Venue: NAACL 2025

# Conflict and Overlap Classification in Construction Standards

Using a Large Language Model

Seong-Jin Park\({}^{1}\), Youn-Gyu Jin\({}^{1*}\), Hyun-Young Moon\({}^{1*}\), Bong-Hyuck Choi\({}^{3}\), Seung-Hwan Lee\({}^{3}\), Ohjoon Kwon\({}^{4}\), Kang-Min Kim\({}^{1,2}\)

\({}^{1}\)Department of Artificial Intelligence \({}^{2}\)Department of Data Science

The Catholic University of Korea, Bucheon, Republic of Korea

\({}^{3}\)Korea Institute of Civil Engineering and Building Technology, Goyang, Republic of Korea

\({}^{4}\)Naver, Seongnam, Republic of Korea

{sjjpark,wlsdbsrb,hyuyounge03,kangmin89}@catholic.ac.kr

{bhchoi,seunghwanilee}@kit.re.kr, ohjoon1209@gmail.com

These authors contributed equally to this work.Corresponding author.Corresponding author.

###### Abstract

Construction standards across different countries provide technical guidelines to ensure the quality and safety of buildings and facilities, with periodic revisions to accommodate advances in construction technology. However, these standards often contain overlapping or conflicting content owing to their broad scope and interdependence, complicating the revision process and creating public inconvenience. Although current expert-driven manual approaches aim to mitigate these issues, they are time-consuming, costly, and error-prone. To address these challenges, we propose conflict and overlap classification in construction standards using a large language model (COSLLM), a framework that leverages a construction domain-adapted large language model for the semantic comparison of sentences in construction standards. COSLLM utilizes a two-step reasoning process that adaptively employs chain-of-thought reasoning for the in-depth analysis of sentences suspected of overlaps or conflicts, ensuring computational and temporal efficiency while maintaining high classification accuracy. The framework achieved an accuracy of 97.9% and a macro F1-score of 0.907 in classifying real-world sentence pairs derived from Korean construction standards as overlapping, conflicting, or neutral. Furthermore, we develop and deploy a real-time, web-based system powered by COSLLM to facilitate the efficient establishment and revision of construction standards.

## 1 Introduction

National construction standards provide technical guidelines for engineers, contractors, and other construction professionals to ensure the quality and safety of buildings and facilities [5]. While the establishment and management of these standards vary by country, they are often grounded in legal frameworks1 or standard codes2. Some countries, such as Iceland, adopt modified versions of international standards, including the Eurocodes3, to meet local environmental requirements. Continuous advancements in civil engineering and legal systems necessitate continual revisions to construction standards. Many countries have national agencies or committees, such as the American National Standards Institute4, the Construction Industry Council5, and the Korea Construction Standards Center (KCSC)6, to oversee these revisions.

Footnote 1: [https://laws.e-gov.go.jp/](https://laws.e-gov.go.jp/)

Footnote 2: [https://codes.iccsafe.org/content/IBC2021P2](https://codes.iccsafe.org/content/IBC2021P2)

Footnote 3: [https://eurocedes.jrc.ec.europa.eu](https://eurocedes.jrc.ec.europa.eu)

Footnote 4: [https://www.ansi.org/](https://www.ansi.org/)

Footnote 5: [https://www.cioc.org.uk/](https://www.cioc.org.uk/)

Footnote 6: [https://www.kscc.re.kr/](https://www.kscc.re.kr/)

In the process of establishing or revising standards, members of the construction standards revision committee focus on preventing overlaps and conflicts between new and existing standards [14]. When overlapping content exists between constructio

Figure 1: An overview of the manual and LLM-based approaches for analyzing overlapping and conflicting content in construction standards is provided. Using the proposed COSLLM, managers can review potential overlaps and conflicts identified by the LLM, along with detailed rationales, which significantly reduces manual effort.



dard may lead to conflicts in interpretation, causing confusion among construction professionals. Such conflicts can disrupt the assurance of quality and safety during the construction of buildings and facilities. To address these challenges, some countries have adopted methods such as explicitly referencing existing standards when citing content already covered by regulations, while also conducting routine reviews to resolve overlaps and conflicts (Kim et al., 2016) (Figure 1). However, this expert-driven approach is both time-consuming and costly. Furthermore, excessive reliance on expert interpretations may result in inconsistent judgments among experts (Sun and Zhang, 2014). In South Korea, where revisions occur more frequently than in other countries, effectively resolving issues of overlap and conflict in construction standards is critical (Choi, 2020).

