ymoslem/acl-abstracts huggingface dataset:
Capturing Human Cognitive Styles with Language: Towards an Experimental Evaluation Paradigm (2025)

While NLP models often seek to capture cognitive states via language, the validity of predicted states is determined by comparing them to annotations created without access the cognitive states of the authors. In behavioral sciences, cognitive states are instead measured via experiments. Here, we introduce an experiment-based framework for evaluating language-based cognitive style models against human behavior. We explore the phenomenon of decision making, and its relationship to the linguistic style of an individual talking about a recent decision they made. The participants then follow a classical decision-making experiment that captures their cognitive style, determined by how preferences change during a decision exercise. We find that language features, intended to capture cognitive style, can predict participants' decision style with moderate-to-high accuracy (AUC 0.8), demonstrating that cognitive style can be partly captured and revealed by discourse patterns.
Understanding LLM Development Through Longitudinal Study: Insights from the Open Ko-LLM Leaderboard (2025)

This paper conducts a longitudinal study over eleven months to address the limitations of prior research on the Open Ko-LLM Leaderboard, which have relied on empirical studies with restricted observation periods of only five months. By extending the analysis duration, we aim to provide a more comprehensive understanding of the progression in developing Korean large language models (LLMs). Our study is guided by three primary research questions: (1) What are the specific challenges in improving LLM performance across diverse tasks on the Open Ko-LLM Leaderboard over time? (2) How does model size impact task performance correlations across various benchmarks? (3) How have the patterns in leaderboard rankings shifted over time on the Open Ko-LLM Leaderboard?. By analyzing 1,769 models over this period, our research offers a comprehensive examination of the ongoing advancements in LLMs and the evolving nature of evaluation frameworks.
RTSM: Knowledge Distillation with Diverse Signals for Efficient Real-Time Semantic Matching in E-Commerce (2025)

Semantic matching plays a pivotal role in e-commerce by facilitating better product discovery and driving sales within online stores. Transformer models have proven exceptionally effective in mapping queries to an embedding space, positioning semantically related entities (queries or products) in close proximity. Despite their effectiveness, the high computational demands of large transformer models pose challenges for their deployment in real-time scenarios. This paper presents RTSM, an advanced knowledge distillation framework designed for Real-Time Semantic Matching. Our approach develops accurate, low-latency student models by leveraging both soft labels from a teacher model and ground truth generated from pairwise query-product and query-query signals. These signals are sourced from direct audits, synthetic examples created by LLMs, user interaction data, and taxonomy-based datasets, with custom loss functions enhancing learning efficiency. Experimental evaluations on internal and external e-commerce datasets demonstrate a 2-2.5{\%} increase in ROC-AUC compared to directly trained student models, outperforming both the teacher model and state-of-the-art knowledge distillation benchmarks.
WorkTeam: Constructing Workflows from Natural Language with Multi-Agents (2025)

Workflows play a crucial role in enhancing enterprise efficiency by orchestrating complex processes with multiple tools or components. However, hand-crafted workflow construction requires expert knowledge, presenting significant technical barriers. Recent advancements in Large Language Models (LLMs) have improved the generation of workflows from natural language instructions (aka NL2Workflow), yet existing single LLM agent-based methods face performance degradation on complex tasks due to the need for specialized knowledge and the strain of task-switching. To tackle these challenges, we propose WorkTeam, a multi-agent NL2Workflow framework comprising a supervisor, orchestrator, and filler agent, each with distinct roles that collaboratively enhance the conversion process. As there are currently no publicly available NL2Workflow benchmarks, we also introduce the HW-NL2Workflow dataset, which includes 3,695 real-world business samples for training and evaluation. Experimental results show that our approach significantly increases the success rate of workflow construction, providing a novel and effective solution for enterprise NL2Workflow services.
How LLMs React to Industrial Spatio-Temporal Data? Assessing Hallucination with a Novel Traffic Incident Benchmark Dataset (2025)

Large language models (LLMs) hold revolutionary potential to digitize and enhance the Health {\&} Public Services (H{\&}PS) industry. Despite their advanced linguistic abilities, concerns about accuracy, stability, and traceability still persist, especially in high-stakes areas such as transportation systems. Moreover, the predominance of English in LLM development raises questions about how they perform in non-English contexts. This study originated from a real world industrial GenAI application, introduces a novel cross-lingual benchmark dataset comprising nearly 99,869 real traffic incident records from Vienna (2013-2023) to assess the robustness of state-of-the-art LLMs ($\geq$ 9) in the spatio vs temporal domain for traffic incident classification. We then explored three hypotheses {---} sentence indexing, date-to-text conversion, and German-to-English translation {---} and incorporated Retrieval Augmented Generation (RAG) to further examine the LLM hallucinations in both spatial and temporal domain. Our experiments reveal significant performance disparities in the spatio-temporal domain and demonstrate what types of hallucinations that RAG can mitigate and how it achieves this. We also provide open access to our H{\&}PS traffic incident dataset, with the project demo and code available at Website \url{https://sites.google.com/view/llmhallucination/home}
Text2Sql: Pure Fine-Tuning and Pure Knowledge Distillation (2025)

