Interpretable and Robust Dialogue State Tracking via Natural Language Summarization with LLMs

This paper introduces a novel approach to Dialogue State Tracking (DST) that leverages Large Language Models (LLMs) to generate natural language descriptions of dialogue states, moving beyond traditional slot-value representations. Conventional DST methods struggle with open-domain dialogues and noisy inputs. Motivated by the generative capabilities of LLMs, our Natural Language DST (NL-DST) framework trains an LLM to directly synthesize human-readable state descriptions. We demonstrate through extensive experiments on MultiWOZ 2.1 and Taskmaster-1 datasets that NL-DST significantly outperforms rule-based and discriminative BERT-based DST baselines, as well as generative slot-filling GPT-2 DST models, in both Joint Goal Accuracy and Slot Accuracy. Ablation studies and human evaluations further validate the effectiveness of natural language state generation, highlighting its robustness to noise and enhanced interpretability. Our findings suggest that NL-DST offers a more flexible, accurate, and human-understandable approach to dialogue state tracking, paving the way for more robust and adaptable task-oriented dialogue systems.

Align, Generate, Learn: A Novel Closed-Loop Framework for Cross-Lingual In-Context Learning

Cross-lingual in-context learning (XICL) has emerged as a transformative paradigm for leveraging large language models (LLMs) to tackle multilingual tasks, especially for low-resource languages. However, existing approaches often rely on external retrievers or task-specific fine-tuning, limiting their scalability and generalizability. In this paper, we propose a novel self-supervised framework that harnesses the generative capabilities of LLMs to internally select and utilize task-relevant examples. Our method introduces two key objectives: a retrieval-generation alignment loss to optimize the quality of selected examples and a semantic coherence loss to ensure cross-lingual consistency. Through extensive experiments on multilingual benchmarks, our approach achieves state-of-the-art performance, significantly outperforming existing baselines. Further analysis highlights its robustness across diverse language families and its ability to generalize to unseen tasks. Human evaluations confirm the superior fluency, relevance, and semantic correctness of outputs generated by our method. This work provides a scalable, effective, and generalizable solution for cross-lingual in-context learning.

Enhancing Scientific Figure Captioning Through Cross-modal Learning

Scientific charts are essential tools for effectively communicating research findings, serving as a vital medium for conveying information and revealing data patterns. With the rapid advancement of science and technology, coupled with the advent of the big data era, the volume and diversity of scientific research data have surged, leading to an increase in the number and variety of charts. This trend presents new challenges for researchers, particularly in efficiently and accurately generating appropriate titles for these charts to better convey their information and results. Automatically generated chart titles can enhance information retrieval systems by providing precise data for detailed chart classification. As research in image captioning and text summarization matures, the automatic generation of scientific chart titles has gained significant attention. By leveraging natural language processing, machine learning, and multimodal techniques, it is possible to automatically extract key information from charts and generate accurate, concise titles that better serve the needs of researchers. This paper presents a novel approach to scientific chart title generation, demonstrating its effectiveness in improving the clarity and accessibility of research data.