Recently, deep learning-based methods have been employed to classify overlaps and conflicts across various domains (Abeba and Alemmeh, 2022; Malik et al., 2022). Previous study (Malik et al., 2024) has defined sentence relationship analysis as being closely aligned with natural language inference (NLI) tasks (Bowman et al., 2015), providing a foundation for analyzing sentences in construction standards. Recent studies (Lee et al., 2023; OpenAI et al., 2024; Street et al., 2024) have demonstrated that large language models (LLMs), equipped with human-level reasoning capabilities, excel at NLI tasks. In addition, the use of chain-of-thought reasoning (CoT) (Wei et al., 2022b) in LLMs enables reliable explanations of the reasoning process (Wei Jie et al., 2024), with the potential to assist construction standard managers in analyzing overlapping or conflicting sentences more effectively. Accordingly, we reframe the classification of overlaps and conflicts in construction standards as a 3-class NLI problem (including neutrality) that can be solved effectively using LLMs.

In this paper, we propose a novel framework for automatically classifying overlaps and conflicts in construction standards, referred to as **C**onflict and **O**verlap classification in construction **S**tandards using a **L**arge **L**anguage **M**odel (COSLLM). COSLLM, built on the latest open-source LLM, is enhanced through two additional training stages. In the first stage, we adapt the LLM to the construction domain using a corpus comprising construction standards, research publications, and news articles. In the second stage, we fine-tune the model to classify sentences into overlap, conflict, or neutral categories using expert-annotated, high-quality sentence pairs from construction standards. We incorporate CoT to handle subtle semantic differences, applying it selectively through task prefixes (Hsieh et al., 2023). This strategy optimizes computational efficiency while maintaining high accuracy. Experiments on real-world construction standard data demonstrated the efficacy of COSLLM. In addition, to support the establishment and revision of construction standards using COSLLM, we develop a real-time construction standards analysis system, which has been deployed. Our main contributions are as follows:

1. We propose COSLLM, an LLM-based framework that automatically classifies overlapping and conflicting sentences, facilitating the establishment and revision of national construction standards.
2. We enhance the effectiveness of an opensource LLM by incorporating domain adaptation and selective CoT, achieving high accuracy in classifying overlaps, conflicts, and neutral relationships.
3. We demonstrate the effectiveness of COSLLM through strong performance in experiments with real-world construction standards data, achieving an accuracy of 97.9% and a macro F1-score of 0.907, highlighting its practical applicability.
4. We develop and deploy a real-time, interactive system powered by COSLLM to significantly improve efficiency and usability in construction standard management.

## 2 Related Work

Overlap and Conflict ClassificationClassifying overlaps and conflicts in textual data poses a significant challenge across various domains (Schmolze and Snyder, 1999; Gambo et al., 2024), with deep learning-based technologies are increasingly being explored to address this issue. In the medical research field, algorithms combining string matching, machine learning, and clustering techniques have been developed to automatically detect and remove duplicate data from large-scale bibliographic references across multiple databases, enhancing data quality and reducing manual effort (Hair et al., 2023). In software development, researchers have proposed (Malik et al., 2024) a transfer-learned 

model built on the SR-BERT architecture (Aum and Choe, 2021), which integrates Sentence-BERT Reimers (2019) with a bi-encoder structure. Their proposed model, fine-tuned with domain-specific data, effectively resolves ambiguities and identifies conflicts in development requirements. Building on these advancements, our study employs LLM to address overlaps and conflicts in construction standards, focusing on scalability, domain adaptation, and real-time applicability.

