The Battle of LLMs: A Comparative Study in Conversational QA Tasks
Project Overview
The document explores the role of generative AI, particularly large language models (LLMs) like ChatGPT, GPT-4, Gemini, Mixtral, and Claude, in education, focusing on their performance in conversational question-answering tasks. It underscores the models' strengths in generating human-like text, which can enhance educational experiences through personalized learning and instant feedback. However, the document also points out significant limitations, such as challenges in achieving consistent accuracy, particularly with complex queries, which can impact their effectiveness in educational settings. User perspectives are highlighted as crucial for evaluating these models, suggesting that feedback from educators and learners is essential for ongoing improvements. The findings indicate that while generative AI holds considerable potential for transforming educational practices by offering innovative applications, there is an urgent need for further refinement of these models to ensure reliability and relevance in real-world educational contexts. Overall, the document presents a balanced view of the promise and challenges of integrating generative AI in education, advocating for continued advancements to fully leverage their capabilities.
Key Applications
Conversational QA using ChatGPT, GPT-4, Gemini, Mixtral, and Claude
Context: Educational context focusing on conversational AI and language processing; target audience includes students, educators, and developers in AI and education sectors.
Implementation: Developed a pipeline for question and response generation, utilizing datasets like CoQA and DialFact for evaluation.
Outcomes: Achieved high-quality responses with significant improvements in relevance and consistency; identified strengths and weaknesses of different models.
Challenges: Models occasionally provide generic or irrelevant answers; inconsistencies in responses can reduce reliability.
Implementation Barriers
Technical Barrier
Inconsistencies and inaccuracies in model responses can undermine their reliability for practical applications.
Proposed Solutions: Refinement of prompts and exploration of alternative generation parameters; potential integration of external knowledge sources.
Ethical Barrier
Biases in the models, particularly towards male gender in ambiguous questions, can affect fairness.
Proposed Solutions: Further research needed to address biases and ensure equitable responses.
Project Team
Aryan Rangapur
Researcher
Aman Rangapur
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Aryan Rangapur, Aman Rangapur
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai