Advances in Conversational Systems and Data Analytics

The field of conversational systems and data analytics is rapidly evolving, with a focus on developing innovative and interactive platforms that can provide actionable insights and support decision-making. Recent developments have seen the integration of natural language processing, machine learning, and data visualization to create more intuitive and user-friendly systems. These advancements have the potential to transform various domains, including urban planning, environmental monitoring, and education. Notably, the use of large language models and multimodal approaches has improved the accuracy and reliability of conversational systems, enabling them to provide more accurate and informative responses. Furthermore, the development of agentic assistants and interactive platforms has facilitated the interpretation and analysis of complex data, making it more accessible to non-experts. Some noteworthy papers in this area include: OceanAI, which presents a conversational platform for accurate and transparent oceanographic insights. PSD2Code, which introduces a novel multi-modal approach for automated front-end code generation from design files.

Sources

RailEstate: An Interactive System for Metro Linked Property Trends

Portal UX Agent - A Plug-and-Play Engine for Rendering UIs from Natural Language Specifications

OceanAI: A Conversational Platform for Accurate, Transparent, Near-Real-Time Oceanographic Insights

VayuChat: An LLM-Powered Conversational Interface for Air Quality Data Analytics

InteracSPARQL: An Interactive System for SPARQL Query Refinement Using Natural Language Explanations

KnowThyself: An Agentic Assistant for LLM Interpretability

PSD2Code: Automated Front-End Code Generation from Design Files via Multimodal Large Language Models

IntelliProof: An Argumentation Network-based Conversational Helper for Organized Reflection

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