The Battle of LLMs: A Comparative Study in Conversational QA Tasks

Aryan Rangapur, Aman Rangapur·May 28, 2024

Summary

This study compares the performance of five large language models (ChatGPT, GPT-4, Gemini, Mixtral, and Claude) in conversational question-answering tasks, focusing on accuracy across CoQA, DialFact, FaVIQ, and CoDAH datasets. GPT-4 and Claude stand out for their improved accuracy and consistency, particularly in Chain of Thought and few-shot learning scenarios. However, models like ChatGPT-3, Gemini, and Mixtral exhibit inconsistent and occasionally misleading answers. The research highlights the potential of these models in applications like customer support but also underscores the need for refining their performance, addressing ethical concerns, and handling low-resource and conversational complexities. Mixtral's strong performance across benchmarks suggests further investigation. The study contributes to the understanding of conversational AI and its limitations, guiding future development in the field.

Key findings

4

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to explore the performance of various large language models, including ChatGPT, Gemini, Mixtral, and Claude, in Conversational QA tasks and analyze their potential applications across different domains . This research delves into assessing the accuracy and consistency of these models' responses to different datasets, identifying areas where the models may exhibit errors . While the use of large language models in Conversational QA tasks is not a new concept, the paper contributes to a comprehensive comparison and evaluation of these state-of-the-art models, shedding light on their capabilities and highlighting potential areas for improvement .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that ChatGPT, Gemini, Mixtral, and Claude, along with existing QA corpora, have significant potential for conversational QA tasks, showcasing improvements in the latest GPT-4 model. The study evaluates these models' capabilities in generating large-scale responses and calculates metrics like BLEU, ROUGE, and TER scores to assess the reliability and suitability of their output for conversational QA tasks .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "The Battle of LLMs: A Comparative Study in Conversational QA Tasks" introduces several new ideas, methods, and models in the field of large language models (LLMs) and conversational question answering (QA) tasks . Here are some key points from the paper:

  1. Models Introduced: The paper discusses various advanced language models such as ChatGPT, GPT-4, Gemini, Mixtral, and Claude, highlighting their capabilities and applications in different domains .

  2. Performance Evaluation: The study evaluates the accuracy, consistency, and performance of these models across different conversational QA corpora, pinpointing instances where the models provided inaccurate answers and areas where they may be prone to errors .

  3. Evaluation Metrics: The paper employs evaluation metrics like BLEU, ROUGE, and Chain of Thought method to assess the quality, fluency, and reliability of the model's responses .

  4. Training Process: It delves into the pre-training process of these models, which involves unsupervised pre-training on massive text datasets followed by supervised fine-tuning on labeled datasets to adapt to specific tasks .

  5. Human-in-the-Loop: The models undergo a "human-in-the-loop" phase where human feedback is incorporated to enhance the model's ability to understand and respond effectively to nuanced instructions, ensuring improved performance .

  6. Applications: These LLMs are considered disruptive technologies with applications in customer service, education, healthcare, finance, and more .

  7. Future Research Directions: The paper suggests future research directions such as including external knowledge sources for fact-checking, investigating alternative approaches for fine-tuning models, and exploring the ethics of AI models like ChatGPT .

Overall, the paper provides a comprehensive comparison and evaluation of these state-of-the-art language models, shedding light on their capabilities, potential areas for improvement, and implications across various domains. The paper "The Battle of LLMs: A Comparative Study in Conversational QA Tasks" introduces several characteristics and advantages of advanced language models like ChatGPT, GPT-4, Gemini, Mixtral, and Claude compared to previous methods . Here are some key points:

  1. Performance Analysis: The study meticulously evaluates the capabilities of these models across different conversational QA corpora, highlighting their potential to generate high-quality responses with an average BLEU score of 0.79 and an average ROUGE-L score of 0.53 . This indicates the models' proficiency in producing relevant and coherent answers.

  2. Enhanced Accuracy and Relevance: GPT-4 and Claude outperform ChatGPT-3, Gemini, and Mixtral in terms of accuracy, relevance, and consistency . These models demonstrate significant improvements in generating contextually relevant responses, making them promising choices for conversational QA tasks.

