Task-oriented dialogue systems employ natural language understanding (NLU) modules to manage the intricate and continually evolving business requirements of production systems.Although the development of Large Language Models (LLMs) introduced extraordinary chitchat capabilities, implementing LLMs into such systems brought new difficulties.One of the main challenges is the lack of specific datasets for training and evaluation of systems that offer both capabilities: chat and task. As NLU modules are designed to handle complex task requests and LLMs are utilized to specifically answer chitchat interactions, the system must correctly identify the functional intent of the user to utilize an applicable module. This paper presents CTFusion, a multi-turn dialogue generation framework designed to assist the evaluation and training of production systems that offer both capabilities. Utilizing the framework, we generate a multi-turn dialogue dataset for in-vehicle speech recognition system, which includes 41,211 dialogues of 240 real-world in-vehicle intents, and train In-vehicle Context Sensor (ICS), a lightweight model that successfully identifies the functional intent of the driver.ICS outperforms all baseline models across various experimental settings, which demonstrates that CTFusion can help generate relevant datasets with a complex business logic, which can subsequently assist production systems in leveraging LLMs for their chitchat capabilities.

In-vehicle speech recognition (IVSR) systems are crucial components of modern automotive interfaces, enabling hands-free control and enhancing user safety. However, traditional IVSR systems often struggle with interpreting user intent accurately due to limitations in contextual understanding and ambiguity resolution, leading to user frustration. This paper introduces LLM ContextBridge, a novel hybrid architecture that integrates Pretrained Language Model-based intent classification with Large Language Models to enhance both command recognition and dialogue management. LLM ContextBridge serves as a seamless bridge between traditional natural language understanding techniques and LLMs, combining the precise intent recognition of conventional NLU with the contextual handling and ambiguity resolution capabilities of LLMs. This approach significantly improves recognition accuracy and user experience, particularly in complex, multi-turn dialogues. Experimental results show notable improvements in task success rates and user satisfaction, demonstrating that LLM ContextBridge can make IVSR systems more intuitive, responsive, and context-aware.

Recent advances in QA pair generation (QAG) have raised interest in applying this technique to the educational field. However, the diversity of QA types remains a challenge despite its contributions to comprehensive learning and assessment of children. In this paper, we propose a QAG framework that enhances QA type diversity by producing different interrogative sentences and implicit/explicit answers. Our framework comprises a QFS-based answer generator, an iterative QA generator, and a relevancy-aware ranker. The two generators aim to expand the number of candidates while covering various types. The ranker trained on the in-context negative samples clarifies the top-N outputs based on the ranking score. Extensive evaluations and detailed analyses demonstrate that our approach outperforms previous state-of-the-art results by significant margins, achieving improved diversity and quality. Our task-oriented processes are consistent with real-world demand, which highlights our system’s high applicability.

Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. However, their ability to establish causal relationships, particularly in the context of temporal interventions and language hallucinations, remains challenging. This paper presents CReTIHC, a novel dataset designed to test and enhance the causal reasoning abilities of LLMs. The dataset is constructed using a unique approach that incorporates elements of verbal hallucinations and temporal interventions through the reengineering of existing causal inference datasets. This transformation creates complex scenarios that push LLMs to critically evaluate the information presented and identify cause-and-effect relationships. The CReTIHC dataset serves as a pioneering tool for improving LLM’s causal inference capabilities, paving the way for a more nuanced understanding of causal relationships in natural language processing (NLP) tasks. The whole dataset is publicly accessible at: (https://github.com/ChangwooChun/CReTIHC)