Large Language ModelLLMs, built on the transformer Vaswani et al. (2017) decoder-only architecture and trained with billions of parameters, excel at capturing linguistic patterns and demonstrate advanced reasoning and generation capabilities across diverse tasks Zhao et al. (2024). Models such as GPT-4 OpenAI et al. (2024) exhibit capabilities such as long-context understanding Kuratov et al. (2024), showcasing abilities in in-context reasoning with few-shot Brown et al. (2020) and zero-shot Radford et al. (2019); Brown et al. (2020) learning. However, LLMs trained on general-purpose datasets often lack the domain-specific vocabulary and contextual understanding necessary for specialized applications Ling et al. (2024). Previous studies Gururangan et al. (2020); Guo and Yu (2022); Jiang et al. (2024) have demonstrated that achieving high performance with LLMs in specialized domains requires training on tailored corpora. Consequently, fields such as law Colombo et al. (2024) and medicine Yang et al. (2024b) have successfully adapted LLMs to fulfill their unique requirements. To further enhance LLM capabilities for complex tasks, techniques such as CoT Wei et al. (2022) and plan-and-solve prompting Wang et al. (2023) have been developed. Building on these findings, our research aims to optimize LLMs for resolving overlaps and conflicts in construction standards.

## 3 Method

Our framework, COSLLM, leverages LLM to classify semantic relationships between construction standard sentences. Section 3.1, describes how we adapt open-source LLM for the construction domain. Section 3.2 outlines the method for fine-tuning the LLM to classify sentence pairs. Finally, Section 3.3 introduces a real-time web-based system powered by the COSLLM to assist in establishing and revising of construction standards.

### Adapting LLM to Construction Domain

Construction Domain-specific CorpusTo address the limitations of general-purpose LLMs in understanding the specialized construction terminology, we curate a construction domain-specific corpus. As no open-source corpus is available, we collect full texts of construction standards, research publications, and news articles. Key sources include the Korea Construction Standards Center6, the Korea Agency for Infrastructure Technology Advancement7, the Korean Society of Civil Engineers8, and construction-related news outlets such as the Civil Engineering Newspaper9 and Construction Love10. Our curated corpus comprises approximately 7.42 million tokens, as measured using the Qwen2 tokenizer Yang et al. (2024a).

Footnote 7: [https://www.kcsc.re.kr/](https://www.kcsc.re.kr/)

Footnote 8: [https://www.kaia.re.kr/portal/main.do](https://www.kaia.re.kr/portal/main.do)

Footnote 9: [https://www.ksce.or.kr/](https://www.ksce.or.kr/)

Footnote 10: [http://www.conews.co.kr/](http://www.conews.co.kr/)

Domain Adaptation ProcessUsing the curated corpus, we fine-tune the open-source multilingual LLM Qwen2-7B-Instruct Yang et al. (2024a) through causal language modeling. We conduct training over 10 epochs using three Nvidia A6000 GPUs, lasting approximately 3.4 days and incurring a total computational cost of 3.378e18 FLOPs. During training, the loss decreases from 2.502 to 0.581, indicating significant performance improvement. Given the improved classification performance after domain adaptation (DA) (see Section 4.4), we demonstrate that DA enhances the model's ability to comprehend the semantic relationships within the construction domain.

### Two-Step Classification of Overlap, Conflict, and Neutrality Using LLMs

Rationale for the Sentence Pair ApproachThe ideal solution that maximizes efficiency and simplifies the system would involve an LLM trained specifically in the construction domain to fully understand the entire corpus of construction standards. Such a model can directly analyze sentences or paragraphs to identify overlaps or conflicts, eliminating the need for sentence pairs or neutrality classification. However, this approach necessitates retraining the model whenever the standards are updated, which is both resource-intensive and impractical owing to the specialized nature of construction standards and the limited user base; for instance, 

the KCSC currently has only 16 committee members12. Although a smaller LLM with fewer than 10 billion parameters is computationally efficient, we empirically observed that its limited size constrains its ability to comprehend the entire corpus, restricting its paragraph-level reasoning capabilities (see Appendix A). To balance effectiveness and efficiency, we adopt a sentence-pair approach. This approach formulates the task as a 3-class NLI problem, where the LLM predicts the semantic relationship between two input sentences.

