead of relying on a single model. For the Short Answer Question (SAQ) track, we employ three agents: a general-purpose model, a continent-specific model, and a country-level or culturally adjacent model. These agents engage in independent generation, mutual refinement, and final adjudication. For the Multiple-Choice Question (MCQ) track, we adopt a debate structure centered on high-performing general-purpose models due to the track's simpler structure. Our system participated in all language-region pairs and achieved overall scores of 55.75 on SAQ and 88.32 on MCQ. Further analysis reveals that grouping the performance of various individual models by continent explains performance patterns more consistently than language-based grouping, highlighting the importance of cultural and historical context in model generalization.
\end{abstract}

\begin{figure}[t]
    \centering
    \includegraphics[width=\columnwidth]{framework.png}
    \caption{Overview of the multi-agent debate framework for the SAQ track. The framework consists of three stages--independent response generation, mutual critique and refinement, and final adjudication. Multiple agents first produce initial answers, revise them after reviewing peer responses, and a judge model then selects the final output.}
    \label{fig:framework}
\end{figure}

\section{Introduction}

Large Language Models (LLMs) achieve strong performance across natural language understanding and generation tasks~\cite{survey, baek}, yet they remain limited in capturing cultural context~\cite{woojin}. Due to pretraining data concentrated on specific regions and high-resource languages, LLMs often overrepresent Western-centric perspectives and underrepresent low-resource languages and regions. This limitation highlights the need for structural approaches that better accommodate cultural diversity.

SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures~\cite{semeval2026workshop, semeval2026task7} extends the BLEnD~\cite{blend} benchmark to evaluate culturally grounded everyday knowledge across more than 30 language-country pairs. The SAQ track requires the open-ended generation of culturally appropriate responses, while the MCQ track evaluates the selection of culturally appropriate answers from multiple options.

We begin from the observation that a single model does not perform uniformly across cultural contexts. For SAQ, we employ three agents: a general-purpose model, a continent-specific model, and a country-level or culturally adjacent model. These agents derive the final answer through independent generation, mutual refinement, and final decision-making. For MCQ, we maintain the same debate structure but use high-performing general-purpose models to ensure stable predictions.

The contributions of this paper are as follows:

\begin{itemize}[noitemsep, topsep=0pt]
    \item We propose a multi-LLM debate-based inference framework for modeling cultural context.
    \item We systematically analyze country- and region-level performance differences, demonstrating culture-specific model strengths.
    \item We design differentiated strategies for SAQ and MCQ and experimentally validate their effectiveness.
\end{itemize}

\section{Related Work}
\subsection{Cultural Bias and Multilingual Cultural Evaluation Benchmarks}

As LLMs become globally deployed, understanding cultural context has emerged as a critical challenge. BLEnD~\cite{blend} evaluated everyday knowledge across 16 countries and demonstrated that country-specific knowledge differences persist even within the same language group. KoBBQ~\cite{kobbq} further showed that cultural context cannot be preserved through simple translation.

While these benchmarks diagnose cultural bias, they do not propose mechanisms to mitigate performance imbalances. In contrast, our work incorporates empirically observed culture-specific model strengths into a multi-agent debate framework to reduce cross-cultural performance gaps.

\subsection{Multi-Agent Debate and Expert-Integrated LLM Collaboration}

Multi-agent debate frameworks have been proposed to improve LLM reasoning~\cite{mad3}. Prior work shows that independent generation followed by iterative cross-review can outperform single-model baselines~\cite{mad}. However, debate may also reinforce shared misconceptions or converge to majority bias when models exhibit similar inductive biases~\cite{mad2}. To address this issue, intervention strategies such as \textit{Misconception Refutation} have been introduced.

We extend the multi-agent debate paradigm to culturally grounded evaluation. By constructing an agent pool that reflects task characteristics and culture-specific model strengths, we design a three-stage architecture consisting of independent response generation, mutual critique \& refinement, and final adjudication.

