Enhancing Persian Conversational Question Answering by Combining Contextual Keyword Extraction and Large Language Models

核心概念

This paper presents a novel method to elevate the performance of Persian Conversational Question Answering (CQA) systems by combining the strengths of Large Language Models (LLMs) with contextual keyword extraction.

要約

The paper proposes a novel approach to improve Persian Conversational Question Answering (CQA) systems by leveraging a combination of contextual keyword extraction and Large Language Models (LLMs).
The key highlights are:

Contextual keyword extraction focuses on identifying crucial information and nuances within the conversation, enabling a deeper understanding of the user's intent. This provides additional context for the LLM to generate more relevant and coherent responses.

The method utilizes TopicRank, an unsupervised graph-based approach, to extract keywords from the Persian text data. This captures the inherent semantic relationships between words without requiring labeled training data.

The extracted keywords are used to enrich the prompts provided to the LLM, guiding it to generate responses that are accurate and consistent with the conversational flow.

Experiments on the PCoQA benchmark dataset demonstrate that the proposed approach, called PerkwE_COQA, outperforms existing Persian CQA models and the LLM-only baseline by up to 8% in terms of F1 score and exact match.

The research highlights the potential of combining contextual information with LLMs to overcome the challenges in Persian CQA, such as handling implicit questions, delivering contextually relevant answers, and tackling complex questions that rely heavily on conversational context.

The findings suggest that this method can contribute to the development of advanced and context-sensitive CQA systems for Farsi, which can enhance practical applications like smart city chatbots and virtual assistants.

統計

"Smart cities need the involvement of their residents to enhance quality of life, sustainability, and efficiency."
"Existing approaches have shown that LLMs offer promising capabilities for CQA, but may struggle to capture the nuances of conversational contexts."
"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."
"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."

引用

"This paper presents a novel method to elevate the performance of Persian Conversational question-answering (CQA) systems."
"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."
"The proposed method effectively handles implicit questions, delivers contextually relevant answers, and tackles complex questions that rely heavily on conversational context."

抽出されたキーインサイト

PerkwE_COQA

by Pardis Morad... 場所 arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05406.pdf

深掘り質問

How can the proposed method be extended to handle more complex conversational scenarios, such as multi-turn dialogues or open-ended discussions?

To extend the proposed method for handling more complex conversational scenarios, such as multi-turn dialogues or open-ended discussions, several enhancements can be implemented:

Memory Mechanism: Introduce a memory component to store past interactions and context from previous turns in the conversation. This memory can be utilized to maintain continuity and coherence in responses across multiple turns.

Contextual Understanding: Enhance the model's ability to understand and retain context by incorporating attention mechanisms that focus on relevant parts of the conversation history. This can help in generating more contextually appropriate responses.

Dynamic Prompting: Develop a dynamic prompting strategy that adapts based on the evolving conversation. By adjusting prompts based on the current dialogue context, the model can generate more accurate and relevant responses.

Multi-stage Processing: Implement a multi-stage processing approach where the model first extracts key information from the conversation using contextual keyword extraction and then utilizes this information to generate responses. This can help in maintaining relevance and coherence in multi-turn dialogues.

Fine-tuning on Multi-turn Data: Fine-tune the model on datasets specifically designed for multi-turn dialogues to improve its performance in handling complex conversational scenarios. This can help the model learn the nuances of multi-turn interactions and generate more engaging responses.

By incorporating these enhancements, the proposed method can be extended to effectively handle more complex conversational scenarios, ensuring seamless interactions in multi-turn dialogues and open-ended discussions.

What are the potential limitations or biases that may arise from the use of LLMs in Persian CQA systems, and how can they be addressed?

Potential limitations and biases that may arise from the use of Large Language Models (LLMs) in Persian CQA systems include:

Data Bias: LLMs can inherit biases present in the training data, leading to biased or inaccurate responses. This can disproportionately affect certain demographics or viewpoints in the generated answers.

Language Specificity: LLMs trained on a specific dataset may struggle with handling diverse linguistic nuances and cultural references in Persian, potentially resulting in inaccuracies or misunderstandings in responses.

Hallucination: LLMs may generate plausible but incorrect information, especially in scenarios where the model lacks the necessary context to provide accurate answers. This can lead to misleading responses in Persian CQA systems.

To address these limitations and biases, the following strategies can be implemented:

Diverse Training Data: Curate a diverse and representative training dataset that covers a wide range of topics, perspectives, and linguistic variations in Persian. This can help mitigate biases and improve the model's understanding of different contexts.

Bias Detection and Mitigation: Implement bias detection mechanisms to identify and mitigate biases in the model's responses. Techniques such as debiasing algorithms and adversarial training can help reduce bias in Persian CQA systems.

Contextual Understanding: Enhance the model's ability to understand and incorporate contextual information from the conversation to reduce the likelihood of generating hallucinated or inaccurate responses. This can improve the overall accuracy and relevance of the answers provided.

By addressing these potential limitations and biases through careful dataset curation, bias mitigation strategies, and improved contextual understanding, LLMs in Persian CQA systems can deliver more reliable and unbiased responses.

How can the integration of contextual keyword extraction and LLMs be leveraged to enhance other Persian NLP tasks beyond CQA, such as text summarization or dialogue generation?

The integration of contextual keyword extraction and Large Language Models (LLMs) can be leveraged to enhance various Persian Natural Language Processing (NLP) tasks beyond Conversational Question Answering (CQA), including text summarization and dialogue generation:

Text Summarization:

Keyword-based Summarization: Utilize contextual keyword extraction to identify key phrases and important information in the text. These keywords can then be used to guide the LLM in generating concise and informative summaries.
Contextual Understanding: Incorporate contextual information extracted from the text to improve the coherence and relevance of the generated summaries. This can help in producing more contextually accurate and comprehensive summaries.

Dialogue Generation:

Contextual Prompting: Use contextual keyword extraction to identify key elements in the dialogue that can guide the generation of responses in dialogue systems. This can help in maintaining consistency and relevance across different turns in the conversation.
Multi-turn Dialogue Management: Leverage the combination of contextual keyword extraction and LLMs to manage multi-turn dialogues effectively. By understanding the context of the conversation, the model can generate more engaging and contextually appropriate responses.

Language Modeling:

Enhanced Language Understanding: By integrating contextual keyword extraction, the LLM can gain a deeper understanding of the text and generate more contextually relevant language models. This can improve the model's performance in tasks like language generation and understanding.

By leveraging the strengths of contextual keyword extraction and LLMs in tasks such as text summarization, dialogue generation, and language modeling, Persian NLP systems can benefit from enhanced contextual understanding, improved coherence in responses, and more accurate generation of text across various applications.