Footnote 12: [https://www.kcsc.re.kr/Intro/Business](https://www.kcsc.re.kr/Intro/Business)

Inference Time Reduction ModuleDespite the effectiveness of LLMs, our system faces efficiency challenges owing to the high computational costs of processing numerous sentence pairs, particularly when many are neutral. To mitigate this issue, we leverage the strong semantic similarity of overlapping or conflicting pairs, in contrast to neutral pairs, to pre-filter most of the neutral sentence pairs. Our inference time reduction module (ITRM) utilizes a transformer Vaswani et al. (2017) encoder-based pre-trained language model (PLM) to compare the semantic similarity of sentence pairs. The PLM pre-embeds existing standard sentences in advance, performs real-time embedding of new sentences and compares them using cosine similarity. Sentence pairs exceeding a predefined cosine similarity threshold are sent to the LLM, significantly reducing computational costs while maintaining accuracy Dong et al. (2024). In addition, users can adjust the threshold to balance precision and speed, tailoring the analysis to specific requirements. The average cosine similarity of sentence pairs for each class and the implementation details of ITRM are provided in Appendix B.

Leveraging an LLM for 3-Class NLITo classify the semantic relationships in construction standard sentences as a 3-class NLI task, we apply instruction tuning (IT) Wei et al. (2022), a technique that fine-tunes LLM by incorporating explicit task instructions, to a construction domain-adapted LLM. For each sentence pair, we create a prompt (provided in Appendix C) containing task descriptions and definitions of overlap, conflict, and neutrality relationships. We curate three example sentence pairs for each relationship to enrich the LLM's understanding of the task, which are reviewed by PhD-level experts. To enhance inference efficiency, we add class-representing tokens ([overlap], [contradict], and [neutrality]) to the LLM tokenizer and train the model to gener

Figure 2: Overview of COSLLM and a real-time construction standards analysis framework. Our framework leverages an inference time reduction module to efficiently filter out irrelevant sentence pairs before LLM inference. It then performs effective classification of overlapping and conflicting sentences through a two-step classification process. Finally, the results are delivered to users via an interactive interface, which highlights overlap and conflict sentences, allows result viewing, and supports downloading for optimal usability.

 

ate the appropriate token. This approach mitigates errors caused by LLM's generation instability and enhances efficiency by minimizing the number of tokens generated during inference.

Selective CoT for Efficient InferenceTo classify overlapping and conflicting sentences with subtle semantic differences, we employ CoT. Because CoT is time-consuming and resource-intensive Wei et al. (2022), we adopt a selective approach during the IT process, inspired by previous work Hsieh et al. (2023). We add task-specific prefixes to the tokenizer, enabling the model to switch between simple inference and CoT based on the task requirements. The [predict] prefix allows for quick single-token prediction, while the [rationale] prefix activates CoT for more complex inferences. Because most sentence pairs in construction standards are neutral, COSLLM defaults to simple predictions and uses CoT only for pairs predicted as overlapping or conflicting (illustrated in the top-right section of Figure 2).

### Interactive Interface for Construction Standard Analysis

OverviewWe develop a real-time web-based interactive system powered by crosslinked to prevent overlaps and conflicts during the establishment or revision of construction standards. This system allows users to compare new construction standards with existing ones and resolve any overlaps and conflicts before release. Users can upload drafts as PDFs or texts, select relevant sections of existing standards, and initiate analysis. The system highlights overlapping or conflicting sentences in the draft, links them to corresponding standard codes, and allows users to download a detailed report (illustrated in the bottom-right section of Figure 2). The CoT results of COSLLM are provided to users, enhancing the convenience of managers during the semantic analysis process. The inference server is implemented using Nvidia Triton (NVIDIA Corporation), with additional modules for real-time construction standard updates. The detailed interfaces of the system are presented in Appendix D.

Real-time Data CollectionTo ensure accurate comparisons with the latest standards, we develop a real-time data collection system. This system utilizes dynamic crawling techniques to extract the content and structure of current construction standards from the KCC website, maintaining reliability even with database changes. Built with Selenium13, the system enables administrators to effortlessly update the standards database.

Footnote 13: [https://selenium-python.readthedocs.io/](https://selenium-python.readthedocs.io/)

## 4 Experiment

### Dataset

We collected 81 overlap instances and 45 conflict instances from Korean construction standards, identified by PhD-level experts. While this dataset provides a solid foundation, its limited size and diversity hinder the model's ability to generalize effectively Feng et al. (2021). In addition, the vast volume of construction standards makes manual data collection impractical. To address these challenges, we adopted a data augmentation approach proposed in prior research Yoo et al. (2021), using GPT-4 to generate additional instances for each class. In this process, a real sentence from construction standards was input into GPT-4, accompanied by a carefully crafted prompt and examples, to generate overlapping or conflicting sentences. The augmented data were then reviewed and validated by PhD-level experts, expanding the dataset to 304 instances. Since the majority of sentence relationships in practice are neutral, we included 1,265 neutral sentence pairs derived from actual construction standards. The final dataset comprises 1,569 instances: 144 overlap cases, 160 conflict cases, and 1,265 neutral sentence pairs.

### Evaluation Metrics

We evaluated the classification performance on the overlap, conflict, and neutrality dataset using accuracy and macro-F1 scores. Macro-F1 calculates the F1-score for each class individually and averages them, making it a robust metric for addressing class imbalance Yang (1999).

### Baselines

In this study, we evaluate the performance of COSLLM by comparing it with both PLMs and LLMs. PLMs have demonstrated strong performance in classification tasks Soyalp et al. (2021) and NLI tasks Liu et al. (2019). We compared COSLLM with PLMs specifically optimized for the Korean language, including BGE-M3-Korean Chen et al. (2024), KLUE-RoBERTa-large Park et al. (2021), and KoSimCSE-RoBERTa Gao et al. (2021). For LLMs, we evaluated Polyglot-Ko-5.8B Ko et al. (2023), a Korean-trained model, and Qwen2-7B-Instruct. PLM baselines are trained to 

predict the class using the [CLS] token, with two sentences separated by the [SEP] token. LLM baselines are trained using IT with CoT. More implementation details are described in Appendix E.

### Experimental Results

Table 1 presents the experimental results comparing the performance of baseline models and COSLLM. Our proposed method, COSLLM, consistently outperforms the baselines, achieving an accuracy of 98.1% and a macro-F1 score of 0.962. Although the augmented data were reviewed by experts, we also tested a setting where the augmented data were excluded from the test set to better simulate real-world conditions. Even under this condition, COSLIM demonstrated superior performance with an accuracy of 97.9% and a macro-F1 score of 0.907, outperforming all baselines. Figure 3 illustrates the class-wise F1-scores for each baseline model and COSLLM. While baseline models struggle to classify overlap and conflict sentences, COSLLM demonstrates strong performance across all classes (details are provided in Appendix F).

## 5 Analysis

Effectiveness of DA and ITTable 2 presents the results comparing models with and without DA and IT. The model without DA shows a slight performance decline, achieving an accuracy of 95.5% and a macro-F1 score of 0.882, which highlights the importance of DA. In contrast, the model without both DA and IT, tested using few-shot prompting with one example per class (otherwise same as IT prompt), exhibits a significant performance drop, with an accuracy of 80.9% and a macro-F1 score of 0.298, further emphasizing the critical role of IT.

Efficacy of Selective CoT and ITRMTable 3 demonstrates the efficacy of CoT. Applying CoT to every sentence pair, including neutral ones, results in the longest inference time. In contrast, Selective CoT matches the performance of full CoT while significantly optimizing inference time, making it the most efficient and effective option for real-time applications. The approach without CoT achieves the fastest inference but delivers the lowest performance. A detailed example of CoT-based sentence analysis is provided in Appendix G.

As shown in Table 3, applying ITRM to pre-filter neutral sentences resulted in a slight performance decrease but reduced inference time by approximately 35% compared to the original time. The performance-time trade-off can be adjusted by modifying the threshold, which we made configurable within the framework. In scenarios requiring both rapid analysis and slower but highly accurate analysis, ITRM effectively balances these demands.

