Summary of Perkwe_coqa: Enhanced Persian Conversational Question Answering by Combining Contextual Keyword Extraction with Large Language Models, By Pardis Moradbeiki et al.
PerkwE_COQA: Enhanced Persian Conversational Question Answering by combining contextual keyword extraction with Large Language Models
by Pardis Moradbeiki, Nasser Ghadiri
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach for enhancing conversational query-answering in Persian is presented, combining Large Language Models (LLMs) with contextual keyword extraction. The method extracts keywords specific to the conversational flow, providing the LLM with additional context to understand user intent and generate more relevant responses. This results in significant improvements in CQA performance compared to an LLM-only baseline. The approach effectively handles implicit questions, delivers contextually relevant answers, and tackles complex questions relying on conversational context. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Smart cities need residents’ involvement to improve quality of life. Conversational query-answering is an emerging way for user engagement. This paper presents a new method to make Persian conversational question-answering better. It combines big language models with keywords from the conversation. The model gets more context and can give more relevant answers. This helps handle tricky questions, delivers good responses, and tackles complex conversations. |
Keywords
» Artificial intelligence » Question answering