  3. Superior Performance: In Chain of Thought evaluations, as well as Zero Shot and 3-shot learning scenarios, GPT-4 and Claude exhibit superior performance compared to other models . They showcase enhanced accuracy and relevance across various scenarios, indicating their robustness and effectiveness in maintaining context and generating appropriate responses.

  4. Scalability and Flexibility: The research underscores the scalability and flexibility of these advanced language models, particularly highlighting ChatGPT's ability to adeptly handle diverse conversational QA tasks . This versatility positions the models as valuable solutions for applications ranging from virtual assistants to customer service chatbots and creative content generation.

  5. Human-in-the-Loop Refinement: The models undergo a "human-in-the-loop" phase where human feedback is incorporated to enhance their ability to comprehend and respond effectively to nuanced instructions . This iterative refinement ensures improved performance and alignment with human communication nuances.

  6. Potential Applications: These large language models are considered groundbreaking technologies with applications in various domains such as chatbots, language translation, text summarization, and creative content generation . Industries like e-commerce, customer service, and healthcare have already adopted these technologies to provide personalized and efficient customer support.

In essence, the paper highlights the advancements and advantages of these advanced language models in conversational QA tasks, emphasizing their improved accuracy, relevance, and performance compared to previous methods.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

In the field of large language models and conversational QA tasks, there are several related research studies and notable researchers:

  • Researchers such as Aryan Rangapur, Aman Rangapur, and various others have conducted studies on large language models like ChatGPT, Gemini, Mixtral, and Claude, evaluating their performance in conversational QA tasks .
  • Noteworthy researchers in this field include Siva Reddy, Danqi Chen, Christopher D. Manning, Yiqiu Shen, Laura Heacock, and many others who have contributed to research on conversational question answering tasks .
  • The key to the solution mentioned in the paper involves analyzing the reliability and suitability of the output of large language models like ChatGPT, Gemini, Mixtral, and Claude for conversational QA tasks. The researchers developed a pipeline to generate large-scale responses and calculated metrics such as BLEU, ROUGE, and TER scores to evaluate the models' responses, highlighting their potential for conversational QA tasks .

How were the experiments in the paper designed?

The experiments in the paper were meticulously designed with a sophisticated pipeline that aimed to harness the capabilities of ChatGPT, Gemini, Mixtral, and Claude to produce expansive responses at scale. The experimental setup involved two pivotal modules: the question generation module and the response generation module . The question generation module was responsible for crafting questions, while the response generation module focused on generating large-scale responses for evaluation . Additionally, the experiments were conducted using the computational power of the NVIDIA RTX 3070 GPU with 16 GB VRAM to ensure reliable and efficient execution of the experimental procedures . The hyperparameter settings were carefully examined, with a max_length setting of 512 for the query producing the most promising results after systematic exploration .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the CoQA (Conversational Question Answering) dataset . However, the information about whether the code is open source is not explicitly mentioned in the provided context. For details regarding the open-source availability of the code used for the evaluation, it would be advisable to refer directly to the authors or the publication source of the study.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study meticulously analyzed the performance of ChatGPT, Gemini, Mixtral, and Claude across different Conversational QA corpora . The research aimed to assess the accuracy and consistency of the model's responses to various datasets and identify potential areas where the models may be prone to errors . By developing a pipeline that generated large-scale responses and conducting a thorough comparison with existing QA corpora, the study evaluated the reliability of the model's output for conversational QA tasks .

Furthermore, the study calculated various scores like BLEU, ROUGE, etc., to assess the golden ratio and fluency of the model's output . It also utilized methods like Chain of Thought, Zero Shot, and 3-shot learning to evaluate the model's ability to maintain context over a series of interrelated queries and quickly adapt to new tasks with minimal examples . These comprehensive analyses and evaluations provide robust evidence to support the scientific hypotheses under investigation in the paper.


What are the contributions of this paper?

This paper provides a comprehensive comparative study in Conversational QA tasks, evaluating the performance of various large language models such as ChatGPT, Gemini, Mixtral, Claude, GPT-4, and others . The study assesses the effectiveness of these models in generating responses for conversational QA tasks, highlighting their capabilities and limitations . The research delves into the responses generated by these models across different Conversational QA corpora, meticulously computing evaluation scores to compare their overall performance . The findings indicate that ChatGPT, Gemini, Mixtral, and Claude show promise for conversational QA tasks, with notable improvements observed in the latest GPT-4 model . The study also emphasizes the importance of enhancing the accuracy and specificity of responses generated by these models for practical applications .