 

itates the effective establishment and revision of construction standards.

### Limitations

Our methodology introduces a framework for automatically classifying overlapping and conflicting sections in construction standards, along with a novel system for addressing the challenges during the establishment and revision process. However, there are certain limitations. First, collecting a sufficiently large dataset of genuine overlapping or conflicting sentences proved challenging. As discussed throughout the paper, the vast volume of construction standards and the substantial time required for expert analysis posed significant obstacles. Second, our analysis focused exclusively on Korean construction standards, limiting the generalizability of our findings. Nonetheless, we believe the methodology is broadly applicable to other languages, as it is not heavily language-dependent. With adequate corpora and sentence-pair data from construction standards in other languages, our approach could be adapted for diverse linguistic contexts. Third, there is a potential risk that incorrect analysis by our framework could lead to the establishment or revision of flawed construction standards. However, our framework is not intended to replace human decision-making but to serve as an auxiliary tool that simplifies and supports experts' work. Since the final decisions are made by well-trained and experienced professionals, we believe this risk is unlikely to pose significant practical issues.

## Acknowledgements

We thank the anonymous reviewers for their helpful comments. This work was supported by the Basic Research Program through a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2022R1C1C1010317) and the 2025 Korea Construction Standards Center Operation - Digital Construction Standards Development Research.

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## Appendix A Paragraph-level Reasoning with Domain-adapted LLM

This section presents experimental results evaluating whether a domain-adapted LLM with fewer than 10 billion parameters effectively understands current construction standards. We conducted experiments using the domain-adapted Qwen2-7B-Instruct model by inputting prompts querying specific content from current construction standards and comparing the generated responses with the actual standard content.

Table 4 presents the results of querying the content of KDS 27 17 00 from the current construction standards, which the domain-adapted Qwen2-7B-Instruct model encountered during training. The model generated outputs entirely different from the actual content of the construction standards, suggesting that it does not retain the current standards accurately. The original text was in Korean and has been translated into English.

## Appendix B Implementation Details of ITRM

We implemented ITRM using KLUE-RoBERTaLarge, a PLM specialized in the Korean language. By measuring the cosine similarity of sentence pairs from the collected construction standards dataset, we observed that neutral pairs showed an average cosine similarity of 0.7554, while overlapping pairs averaged 0.9218 and conflicting pairs 0.8852. Based on these findings, we hypothesized that leveraging PLM embeddings could effectively pre-filter neutral sentence pairs.

For our experiments, we set the threshold at 0.797. As a result, 66.6% of neutral pairs were pre-filtered, along with 3.4% of overlapping pairs and 11.8% of conflicting pairs.

## Appendix C Prompt for Cosllm

Table 5 presents the prompts used for instruction tuning COSLLM. Same prompts used to train baseline LLMs.

## Appendix D System Interfaces

To facilitate convenient management of construction standards, we developed a web-based interactive system. Figure 4 illustrates the system's main interface (a), the screen for setting analysis parameters (b), the process of directly inserting target content (c), and uploading documents to initiate analysis (d). Figure 5 illustrates the screens for selecting the target construction standards for analysis (a), reporting the analysis progress (b), viewing the analysis results (c), and the analysis results presented in an Excel report format (d).

## Appendix E Implementation Details of COSLLM and Baselines

We implemented the models and baselines using PyTorch Paszke et al. (2017) and HuggingFace14. We conducted a series of experiments with various hyperparameters to enhance the accuracy and F1 scores of the models. For the PLMs, we conducted experiments with batch sizes [8; 16; 32], learning rates [1e-4 to 1e-6], and epochs [20; 30; 40]. For the LLMs, batch sizes [8; 16; 32], learning rates [5e-4 to 5e-5], and epochs [20; 30] were used. Prediction loss alpha and explanation loss alpha for selctive CoT ranged from 0.1 to 1.0. All of the LLMs are trained to utilize CoT while predicting. All experiments were conducted on three NVIDIA A6000 GPUs.

Footnote 14: [https://huggingface.co/](https://huggingface.co/)

## Appendix F Detailed Class-wise Performance

Table 6 presents detailed class-wise performance for classifying overlap, contradict, and neutrality.

