Sentence-level explanations can miss the bigger picture of how a black-box model behaves across data, which matters most for complex criteria like safety that cannot be defined by a single rule. We trace Logit-Trajectory, which tracks adjacent-layer logit updates as vectors and aggregates them into a reproducible dataset-level trajectory pattern, enabling depth-wise explainability through signals such as coherence and angular rotation. Across 6 languages and 5 NLP tasks, we show these trajectory summaries reveal consistent depth-wise patterns that divergence- and similarity-based baselines often wash out due to scalarization. As a case study where dataset-level intermediate decision structure matters, we evaluate safety classification, reporting both trajectory-level visual separability and classification performance.

BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disease. Traditional models for estimating AD onset cannot capture nonlinear interactions (epistasis) among the numerous genetic variables that contribute to AD risk. METHODS: We developed a feedforward neural network (FFN)-Weibull survival model to predict AD onset using large-scale single-nucleotide polymorphism (SNP) data. We integrated an XAI technique, Shapley additive explanations (SHAP), to address the black-box nature of deep learning, interpret model predictions, and quantify the contribution of each genetic factor to AD. RESULTS: The FFN model achieved a mean concordance index of 0.647, demonstrating an approximately 3.6% improvement over the traditional linear baseline (0.625). The FFN-SHAP model validated established findings, identifying APOE E4 as a primary AD risk factor. APOE E2 strongly protected against AD. Metabolic-disorder-related SNPs had conflicting effects, suggesting gene-environment interactions influence AD onset. CONCLUSIONS: By effectively bypassing the combinatorial explosion of interaction terms, the predictive power of an FFN combined with XAI provides a robust methodological tool for identifying the genetic basis of complex diseases, even in cohorts with limited sample sizes. Our model generated novel testable hypotheses regarding the intricate roles of gene-gene and gene-environment interactions in AD pathogenesis.

In this research, we introduce the Courtroom-LLM framework, a novel multi-LLM structure inspired by legal courtroom processes, aiming to enhance decision-making in ambiguous text classification scenarios. Our approach simulates a courtroom setting within LLMs, assigning roles similar to those of prosecutors, defense attorneys, and judges, to facilitate comprehensive analysis of complex textual cases. We demonstrate that this structured multi-LLM setup can significantly improve decision-making accuracy, particularly in ambiguous situations, by harnessing the synergistic effects of diverse LLM arguments. Our evaluations across various text classification tasks show that the Courtroom-LLM framework outperforms both traditional single-LLM classifiers and simpler multi-LLM setups. These results highlight the advantages of our legal-inspired model in improving decision-making for text classification.

Many statistical facts are conveyed through charts.While various methods have emerged for chart understanding, chart generation typically requires users to manually input code, intent, and other parameters to obtain the desired format on chart generation tools.Recently, the advent of image-generating Large Language Models has facilitated chart generation; however, even this process often requires users to provide numerous constraints for accurate results.In this paper, we propose a loop-based framework for automatically evolving charts in a multi-agent environment.Within this framework, three distinct agents-Chart Code Generator, Chart Replier, and Chart Quality Evaluator-collaborate for iterative, user-tailored chart generation using large language models.Our approach demonstrates an improvement of up to 29.97% in performance compared to first generation, while also reducing generation time by up to 86.9% compared to manual promptbased methods, showcasing the effectiveness of this multi-agent collaboration in enhancing the quality and efficiency of chart generation.

Summarization is one of the important tasks of natural language processing used to distill information. Recently, the sequence-to-sequence method was applied, in a general manner, to summarization tasks. The problem is that a large amount of information must be pre-trained for a specific domain, and information other than input statements cannot be utilized. To compensate for this shortcoming, controllable summarization has recently been in the spotlight. We introduced three properties into controllable summarization: 1) a new human-machine communication input format, 2) a robust constraint-sensitive summarization method for these formats, and 3) a practical interactive summarization interface available to the user. Experiments on the Wizard-of-Wikipedia dataset show that applying this input format and the constraint-sensitive method enhances summarization performance compared to the typical method. A user study shows that the interactive summarization interface is practical and that participants are evaluating it positively.

Dependency parsing is a fundamental problem in natural language processing. We introduce a novel dependency-parsing framework called head-pointing--based dependency parsing . In this framework, we cast the Korean dependency parsing problem as a statistical head-pointing and arc-labeling problem. To address this problem, a novel neural network called the multitask pointer network is devised for a neural sequential head-pointing and type-labeling architecture. Our approach does not require any handcrafted features or language-specific rules to parse dependency. Furthermore, it achieves state-of-the-art performance for Korean dependency parsing.

While learning embedding models has yielded fruitful results in several NLP subfields, most notably Word2Vec, embedding correspondence has relatively not been well explored especially in the context of natural language understanding (NLU), a task that typically extracts structured semantic knowledge from a text.A NLU embedding model can facilitate analyzing and understanding relationships between unstructured texts and their corresponding structured semantic knowledge, essential for both researchers and practitioners of NLU.Toward this end, we propose a framework that learns to embed semantic correspondence between text and its extracted semantic knowledge, called semantic frame.One key contributed technique is semantic frame reconstruction used to derive a one-to-one mapping between embedded vectors and their corresponding semantic frames.Embedding into semantically meaningful vectors and computing their distances in vector space provide a simple, but effective way to measure semantic similarities.With the proposed framework, we demonstrate three key areas where the embedding model can be effective: visualization, semantic search and re-ranking.