\begin{table*}[t]
\centering
\resizebox{\textwidth}{!}{%
\begin{tabular}{l|cc|cc|ccc|cc|ccc|cc|c}
\hline
\multicolumn{1}{c|}{\multirow{2}{*}{Model}} & \multicolumn{2}{c|}{Africa} & \multicolumn{2}{c|}{Middle East} & \multicolumn{3}{c|}{Europe} & \multicolumn{2}{c|}{Americas} & \multicolumn{3}{c|}{East Asia} & \multicolumn{2}{c|}{SE Asia} & \multirow{2}{*}{overall} \\ \cline{2-15}
\multicolumn{1}{c|}{} & am-ET & ha-NG & ar-SA & fa-IR & fr-FR & en-GB & es-ES & en-US & es-MX & zh-CN & ja-JP & ko-KR & id-ID & tl-PH & \\ \hline
gpt-4o-mini & 29.6 & 29.8 & \textbf{51.8} & \textbf{63.8} & 70.4 & 72.4 & 62.0 & 75.8 & 58.6 & 66.8 & 60.0 & 64.8 & 67.4 & \textbf{62.6} & 52.97 \\
gemma-3-27b-it & 25.4 & 27.0 & 46.4 & 61.6 & 64.6 & 71.8 & 56.8 & 73.4 & 49.0 & 69.0 & 58.4 & 71.2 & 70.6 & 56.0 & 49.14 \\
Qwen2.5-14B-Instruct & 14.0 & 10.4 & 40.0 & 39.2 & 66.2 & \textbf{73.6} & 62.8 & \textbf{77.4} & 60.2 & 73.6 & 59.0 & 56.8 & 63.8 & 38.6 & 45.26 \\
EuroLLM-22B-Instruct & 2.2 & 5.6 & 41.6 & 29.0 & 70.0 & 72.4 & 66.4 & 76.0 & \textbf{61.0} & 68.2 & 53.0 & 63.6 & 49.4 & 29.4 & 46.10 \\ \hline
\textbf{Ours} & \textbf{50.6} & \textbf{40.8} & 46.8 & 54.0 & \textbf{71.6} & 72.6 & \textbf{69.6} & 75.6 & 59.6 & \textbf{78.0} & \textbf{63.6} & \textbf{78.0} & \textbf{73.2} & 60.0 & \textbf{55.75} \\ \hline
\end{tabular}%
}
\caption{SAQ performance of major single-model baselines and the proposed framework across representative locales from each continent. Performance differences are observed across locales, with general-purpose and region-specific models exhibiting varying strengths depending on cultural context. The proposed multi-agent debate framework integrates these complementary tendencies, leading to more consistent performance across culturally diverse settings.}
\label{tab:main_results}
\end{table*}


\section{System Overview}

Our system is a multi-LLM debate-based inference framework for culturally grounded response generation. It consists of three stages: (1) independent response generation, (2) mutual critique and refinement, and (3) final adjudication. Figure~\ref{fig:framework} illustrates the overall debate process for the SAQ track.

\subsection{Multi-Agent Debate Framework}
\label{sec:framework}

Given a question $q$, we construct a set of $N$ agents $A$. Each agent $A_i$ generates responses through the following procedure:

\begin{equation}
A = \{A_1, A_2, \cdots, A_N\}.
\end{equation}

\paragraph{Independent Response Generation.}
Each agent independently produces an initial response $r_i^{(0)}$ to the question $q$:

\begin{equation}
r_i^{(0)} = A_i(q).
\end{equation}
At this stage, agents do not access each other's outputs.

\paragraph{Mutual Critique and Refinement.}
Each agent receives the set of initial responses. $R^{(0)}$ from the other agents and generates a revised response $r_i^{(1)}$ by reviewing and refining them:

\begin{equation}
R^{(0)} = \{r_1^{(0)}, r_2^{(0)}, \cdots, r_N^{(0)}\}.
\end{equation}

\begin{equation}
r_i^{(1)} = A_i\bigl(q, R^{(0)} \setminus r_i^{(0)}\bigr).
\end{equation}
This step compensates for missing cultural context and mitigates individual model biases.

\paragraph{Final Adjudication.}
A judge model $J$ determines the final answer $\hat{y}$ based on the question $q$ and the revised responses:

\begin{equation}
R^{(1)} = \{r_1^{(1)}, r_2^{(1)}, \cdots, r_N^{(1)}\}.
\end{equation}

\begin{equation}
\hat{y} = J(q, R^{(1)}).
\end{equation}

The judge selects the final output by evaluating consistency, validity, and cultural appropriateness. In the case of MCQ, unlike SAQ which involves open-ended outputs, the output structure is constrained to a fixed set of answer options. Therefore, when the agents produce the same final choice (i.e., select the same option number), we omit the use of a Judge LLM to improve computational efficiency.

\subsection{Agent Configuration for SAQ}

The SAQ track is an open-ended generation problem that requires culturally grounded responses. To reflect culture-specific strengths, we construct a three-agent configuration.