Text2Sql is a task that converts natural language questions into SQL queries. In previous research on LLM fine-tuning, researchers typically input both the entire database schema and the natural language question into the model. This approach has two issues: 1) the model`s context is limited when dealing with a large number of database tables; 2) the question is often related to only a few tables, leading to excessive irrelevant information that distracts the model. To address these issues, we employed pure fine-tuning strategy to reduce redundancy. The model fine-tuned with pure prompts, using prompts that are only 53{\%} of the baseline length, outperforms the baseline (fine-tuned with all tables in the prompt) by 8.2{\%} and 8.6{\%} in Test-suite accuracy (TS) and exact-set-match accuracy (EM), respectively, on the Spider dev set. Under the most refined Spider dev set of prompts, the model achieves TS and EM scores of 73.5{\%} and 75.4{\%}, respectively, approaching state-of-the-art (SOTA) levels. To leverage the capabilities of the model with pure prompts, we applied pure knowledge distillation strategy to transfer its abilities. The distilled student model achieved a 1.9{\%} improvement in TS, while the teacher model`s prompt length was only 23{\%} of that of the student model.
MoEMoE: Question Guided Dense and Scalable Sparse Mixture-of-Expert for Multi-source Multi-modal Answering (2025)

Question Answering (QA) and Visual Question Answering (VQA) are well-studied problems in the language and vision domain. One challenging scenario involves multiple sources of information, each of a different modality, where the answer to the question may exist in one or more sources. This scenario contains richer information but is highly complex to handle. In this work, we formulate a novel question-answer generation (QAG) framework in an environment containing multi-source, multimodal information. The answer may belong to any or all sources; therefore, selecting the most prominent answer source or an optimal combination of all sources for a given question is challenging. To address this issue, we propose a question-guided attention mechanism that learns attention across multiple sources and decodes this information for robust and unbiased answer generation. To learn attention within each source, we introduce an explicit alignment between questions and various information sources, which facilitates identifying the most pertinent parts of the source information relative to the question. Scalability in handling diverse questions poses a challenge. We address this by extending our model to a sparse mixture-of-experts (sparse-MoE) framework, enabling it to handle thousands of question types. Experiments on T5 and Flan-T5 using three datasets demonstrate the model`s efficacy, supported by ablation studies.
Finding-Centric Structuring of Japanese Radiology Reports and Analysis of Performance Gaps for Multiple Facilities (2025)

This study addresses two key challenges in structuring radiology reports: the lack of a practical structuring schema and datasets to evaluate model generalizability. To address these challenges, we propose a {\textquotedblleft}Finding-Centric Structuring,{\textquotedblright} which organizes reports around individual findings, facilitating secondary use. We also construct JRadFCS, a large-scale dataset with annotated named entities (NEs) and relations, comprising 8,428 Japanese Computed Tomography (CT) reports from seven facilities, providing a comprehensive resource for evaluating model generalizability. Our experiments reveal performance gaps when applying models trained on single-facility reports to those from other facilities. We further analyze factors contributing to these gaps and demonstrate that augmenting the training set based on these performance-correlated factors can efficiently enhance model generalizability.
Learning LLM Preference over Intra-Dialogue Pairs: A Framework for Utterance-level Understandings (2025)

Large language models (LLMs) have demonstrated remarkable capabilities in handling complex dialogue tasks without requiring use case-specific fine-tuning. However, analyzing live dialogues in real-time necessitates low-latency processing systems, making it impractical to deploy models with billions of parameters due to latency constraints. As a result, practitioners often prefer smaller models with millions of parameters, trained on high-quality, human-annotated datasets. Yet, curating such datasets is both time-consuming and costly. Consequently, there is a growing need to combine the scalability of LLM-generated labels with the precision of human annotations, enabling fine-tuned smaller models to achieve both higher speed and accuracy comparable to larger models. In this paper, we introduce a simple yet effective framework to address this challenge. Our approach is specifically designed for per-utterance classification problems, which encompass tasks such as intent detection, dialogue state tracking, and more. To mitigate the impact of labeling errors from LLMs {--} the primary source of inaccuracies in student models {--} we propose a noise-reduced preference learning loss. Experimental results demonstrate that our method significantly improves accuracy across utterance-level dialogue tasks, including sentiment detection (over 2{\%}), dialogue act classification (over 1.5{\%}), etc.
Enhancing Function-Calling Capabilities in LLMs: Strategies for Prompt Formats, Data Integration, and Multilingual Translation (2025)