What work can be continued in depth?

Further research in the field of large language models (LLMs) can focus on several areas to enhance their capabilities and address existing challenges. One avenue for continued work is the exploration of incorporating external knowledge sources, such as knowledge bases, to improve the accuracy and specificity of responses generated by LLMs . Additionally, investigating alternative approaches for fine-tuning these models specifically for conversational QA tasks could lead to more favorable outcomes . Moreover, future research could delve into refining the hyperparameter settings of LLMs, such as experimenting with different configurations like the max_length input, to optimize their performance . Another aspect that warrants further exploration is the evaluation of the responses generated by LLMs using a variety of metrics like BLEU, ROUGE, METEOR, and Jaccard scores to assess their accuracy, fluency, and coherence . This comprehensive evaluation can provide valuable insights into the strengths and weaknesses of LLMs in conversational QA tasks, guiding improvements in their performance .


Introduction
Background
Emergence of large language models in conversational AI
Importance of conversational question-answering tasks
Objective
To evaluate and compare model performance
Identify strengths and weaknesses of GPT-4, Claude, ChatGPT, Gemini, and Mixtral
Address ethical implications and future directions
Method
Data Collection
Selection of datasets: CoQA, DialFact, FaVIQ, and CoDAH
Few-shot and chain of thought scenarios
Data Preprocessing
Standardization and formatting for model input
Handling of conversational complexities and low-resource scenarios
Model Performance Analysis
GPT-4 and Claude
Accuracy and Consistency
Improved performance across datasets
Emphasis on chain of thought and few-shot learning
ChatGPT-3, Gemini, and Mixtral
Inconsistencies and Limitations
Occasional misleading answers
Performance variability across benchmarks
Ethical Considerations
Addressing biases and potential harm in responses
Transparency and responsible deployment
Mixtral's Performance
Strong performance implications for further research
Applications and Future Directions
Customer support potential
Recommendations for refining model capabilities
Navigating conversational AI advancements and challenges
Conclusion
Summary of findings and implications for the field of conversational AI
Call to action for future model development and improvements.
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
Which two models are noted for their improved accuracy and consistency in the research?
What does the study suggest regarding the potential applications of these models, and what areas need improvement?
What are some challenges faced by ChatGPT-3, Gemini, and Mixtral as mentioned in the study?
Which language models does the study compare in conversational question-answering tasks?

The Battle of LLMs: A Comparative Study in Conversational QA Tasks

Aryan Rangapur, Aman Rangapur·May 28, 2024

Summary

This study compares the performance of five large language models (ChatGPT, GPT-4, Gemini, Mixtral, and Claude) in conversational question-answering tasks, focusing on accuracy across CoQA, DialFact, FaVIQ, and CoDAH datasets. GPT-4 and Claude stand out for their improved accuracy and consistency, particularly in Chain of Thought and few-shot learning scenarios. However, models like ChatGPT-3, Gemini, and Mixtral exhibit inconsistent and occasionally misleading answers. The research highlights the potential of these models in applications like customer support but also underscores the need for refining their performance, addressing ethical concerns, and handling low-resource and conversational complexities. Mixtral's strong performance across benchmarks suggests further investigation. The study contributes to the understanding of conversational AI and its limitations, guiding future development in the field.
Mind map
Performance variability across benchmarks
Occasional misleading answers
Emphasis on chain of thought and few-shot learning
Improved performance across datasets
Inconsistencies and Limitations
Accuracy and Consistency
Handling of conversational complexities and low-resource scenarios
Standardization and formatting for model input
Few-shot and chain of thought scenarios
Selection of datasets: CoQA, DialFact, FaVIQ, and CoDAH
Address ethical implications and future directions
Identify strengths and weaknesses of GPT-4, Claude, ChatGPT, Gemini, and Mixtral
To evaluate and compare model performance
Importance of conversational question-answering tasks
Emergence of large language models in conversational AI
Call to action for future model development and improvements.
Summary of findings and implications for the field of conversational AI
Navigating conversational AI advancements and challenges
Recommendations for refining model capabilities
Customer support potential
Strong performance implications for further research
Transparency and responsible deployment
Addressing biases and potential harm in responses
ChatGPT-3, Gemini, and Mixtral
GPT-4 and Claude
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Applications and Future Directions
Mixtral's Performance
Ethical Considerations
Model Performance Analysis
Method
Introduction
Outline
Introduction
Background
Emergence of large language models in conversational AI
Importance of conversational question-answering tasks
Objective
To evaluate and compare model performance
Identify strengths and weaknesses of GPT-4, Claude, ChatGPT, Gemini, and Mixtral
Address ethical implications and future directions
Method
Data Collection
Selection of datasets: CoQA, DialFact, FaVIQ, and CoDAH
Few-shot and chain of thought scenarios
Data Preprocessing
Standardization and formatting for model input
Handling of conversational complexities and low-resource scenarios
Model Performance Analysis
GPT-4 and Claude
Accuracy and Consistency
Improved performance across datasets
Emphasis on chain of thought and few-shot learning
ChatGPT-3, Gemini, and Mixtral
Inconsistencies and Limitations
Occasional misleading answers
Performance variability across benchmarks
Ethical Considerations
Addressing biases and potential harm in responses
Transparency and responsible deployment
Mixtral's Performance
Strong performance implications for further research
Applications and Future Directions
Customer support potential
Recommendations for refining model capabilities
Navigating conversational AI advancements and challenges
Conclusion
Summary of findings and implications for the field of conversational AI
Call to action for future model development and improvements.
Key findings
4

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to explore the performance of various large language models, including ChatGPT, Gemini, Mixtral, and Claude, in Conversational QA tasks and analyze their potential applications across different domains . This research delves into assessing the accuracy and consistency of these models' responses to different datasets, identifying areas where the models may exhibit errors . While the use of large language models in Conversational QA tasks is not a new concept, the paper contributes to a comprehensive comparison and evaluation of these state-of-the-art models, shedding light on their capabilities and highlighting potential areas for improvement .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that ChatGPT, Gemini, Mixtral, and Claude, along with existing QA corpora, have significant potential for conversational QA tasks, showcasing improvements in the latest GPT-4 model. The study evaluates these models' capabilities in generating large-scale responses and calculates metrics like BLEU, ROUGE, and TER scores to assess the reliability and suitability of their output for conversational QA tasks .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "The Battle of LLMs: A Comparative Study in Conversational QA Tasks" introduces several new ideas, methods, and models in the field of large language models (LLMs) and conversational question answering (QA) tasks . Here are some key points from the paper:

  1. Models Introduced: The paper discusses various advanced language models such as ChatGPT, GPT-4, Gemini, Mixtral, and Claude, highlighting their capabilities and applications in different domains .

  2. Performance Evaluation: The study evaluates the accuracy, consistency, and performance of these models across different conversational QA corpora, pinpointing instances where the models provided inaccurate answers and areas where they may be prone to errors .

  3. Evaluation Metrics: The paper employs evaluation metrics like BLEU, ROUGE, and Chain of Thought method to assess the quality, fluency, and reliability of the model's responses .

  4. Training Process: It delves into the pre-training process of these models, which involves unsupervised pre-training on massive text datasets followed by supervised fine-tuning on labeled datasets to adapt to specific tasks .

  5. Human-in-the-Loop: The models undergo a "human-in-the-loop" phase where human feedback is incorporated to enhance the model's ability to understand and respond effectively to nuanced instructions, ensuring improved performance .

  6. Applications: These LLMs are considered disruptive technologies with applications in customer service, education, healthcare, finance, and more .

  7. Future Research Directions: The paper suggests future research directions such as including external knowledge sources for fact-checking, investigating alternative approaches for fine-tuning models, and exploring the ethics of AI models like ChatGPT .

Overall, the paper provides a comprehensive comparison and evaluation of these state-of-the-art language models, shedding light on their capabilities, potential areas for improvement, and implications across various domains. The paper "The Battle of LLMs: A Comparative Study in Conversational QA Tasks" introduces several characteristics and advantages of advanced language models like ChatGPT, GPT-4, Gemini, Mixtral, and Claude compared to previous methods . Here are some key points:

  1. Performance Analysis: The study meticulously evaluates the capabilities of these models across different conversational QA corpora, highlighting their potential to generate high-quality responses with an average BLEU score of 0.79 and an average ROUGE-L score of 0.53 . This indicates the models' proficiency in producing relevant and coherent answers.