In this article, we introduce a novel neural architecture for the end-to-end Korean Part-of-Speech (POS) tagging problem. To address the problem, we extend the present recurrent neural network-based sequence-to-sequence models to deal with the key challenges in this task: rare word generation and POS tagging. To overcome these issues, Input-Feeding and Copying mechanism are adopted. Although our approach does not require any manual features or preprocessed pattern matching dictionaries, our best single model achieves an F-score of 97.08. This is competitive with the current state-of-the-art model (F-score 98.03), which requires extensive manual feature processing.

This paper proposes a method to model confirmations for example-based dialog management. To enable the system to provide a confirmation to the user in an appropriate time, we employed a multiple dialog state representation approach for keeping track of user input uncertainty and implemented a confirmation agent which decides when the information gathered from the user contains an error. We developed a car navigation dialog system to evaluate our proposed method. Evaluations with simulated dialogs show our approach is useful for handling misunderstanding errors in example-based dialog management.

Query relaxation refers to the process of reducing the number of constraints on a query if it returns no result when searching a database. This is an important process to enable extraction of an appropriate number of query results because queries that are too strictly constrained may return no result, whereas queries that are too loosely constrained may return too many results. This paper proposes an automated method of correlation-based query relaxation (CBQR) to select an appropriate constraint subset. The example-based dialog modeling framework was used to validate our algorithm. Preliminary results show that the proposed method facilitates the automation of query relaxation. We believe that the CBQR algorithm effectively relaxes constraints on failed queries to return more dialog examples.

Error handling has become an important issue in spoken dialog systems. We describe an example-based approach to detect and repair errors in an example-based dialog modeling framework. Our approach to error recovery is focused on the re-phrase strategy with a system and a task guidance to help the novice users to re-phrase well-recognizable and well-understandable input. The dialog system gives possible utterance templates and contents related to the current situation when errors are detected. An empirical evaluation of the car navigation system shows that our approach is effective to the novice users for operating the spoken dialog system.

In this paper, we present POSTECH Spoken Dialogue System for Electronic Program Guide Information Access (POSSDS-EPG). POSSDS-EPG consists of automatic speech recognizer, spoken language understanding, dialogue manager, system utterance generator, text-to-speech synthesizer, and EPG database manager. Each module is designed and implemented to make an effective and practical spoken dialogue system. In particular, in order to reflect the up-to-date EPG information which is updated frequently and periodically, we applied a web-mining technology to the EPG database manager, which builds the content database based on automatically extracted information from popular EPG websites. The automatically generated content database is used by other modules in the system for building their own resources. Evaluations show that our system performs EPG access task in high performance and can be managed with low cost.

In this paper, we present POSTECH Situation-Based Dialogue Manager (POSSDM) for a spoken dialogue system using both example- and rule-based dialogue management techniques for effective generation of appropriate system responses. A spoken dialogue system should generate cooperative responses to smoothly control dialogue flow with the users. We introduce a new dialogue management technique incorporating dialogue examples and situation-based rules for the electronic program guide (EPG) domain. For the system response generation, we automatically construct and index a dialogue example database from the dialogue corpus, and the proper system response is determined by retrieving the best dialogue example for the current dialogue situation, which includes a current user utterance, dialogue act, semantic frame and discourse history. When the dialogue corpus is not enough to cover the domain, we also apply manually constructed situation-based rules mainly for meta-level dialogue management. Experiments show that our example-based dialogue modeling is very useful and effective in domain-oriented dialogue processing

Spoken dialog tasks incur many errors including speech recognition errors, understanding errors, and even dialog management errors. These errors create a big gap between user's will and the system's understanding, and eventually result in a misinterpretation. To fill in the gap, people in human-to-human dialog try to clarify the major causes of the misunderstanding and selectively correct them. This paper presents a method for applying the human's clarification techniques to human-machine spoken dialog systems. To increase the error detection precision and error recovery efficiency for the clarification dialogs, error detection phase is organized into three systematic phases and a clarification expert is devised for recovering the errors using the three phase verification. The experiment results demonstrate that the three phase verification could effectively catch the word and utterance-level errors in order to increase the SLU (spoken language understanding) performance and the clarification experts can actually increase the dialog success rate and the dialog efficiency.

This paper discusses development of a multi-domain conversational dialog system for simultaneously managing chats and goal-oriented dialogs. In this paper, we present a UMDM (unified multi-domain dialog manager) using a novel example-based dialog management technique. We have developed an effective utterance classifier with linguistic, semantic, and keyword features for domain switching and an example-based dialog modeling technique for domain-portable dialog models. Our experiments show that our approach is very useful and effective in multi-domain dialog system.

We present a new language model adaptation framework integrated with error handling method to improve accuracy of speech recognition and performance of spoken language applications. The proposed error corrective language model adaptation approach exploits domain-specific language variations and recognition environment characteristics to provide robustness and adaptability for a spoken language system. We demonstrate some experiments of spoken dialogue tasks and empirical results which show an improvement of the accuracy for both speech recognition and spoken language understanding. 1.

A speech interface is often required in many application environments, such as telephone-based information retrieval, car navigation systems, and user-friendly interfaces, but the low speech recognition rate makes it difficult to extend its application to new fields. We propose a domain adaptation technique via error correction with a maximum entropy language model, which is a general and elegant framework to combine higher level linguistic knowledge. Our approach has the ability to correct both semantic and lexical errors in 1-best output from the black-box style speech recognizer, and can improve the performance of speech recognition and application system. Through extensive experiments using a speechdriven in-vehicle telematics information retrieval and spoken language understanding, we demonstrate the superior performance of our approach and some advantages over previous lexical-oriented error correction approaches.