The agent set is define as follows:

\begin{equation}
A_{\text{SAQ}} = \{A_{\text{gen}}, A_{\text{cont}}, A_{\text{loc}}\}.
\end{equation}

\begin{itemize}
    \item \textbf{$A_{\text{gen}}$:} a high-performing general-purpose model with stable overall performance.
    \item \textbf{$A_{\text{cont}}$:} a model developed within the continent of the target country.
    \item \textbf{$A_{\text{loc}}$:} a country-specific model or one developed in a culturally adjacent region.
\end{itemize}

This configuration reflects model- and region-level performance differences identified in our preliminary analysis (Section~\ref{sec:5.3}), where continent- or country-developed models showed relatively higher scores on related regional questions.

The three agents derive the final response through the three-stage debate process described in Section~\ref{sec:framework}.

\subsection{Agent Configuration for MCQ}

The MCQ track is a multiple-choice classification problem. Unlike SAQ, it evaluates the selection of culturally appropriate answers from predefined candidates. Accordingly, we do not employ culture-specific agent specialization.

While retaining the same multi-agent debate framework, we construct the agent set using high-performing general-purpose LLMs with stable overall performance. Given the structural simplicity and constrained response space of MCQ, the focus is on improving decision consistency rather than generative diversity.

\section{Experimental Setup}
\paragraph{Model.}

We perform no additional training or fine-tuning. All experiments are conducted in an inference-only setting using pretrained LLMs as-is.

For the SAQ track, we use ten LLMs to construct the multi-agent debate framework. For each question, three debater agents are instantiated, with \texttt{gpt-4o-mini} fixed as the $A_{\text{gen}}$ due to its stable performance. The remaining two agents are selected from models developed within the continent or country corresponding to the target locale. We use \texttt{gemma3-27b-it} as the judge model for SAQ. The country-level routing table is provided in Appendix~\ref{sec:appendix_A}. The models used in our experiments are as follows:
\begin{itemize}[noitemsep, topsep=0pt]
    \item gpt-4o-mini~\cite{gpt}
    \item gemma-3-27b-it~\cite{gemma}
    \item Llama-3.1-8B-Instruct~\cite{llama}
    \item Midm-2.0-Base-Instruct~\cite{midm}
    \item Mistral-Nemo-Japanese-Instruct-2408~\cite{cyberagent-mistral-nemo-japanese-instruct-2408}
    \item Qwen2.5-14B-Instruct~\cite{qwen2.5}
    \item Qwen-SEA-LION-v4-32B-IT~\cite{sealion}
    \item Falcon3-10B-Instruct~\cite{Falcon3}
    \item Mistral-7B-Instruct-v0.3~\cite{mistral}
    \item EuroLLM-22B-Instruct-2512~\cite{eurollm}
\end{itemize}

For MCQ, we construct the agent set using high-performing general-purpose LLMs. Specifically, \texttt{gpt-4o-mini}~\cite{gpt} and \texttt{claude-3.5-haiku}~\cite{claude3} serve as debaters, and \texttt{gpt-4o}\cite{gpt4o} serves as the judge.

\paragraph{Implementation Details.}
Open-source LLM experiments were conducted on two NVIDIA A6000 GPUs. Closed-source models were accessed via their official APIs. For all models, the temperature was set to 0 to ensure deterministic, fact-based responses.

\section{Results \& Analysis}
\subsection{SAQ Result}

Table \ref{tab:main_results} reports performance on representative locales from each continent. The overall column reflects the official score computed across all SAQ locales rather than the average of representative ones. Detailed results are provided in Appendix~\ref{sec:appendix_B}.

Our method achieved an overall score of 55.75, improving by approximately 2.8 points over the best single model, \texttt{gpt-4o-mini} (52.97). This confirms that the multi-agent debate framework yields consistent gains over single-model baselines in open-ended culturally grounded generation tasks.

Improvements were most pronounced in low-resource languages. In locales such as Amharic (am-ET) and Hausa (ha-NG), where single-model performance was low, Ours achieved substantial gains. In contrast, improvements were limited for high-resource languages such as English (en-US, en-GB), where baseline performance was already strong. This suggests that multi-agent debate enhances robustness in settings with larger performance inconsistencies.

