Smart cities need the involvement of their residents to enhance quality of
life. Conversational query-answering is an emerging approach for user
engagement. There is an increasing demand of an advanced conversational
question-answering that goes beyond classic systems. Existing approaches have
shown that LLMs offer promising capabilities for CQA, but may struggle to
capture the nuances of conversational contexts. The new approach involves
understanding the content and engaging in a multi-step conversation with the
user to fulfill their needs. This paper presents a novel method to elevate the
performance of Persian Conversational question-answering (CQA) systems. It
combines the strengths of Large Language Models (LLMs) with contextual keyword
extraction. Our method extracts keywords specific to the conversational flow,
providing the LLM with additional context to understand the user's intent and
generate more relevant and coherent responses. We evaluated the effectiveness
of this combined approach through various metrics, demonstrating significant
improvements in CQA performance compared to an LLM-only baseline. The proposed
method effectively handles implicit questions, delivers contextually relevant
answers, and tackles complex questions that rely heavily on conversational
context. The findings indicate that our method outperformed the evaluation
benchmarks up to 8% higher than existing methods and the LLM-only baseline.

本篇论文提出了一种新的方法，通过结合大型语言模型（LLMs）和上下文关键词提取，提升波斯语对话式问答系统（CQA）的性能，实现更准确、连贯的回答，有效处理隐含问题和依赖于对话上下文的复杂问题。评估结果显示，该方法在 CQA 性能上相较于现有方法和仅使用 LLMs 的基准测试结果高出 8%。

PerkwE_COQA：将上下文关键词提取与大型语言模型相结合以增强波斯语会话问答

PerkwE_COQA: enhance Persian Conversational Question Answering by  combining contextual keyword extraction with Large Language Models

Information visualizations such as bar charts and line charts are very common
for analyzing data and discovering critical insights. Often people analyze
charts to answer questions that they have in mind. Answering such questions can
be challenging as they often require a significant amount of perceptual and
cognitive effort. Chart Question Answering (CQA) systems typically take a chart
and a natural language question as input and automatically generate the answer
to facilitate visual data analysis. Over the last few years, there has been a
growing body of literature on the task of CQA. In this survey, we
systematically review the current state-of-the-art research focusing on the
problem of chart question answering. We provide a taxonomy by identifying
several important dimensions of the problem domain including possible inputs
and outputs of the task and discuss the advantages and limitations of proposed
solutions. We then summarize various evaluation techniques used in the surveyed
papers. Finally, we outline the open challenges and future research
opportunities related to chart question answering.

本篇综述论文旨在系统回顾当前关于数据可视化分析中图表问题回答（Chart Question Answering，CQA）系统的最新研究进展。论文采用分类法，从任务输入输出和解决方案等多个角度进行了整理和讨论，并总结了相关试验评估技术，同时还指出了与 CQA 相关的挑战和未来研究机遇。