Large language models (LLMs) have significantly advanced autonomous agents, particularly in zero-shot tool usage, also known as function calling. This research delves into enhancing the function-calling capabilities of LLMs by exploring different approaches, including prompt formats for integrating function descriptions, blending function-calling and instruction-following data, introducing a novel Decision Token for conditional prompts, leveraging chain-of-thought reasoning, and overcoming multilingual challenges with a translation pipeline. Our key findings and contributions are as follows: (1) Instruction-following data improves both function-calling accuracy and relevance detection. (2) The use of the newly proposed Decision Token, combined with synthetic non-function-call data, enhances relevance detection. (3) A tailored translation pipeline effectively overcomes multilingual limitations, demonstrating significant improvements in Traditional Chinese. These insights highlight the potential for improved function-calling capabilities and multilingual applications in LLMs.
Exploring Straightforward Methods for Automatic Conversational Red-Teaming (2025)

Large language models (LLMs) are increasingly used in business dialogue systems but they also pose security and ethical risks. Multi-turn conversations, in which context influences the model`s behavior, can be exploited to generate undesired responses. In this paper, we investigate the use of off-the-shelf LLMs in conversational red-teaming settings, where an attacker LLM attempts to elicit undesired outputs from a target LLM. Our experiments address critical questions and offer valuable insights regarding the effectiveness of using LLMs as automated red-teamers, shedding light on key strategies and usage approaches that significantly impact their performance.Our findings demonstrate that off-the-shelf models can serve as effective red-teamers, capable of adapting their attack strategies based on prior attempts. Allowing these models to freely steer conversations and conceal their malicious intent further increases attack success. However, their effectiveness decreases as the alignment of the target model improves.
A Diverse and Effective Retrieval-Based Debt Collection System with Expert Knowledge (2025)

Designing effective debt collection systems is crucial for improving operational efficiency and reducing costs in the financial industry. However, the challenges of maintaining script diversity, contextual relevance, and coherence make this task particularly difficult. This paper presents a debt collection system based on real debtor-collector data from a major commercial bank. We construct a script library from real-world debt collection conversations, and propose a two-stage retrieval based response system for contextual relevance. Experimental results show that our system improves script diversity, enhances response relevance, and achieves practical deployment efficiency through knowledge distillation. This work offers a scalable and automated solution, providing valuable insights for advancing debt collection practices in real-world applications.
Search Query Embeddings via User-behavior-driven Contrastive Learning (2025)

Universal query embeddings that accurately capture the semantic meaning of search queries are crucial for supporting a range of query understanding (QU) tasks within enterprises.However, current embedding approaches often struggle to effectively represent queries due to the shortness of search queries and their tendency for surface-level variations.We propose a user-behavior-driven contrastive learning approach which directly aligns embeddings according to user intent.This approach uses intent-aligned query pairs as positive examples, derived from two types of real-world user interactions: (1) clickthrough data, in which queries leading to clicks on the same URLs are assumed to share the same intent, and (2) session data, in which queries within the same user session are considered to share intent.By incorporating these query pairs into a robust contrastive learning framework, we can construct query embedding models that align with user intent while minimizing reliance on surface-level lexical similarities.Evaluations on real-world QU tasks demonstrated that these models substantially outperformed state-of-the-art text embedding models such as mE5 and SimCSE.Our models have been deployed in our search engine to support QU technologies.
QSpell 250K: A Large-Scale, Practical Dataset for Chinese Search Query Spell Correction (2025)

Chinese Search Query Spell Correction is a task designed to autonomously identify and correct typographical errors within queries in the search engine. Despite the availability of comprehensive datasets like Microsoft Speller and Webis, their monolingual nature and limited scope pose significant challenges in evaluating modern pre-trained language models such as BERT and GPT. To address this, we introduce \textbf{QSpell 250K}, a large-scale benchmark specifically developed for Chinese Query Spelling Correction. QSpell 250K offers several advantages: 1) It contains over 250K samples, which is ten times more than previous datasets. 2) It covers a broad range of topics, from formal entities to everyday colloquialisms and idiomatic expressions. 3) It includes both Chinese and English, addressing the complexities of code-switching. Each query undergoes three rounds of high-fidelity annotation to ensure accuracy. Our extensive testing across three popular models demonstrates that QSpell 250K effectively evaluates the efficacy of representative spelling correctors. We believe that QSpell 250K will significantly advance spelling correction methodologies. The accompanying data and code will be made publicly available.
CONSTRUCTA: Automating Commercial Construction Schedules in Fabrication Facilities with Large Language Models (2025)