## Appendix G CoT Reasoning Examples

Table 7 presents the CoT reasoning results for one pair each of overlapping and conflicting sentence pairs. The original is in Korean but translated into English.



Command: As an expert in the field of civil engineering, you should provide answers to questions related to construction standards, which are documents that present standards for the design, construction, and maintenance of structures.

Question: In Tunnel Seismic Design KDS 27 17 00, Section 4.2 provides seismic analysis methods for tunnel seismic design (1), (2), (3), (4), and (5). Could you tell me each item individually?

\begin{table}
\begin{tabular}{l|p{284.9pt}} \hline  & The above sentence asks for information regarding seismic analysis methods for tunnel seismic design. It requires finding the respective methods presented in the construction standard ’Tunnel Seismic Design’ KDS 27 17 00. This standard provides detailed procedures and content, so it is necessary to refer to the relevant parts to explain each method. For example, (1) could be the Simple Seismic Analysis, (2) could be Static Seismic Analysis, (3) could be Dynamic Seismic Analysis, (4) could be Seismic Vulnerability Analysis, and (5) could include Seismic Performance Estimation. It is important to find and respond with these details to answer the question. If additional information such as exact definitions or application methods for each method is needed, the detailed content of the construction standard should also be provided. Additionally, when answering, consider the specific context in which the question is asked. For instance, by providing information on how each seismic analysis method is applied, depending on tunnel length, ground conditions, and seismic performance goals. This approach will help provide the questioner with more useful information. \\ \hline Correct Response & 4.2 Seismic Analysis Methods for Tunnel Seismic Design (1) The seismic analysis methods for tunnels may include the Response Displacement Method, Dynamic Analysis Method, and Pseudo-Static Analysis Method. (2) For tunnels, when the interaction between the ground and the structure is significant and displacement must be carefully considered, the Response Displacement Method can be applied. (3) In the dynamic analysis method for Seismic Grade I, the input seismic waves can apply artificially synthesized ground motion time-history curves inferred by considering the seismic damage history of the area where the tunnel is built, ground characteristics, and the natural period of the structure. The generation of these artificially synthesized ground motion time-history curves should follow KDS 17 10 00 (4.2.1.4(10)). (4) The Pseudo-Static Analysis Method is locally applied to the portal structures of retaining wall-type tunnel entrances, simplifying the dynamic time-history characteristics into static loads to make the design simpler and more conservative. (5) During seismic design, analysis methods that consider the non-linear behavior characteristics of the ground can be used. \\ \hline \end{tabular}
\end{table}
Table 4: Generated results from the domain-adapted model for queries related to construction standards 

Instruction: As a civil engineering expert, your task is to analyze sentences extracted from construction standards, which are documents that provide guidelines for the design, construction, and maintenance of structures. Your job is to determine whether the sentences are semantically overlapping, conflicting, or unrelated.

Semantic overlaps and conflicts between sentences in construction standards require analysis and judgment based on meaning, rather than just identifying similar words or tones. Overlapping sentences describe the same content under the same section, while conflicting sentences describe different content under the same section. In particular, conflicts may include cases where the same content is described with different values (e.g., numerical discrepancies) or referenced with different construction standard codes. Sentences that are neither overlapping nor conflicting are considered unrelated, meaning they address entirely different topics.

The data provided to you are formatted as follows. Sentences from construction standards appear after <sentence1> and <lstentence2|>. The label after <lpred|> indicates whether the relationship is semantic overlap, conflict, or none: <loverlap|>, <lcontradict>, or <lonnel>. The explanation for the judgment follows <lexpl>. Based on this structure, carefully review the two sentences and provide the correct semantic judgment (overlap, conflict, or none) along with an explanation. An example is as follows:

[Overlap Examples]

[Conflict Examples]

[Neutrality Examples]

Now, based on the given construction standard sentences, provide the appropriate semantic classification (overlap, conflict, or none) and explain your reasoning.

