Time-Series Question Answering (TSQA) aims to interpret time-series information and generate answers according to the intent of natural language questions. However, in TSQA, the required temporal granularity and reasoning scope vary across queries, making it crucial to effectively organize the time-series information needed for each query. In this study, we propose TiMER (**Ti**me-series and language alignment with **M**ulti-granularity **E**xpert **R**outing), a time-series-language alignment framework that selectively constructs multi-granularity time-series information according to query intent and connects it to the LLM input space. Experimental results on a large-scale TSQA benchmark show that TiMER achieves an overall accuracy of 81.02% using only a small number of external alignment modules while keeping the LLM frozen, outperforming various LLM-based baselines. In addition, extensive ablation studies verify the effectiveness of each component. This study presents an effective direction for natural language-based time-series understanding and temporal reasoning by dynamically organizing and utilizing time-series information according to query intent. The code is publicly available at https://github.com/hyuna0926/TiMER_paper.

ESG analysis requires complex language understanding across diverse sources, including corporate reports, news, and disclosures, for tasks such as ESG relevance detection, action interpretation, impact analysis, and question answering. However, existing ESG datasets are fragmented by task and source, with heterogeneous formats and annotation schemes, limiting their use for training general-purpose ESG LLMs and unified evaluation. In this work, we construct ESG-Instruct, a 30,000-instance ESG instruction dataset built from public ESG resources, and ESGU-Bench, an ESG understanding benchmark covering six representative ESG tasks. Based on these resources, we further develop ESGPT, an 8B ESG-specialized LLM, and evaluate it against diverse open-source and API-based LLMs. Experimental results show that general-purpose LLMs exhibit substantial performance variation across ESG tasks, while ESGPT, despite its relatively small scale, achieves the best performance on multiple ESG tasks. This work provides unified data and benchmark resources for training and evaluating ESG-specialized LLMs.

Humans attempt to understand the real world by asking the fundamental question ”Why?” when faced with incomprehensible situations in everyday life. Such why-questions provide essential knowledge that can help in understanding these situations. In this study, we conducted an end-to-end process to verify the utility of consecutive why-questions, from constructing a large language model (LLM)-based dataset to performing quantitative evaluation and analysis. Firstly, we created a WHY-Chain dataset, consisting of answers generated by an LLM in response to chain-of-why-questions, including a validity check. We also incorporated objectives that effectively capture the ”consecutive” characteristic of the data. Using the WHY-Chain dataset and two types of self-supervised objectives, we trained the pre-trained model. As a result, the refined model demonstrated improved performance on downstream tasks that require commonsense reasoning. Additionally, we conducted various ablation studies to assess the impact of different factors, confirming the scalability of the proposed approach. Lastly, we confirmed the consistency of the logical information by reasoning chain analysis of the answers generated from consecutive why-questions.

With the expansion of pre-trained language model usage in recent years, the importance of datasets for performing tasks in specialized domains has significantly increased. Therefore, we have built a Korean dataset called ESG-Kor to automatically extract Environmental, Social, and Governance (ESG) information, which has recently gained importance. ESG-Kor is a dataset consisting of a total of 118,946 sentences that extracted information on each ESG component from Korean companies’ sustainability reports and manually labeled it according to objective rules provided by ESG evaluation agencies. To verify the effectiveness and applicability of the ESG-Kor dataset, classification performance was confirmed using several Korean pre-trained language models, and significant performance was obtained. Additionally, by extending the ESG classification model to documents of small and medium enterprises and extracting information based on ESG key issues and in-depth analysis, we demonstrated potential and practical use cases in the ESG field.