Automating planning with LLMs presents transformative opportunities for traditional industries, yet remains underexplored. In commercial construction, the complexity of automated scheduling often requires manual intervention to ensure precision. We propose CONSTRUCTA, a novel framework leveraging LLMs to optimize construction schedules in complex projects like semiconductor fabrication. CONSTRUCTA addresses key challenges by: (1) integrating construction-specific knowledge through static RAG; (2) employing context-sampling techniques inspired by architectural expertise to provide relevant input; and (3) deploying Construction DPO to align schedules with expert preferences using RLHF. Experiments on proprietary data demonstrate performance improvements of +42.3{\%} in missing value prediction, +79.1{\%} in dependency analysis, and +28.9{\%} in automated planning compared to baseline methods, showcasing its potential to revolutionize construction workflows and inspire domain-specific LLM advancements.
Challenges and Remedies of Domain-Specific Classifiers as LLM Guardrails: Self-Harm as a Case Study (2025)

Context:Despite the impressive capabilities of Large Language Models (LLMs), they pose significant risks in many domains and therefore require guardrails throughout the lifecycle.Problem:Many such guardrails are trained as classifiers with domain-specific human text datasets obtained from sources such as social media and they achieve reasonable performance against closed-domain benchmarks. When deployed in the real world, however, the guardrails have to deal with machine text in an open domain, and their performance deteriorates drastically, rendering them almost unusable due to a high level of false refusal.Solution:In this paper, using a self-harm detector as an example, we demonstrate the specific challenges facing guardrail deployment due to the data drift between training and production environments. More specifically, we formed two hypotheses about the potential causes, i.e. closed vs. open domain, human vs. LLM-generated text, and conducted five experiments to explore various potential remedies, including their respective advantages and disadvantages.Evaluation:While focusing on one example, our experience and knowledge of LLM guardrails give us great confidence that our work contributes to a more thorough understanding of guardrail deployment and can be generalized as a methodology to build more robust domain-specific guardrails in real-world applications.
Mitigating Bias in Item Retrieval for Enhancing Exam Assembly in Vocational Education Services (2025)

In education, high-quality exams must cover broad specifications across diverse difficulty levels during the assembly and calibration of test items to effectively measure examinees' competence. However, balancing the trade-off of selecting relevant test items while fulfilling exam specifications without bias is challenging, particularly when manual item selection and exam assembly rely on a pre-validated item base. To address this limitation, we propose a new mixed-integer programming re-ranking approach to improve relevance, while mitigating bias on an industry-grade exam assembly platform. We evaluate our approach by comparing it against nine bias mitigation re-ranking methods in 225 experiments on a real-world benchmark data set from vocational education services. Experimental results demonstrate a 17{\%} relevance improvement with a 9{\%} bias reduction when integrating sequential optimization techniques with improved contextual relevance augmentation and scoring using a large language model. Our approach bridges information retrieval and exam assembly, enhancing the human-in-the-loop exam assembly process while promoting unbiased exam design
Breaking Boundaries: Investigating the Effects of Model Editing on Cross-linguistic Performance (2025)

Pretrained language models (PLMs) have revolutionized NLP but amplify linguistic inequities in multilingual applications. While prior studies focused on transformer architectures such as BERT, we evaluate large language models (LLMs) including Mistral, TowerInstruct, OpenHathi, Tamil-Llama, and Kan-Llama. Through rigorous testing across eight languages spanning high-resource (English, German, French, Italian, Spanish) and low-resource (Hindi, Tamil, Kannada) settings, we reveal systemic failures in preserving multilingual reliability and adaptability. Using paradigms like each language for itself' (ELFI) and each language for others' (ELFO), we highlight the inability of current LLMs to bridge linguistic divides. Even model merging fail to mitigate these gaps, exposing fundamental limitations. These findings emphasize the critical need for reimagining AI architectures to deliver true linguistic inclusivity and equitable performance across diverse languages.
Towards Reliable and Practical Phishing Detection (2025)

As the prevalence of phishing attacks continues to rise, there is an increasing demand for more robust detection technologies. With recent advances in AI, we discuss how to construct a reliable and practical phishing detection system using language models. For this system, we introduce the first large-scale Korean dataset for phishing detection, encompassing six types of phishing attacks. We consider multiple factors for building a real-time detection system for edge devices, such as model size, Speech-To-Text quality, split length, training technique and multi-task learning. We evaluate the model`s ability twofold: in-domain, and unseen attack detection performance which is referred to as zero-day performance. Additionally, we demonstrate the importance of accurate comparison groups and evaluation datasets, showing that voice phishing detection performs reasonably well while smishing detection remains challenging. Both the dataset and the trained model will be available upon request.