[Data]

### Response:

Figure 4: Interfaces of the Interactive Interface for Construction Standard Analysis. (a) Main Interface: Users can select a document or input newly established or revised sentences in text form to initiate analysis. (b) Analysis Parameter Settings: Users can selectively analyze overlaps and conflicts or configure the cosine similarity threshold for ITRM. (c) Text Input Screen: Users can input target sentences for analysis in text form. (d) File Upload Screen: Users can upload .hwp or .pdf files to start the analysis.

 



\begin{table}
\begin{tabular}{l l c c c} \hline Method & Model & Class & Accuracy & F1-Score \\ \hline  & BGE-M3 & contradict & 0.625 & 0.645 \\  & & neutrality & 0.976 & 0.954 \\  & & overlap & 0.500 & 0.609 \\ \cline{2-5}  & RoBERTa-large & contradict & 0.625 & 0.741 \\ Encoder & & neutrality & 0.984 & 0.980 \\  & & overlap & 0.857 & 0.750 \\ \cline{2-5}  & KoSimCSE-RoBERTa & contradict & 0.813 & 0.839 \\  & & neutrality & 1.000 & 0.985 \\  & & overlap & 0.714 & 0.800 \\ \hline  & polyglot-ko-5.8b & contradict & 0.625 & 0.741 \\  & & neutrality & 1.000 & 0.973 \\  & & overlap & 0.786 & 0.846 \\ \cline{2-5}  & Qwen2-7B-Instruct & contradict & 0.625 & 0.769 \\ Decoder & & neutrality & 1.000 & 0.981 \\  & & overlap & 0.929 & 0.897 \\ \cline{2-5}  & COSLLM (Ours) & contradict & 0.875 & 0.933 \\  & & neutrality & 1.000 & 0.988 \\  & & overlap & 0.929 & 0.963 \\ \hline \end{tabular}
\end{table}
Table 6: Class-wise performance

Figure 5: Interfaces of the Interactive Interface for Construction Standard Analysis. (a) Target Construction Standards Selection Screen: Allows experts to select only the relevant standards from the current construction standards for analysis. (b) Analysis Progress Screen: Displays the progress of LLM inference on the analysis server in real-time. (c) Analysis Results Screen: Highlights results directly on the uploaded document, allowing users to immediately view overlapping or conflicting construction standards. A comprehensive list and summary are available on the right panel for an at-a- glance overview. (d) Analysis Results in Excel Report Format: Provides a downloadable construction standards analysis report in MS Excel format.





\begin{table}
\begin{tabular}{c|p{284.5pt}|l} \hline Predict & \multicolumn{1}{c}{Result} \\ \hline Overlap & sentence1 & When determining the scale of the drainage exclusion facility, the safety of past flood records must also be reviewed. \\ \cline{2-3}  & sentence2 & In studies for flood prevention, the design of drainage exclusion facilities must ensure sufficient safety by referring to past flood data. \\ \cline{2-3}  & CoT & In sentence 1, it is mentioned that the **safety of past flood records must also be reviewed** during the process of determining the scale of the drainage exclusion facility. In sentence 2, **the importance of ensuring sufficient safety by referring to past flood data** in the design of drainage exclusion facilities for flood prevention is emphasized. Accordingly, both sentences approach the design of drainage exclusion facilities through safety reviews, containing similar key content, and can be considered to have an overlap relationship. \\ \hline Conflict & sentence1 & If a retest is required based on the results of the inspection test, the manufacturer shall bear the costs for the retest, inspection, and any time-related expenses for the retest and inspection. \\ \cline{2-3}  & sentence2 & In the case where defects are found in the inspection test and a retest is necessary, all retest costs shall be borne by the client, and the manufacturer’s responsibility is limited to repairing or replacing the product. \\ \cline{2-3}  & CoT & The statement that **the manufacturer must bear all costs when a retest is required through inspection testing** exceeds the manufacturer’s role. While the client can request a retest if defects are found in the inspection test, **the responsibility for covering all costs is limited to the manufacturer.** Therefore, the two sentences provide conflicting instructions regarding the responsibility for retest costs and the authority of the client. \\ \hline \end{tabular}
\end{table}
Table 7: CoT reasoning examples