Including locally or regionally developed models as debate agents further contributed to performance gains. For example, \texttt{Qwen2.5} was used for Chinese, \texttt{Midm} for Korean, and \texttt{Mistral} and \texttt{EuroLLM} for European languages, leveraging region-specific strengths. By combining regional cultural signals with general-purpose stability, the proposed method maintains strong performance across diverse cultural contexts. However, in regions such as the Middle East (ar-SA, fa-IR), where low-performing models were included as agents, they appeared to introduce noise rather than complementary cultural signals, resulting in performance degradation. This highlights the importance of agent quality in the debate framework.

\begin{table}[t] % 별표 없는 table로 시작
\centering
\scriptsize 
\setlength{\tabcolsep}{4pt} 
\begin{tabular}{l ccc|cc}
\toprule
Locale/Region & $S(M_1)$ & $S(M_2)$ & $D$ & $\Delta_1$ & $\Delta_2$ \\
\midrule
am-ET & \textbf{68.46} & 61.82 & 66.36 & -2.10 & +4.54 \\
ar-DZ & 91.27 & 90.73 & \textbf{92.23} & +0.96 & +1.50 \\
ar-EG & 90.22 & 89.67 & \textbf{91.03} & +0.81 & +1.36 \\
ar-MA & 77.53 & 79.19 & \textbf{80.85} & +3.32 & +1.66 \\
ar-SA & 76.58 & 79.28 & \textbf{81.31} & +4.73 & +2.03 \\
as-AS & 76.70 & 77.97 & \textbf{78.95} & +2.25 & +0.98 \\
az-AZ & 80.37 & 77.80 & \textbf{81.28} & +0.91 & +3.48 \\
bg-BG & \textbf{96.60} & 95.06 & 95.68 & -0.92 & +0.62 \\
el-GR & \textbf{93.93} & 92.39 & \textbf{93.93} & 0.00 & +1.54 \\
en-AU & 92.20 & 88.11 & \textbf{92.59} & +0.39 & +4.48 \\
en-GB & 94.97 & 94.32 & \textbf{95.80} & +0.83 & +1.48 \\
en-US & 95.37 & 94.64 & \textbf{95.62} & +0.25 & +0.98 \\
es-EC & 98.46 & \textbf{98.57} & \textbf{98.57} & +0.11 & 0.00 \\
es-ES & 94.30 & 91.09 & \textbf{95.18} & +0.88 & +4.09 \\
es-MX & \textbf{94.21} & 93.42 & 94.10 & -0.11 & +0.68 \\
eu-PV & 87.53 & 86.60 & \textbf{90.79} & +3.26 & +4.19 \\
fa-IR & 77.45 & 77.70 & \textbf{79.10} & +1.65 & +1.40 \\
fr-FR & \textbf{96.09} & 93.49 & \textbf{96.09} & 0.00 & +2.60 \\
ga-IE & 90.07 & 89.02 & \textbf{91.24} & +1.17 & +2.22 \\
ha-NG & 79.18 & 73.75 & \textbf{79.43} & +0.25 & +5.68 \\
id-ID & 84.56 & 86.52 & \textbf{87.77} & +3.21 & +1.25 \\
ja-JP & 83.17 & \textbf{85.85} & \textbf{85.85} & +2.68 & 0.00 \\
ko-KP & \textbf{73.59} & 71.08 & 73.50 & -0.09 & +2.42 \\
ko-KR & 88.22 & \textbf{89.49} & 90.13 & +1.91 & +0.64 \\
su-JB & 83.75 & 78.38 & \textbf{87.55} & +3.80 & +9.17 \\
sv-SE & 82.55 & 86.13 & \textbf{87.47} & +4.92 & +1.34 \\
ta-LK & \textbf{96.95} & 92.73 & 95.96 & -0.99 & +3.23 \\
tl-PH & 87.72 & 87.26 & \textbf{88.47} & +0.75 & +1.21 \\
zh-CN & 87.82 & 88.34 & \textbf{90.88} & +3.06 & +2.54 \\
zh-SG & 91.36 & 90.65 & \textbf{91.82} & +0.46 & +1.17 \\
\midrule
\textbf{Overall} & 85.67 & 84.30 & \textbf{88.32} & +2.65 & +4.02 \\
\bottomrule
\end{tabular}
\caption{Main results for Track 2 (MCQ). Comparison of single-model accuracies $S(M_1)$ and $S(M_2)$ with the final performance $D$ achieved by the debate-based framework across locales/regions. Here, $M_1$ and $M_2$ denote \texttt{gpt-4o-mini} and \texttt{claude-3.5-haiku}, respectively. $\Delta_1$ and $\Delta_2$ represent $D - S(M_1)$ and $D - S(M_2)$, respectively.}
\label{tab:locale_results}
\end{table}

\subsection{MCQ Result}

Table~\ref{tab:locale_results} reports locale-level performance and overall averages for the MCQ track. The proposed multi-agent debate model achieved an overall accuracy of 88.32, improving by 2.65 points over \texttt{gpt-4o-mini} (85.67) and 4.02 points over \texttt{claude-3.5-haiku} (84.30).

While slight decreases were observed in a few locales (e.g., am-ET, bg-BG), most locales showed positive gains. This indicates that multi-agent feedback mitigates individual model biases and leads to more stable decisions.

Although MCQ does not use culture-specific agent specialization, the debate structure itself functions as a mechanism for mutual error correction during option comparison.


\begin{figure*}[t]
    \centering
    % (a) 대륙별 분석: 미세하게 너비를 줄임 (0.38 -> 0.36)
    \begin{subfigure}[b]{0.37\textwidth}
        \centering
        \includegraphics[width=\textwidth]{country.png}
        \caption{Performance analysis by Continental Grouping.}
        \label{fig:analysis_country}
    \end{subfigure}
    \hspace{0.02\textwidth} % 간격 유지
    % (b) 언어별 분석: 미세하게 너비를 키워 높이를 맞춤 (0.58 -> 0.60)
    \begin{subfigure}[b]{0.59\textwidth}
        \centering
        \includegraphics[width=\textwidth]{language.png}
        \caption{Performance analysis by Language Grouping.}
        \label{fig:analysis_language}
    \end{subfigure}
    
    \caption{Agent selection analysis for SAQ. The results compare SEM-B scores across different continental and linguistic groupings to determine the optimal agent configuration.}
    \label{fig:agent_selection_analysis}
\end{figure*}

\subsection{Agent Selection Analysis for SAQ}
\label{sec:5.3}

To determine the SAQ agent configuration, we analyzed single-model performance across language-country pairs. From the BLEnD~\cite{blend} dataset, we randomly selected 150 samples per country and evaluated them according to the official evaluation protocol. In accordance with SemEval-2026 Task 7 regulations, the data were used solely for analysis and not for training or fine-tuning.

We compared the average single-model performance across groups under two grouping criteria: (1) continental grouping, (2) language grouping.

\paragraph{Continental Grouping.}

When aggregated by continent, models developed within the corresponding continent showed higher average performance, particularly in Europe and East Asia. For example, \texttt{EuroLLM} performed strongly in European regions, and \texttt{Qwen2.5} in Chinese contexts. In Africa, European-based models also showed relatively strong performance, possibly reflecting historical and linguistic connections.

\paragraph{Language Grouping.}

Under language-based grouping, country-specific or culturally adjacent models performed well in  single-country-centered languages (e.g., Korean, Chinese, Indonesian). However, for languages spoken across multiple countries (e.g., English, Spanish), no consistent country-level dominance was observed, and performance varied within the same language.

Overall, language grouping explained certain localized advantages but lacked consistent global patterns. In contrast, continental grouping yielded more stable and interpretable trends. Accordingly, we adopted continent-based agent selection for SAQ.

\section{Conclusion}

We proposed a multi-LLM debate-based inference framework to address the limitations of single models in culturally grounded multilingual settings. Based on observed model- and country-level performance variations, we adopted continent- and region-based agent selection for SAQ, while using high-performing general-purpose models for MCQ to ensure stable classification.

The proposed method achieved overall scores of 55.75 on SAQ and 88.32 on MCQ. The multi-agent debate framework improved culturally grounded scores over single-model baselines, particularly in open-ended generation. These results highlight the importance of structurally incorporating cultural diversity in multilingual evaluation benchmarks.

\section{Ethical Considerations}

Our study does not involve new data collection or human subjects, but continent-level grouping may still simplify cultural diversity and should therefore be interpreted with care.

\section*{Limitations}

We employ a static agent configuration for both SAQ and MCQ. Recent routing and orchestration approaches dynamically adjust model selection or collaboration based on query characteristics (e.g., MASRouter~\cite{masrouter}; Agent-Oriented Planning~\cite{aop}). The absence of such dynamic mechanisms remains a limitation of our framework.

\section*{Acknowledgments}
This work was supported by Institute for Information \& communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (RS-2024-00343989, Enhancing the Ethics of Data Characteristics and Generation AI Models for Social and Ethical Learning).

This work was supported by the National Research Foundation of Korea(NRF) grant
funded by the Korea government(MSIT) (RS-2025-00553041, Enhancement of Rational and Emotional Intelligence of Large Language Models for Implementing Dependable Conversational Agents).

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\appendix
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