  2. Enhanced Accuracy and Relevance: GPT-4 and Claude outperform ChatGPT-3, Gemini, and Mixtral in terms of accuracy, relevance, and consistency . These models demonstrate significant improvements in generating contextually relevant responses, making them promising choices for conversational QA tasks.

  3. Superior Performance: In Chain of Thought evaluations, as well as Zero Shot and 3-shot learning scenarios, GPT-4 and Claude exhibit superior performance compared to other models . They showcase enhanced accuracy and relevance across various scenarios, indicating their robustness and effectiveness in maintaining context and generating appropriate responses.

  4. Scalability and Flexibility: The research underscores the scalability and flexibility of these advanced language models, particularly highlighting ChatGPT's ability to adeptly handle diverse conversational QA tasks . This versatility positions the models as valuable solutions for applications ranging from virtual assistants to customer service chatbots and creative content generation.

  5. Human-in-the-Loop Refinement: The models undergo a "human-in-the-loop" phase where human feedback is incorporated to enhance their ability to comprehend and respond effectively to nuanced instructions . This iterative refinement ensures improved performance and alignment with human communication nuances.

  6. Potential Applications: These large language models are considered groundbreaking technologies with applications in various domains such as chatbots, language translation, text summarization, and creative content generation . Industries like e-commerce, customer service, and healthcare have already adopted these technologies to provide personalized and efficient customer support.

In essence, the paper highlights the advancements and advantages of these advanced language models in conversational QA tasks, emphasizing their improved accuracy, relevance, and performance compared to previous methods.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

In the field of large language models and conversational QA tasks, there are several related research studies and notable researchers:

  • Researchers such as Aryan Rangapur, Aman Rangapur, and various others have conducted studies on large language models like ChatGPT, Gemini, Mixtral, and Claude, evaluating their performance in conversational QA tasks .
  • Noteworthy researchers in this field include Siva Reddy, Danqi Chen, Christopher D. Manning, Yiqiu Shen, Laura Heacock, and many others who have contributed to research on conversational question answering tasks .
  • The key to the solution mentioned in the paper involves analyzing the reliability and suitability of the output of large language models like ChatGPT, Gemini, Mixtral, and Claude for conversational QA tasks. The researchers developed a pipeline to generate large-scale responses and calculated metrics such as BLEU, ROUGE, and TER scores to evaluate the models' responses, highlighting their potential for conversational QA tasks .

How were the experiments in the paper designed?

The experiments in the paper were meticulously designed with a sophisticated pipeline that aimed to harness the capabilities of ChatGPT, Gemini, Mixtral, and Claude to produce expansive responses at scale. The experimental setup involved two pivotal modules: the question generation module and the response generation module . The question generation module was responsible for crafting questions, while the response generation module focused on generating large-scale responses for evaluation . Additionally, the experiments were conducted using the computational power of the NVIDIA RTX 3070 GPU with 16 GB VRAM to ensure reliable and efficient execution of the experimental procedures . The hyperparameter settings were carefully examined, with a max_length setting of 512 for the query producing the most promising results after systematic exploration .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the CoQA (Conversational Question Answering) dataset . However, the information about whether the code is open source is not explicitly mentioned in the provided context. For details regarding the open-source availability of the code used for the evaluation, it would be advisable to refer directly to the authors or the publication source of the study.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study meticulously analyzed the performance of ChatGPT, Gemini, Mixtral, and Claude across different Conversational QA corpora . The research aimed to assess the accuracy and consistency of the model's responses to various datasets and identify potential areas where the models may be prone to errors . By developing a pipeline that generated large-scale responses and conducting a thorough comparison with existing QA corpora, the study evaluated the reliability of the model's output for conversational QA tasks .

Furthermore, the study calculated various scores like BLEU, ROUGE, etc., to assess the golden ratio and fluency of the model's output . It also utilized methods like Chain of Thought, Zero Shot, and 3-shot learning to evaluate the model's ability to maintain context over a series of interrelated queries and quickly adapt to new tasks with minimal examples . These comprehensive analyses and evaluations provide robust evidence to support the scientific hypotheses under investigation in the paper.


What are the contributions of this paper?

This paper provides a comprehensive comparative study in Conversational QA tasks, evaluating the performance of various large language models such as ChatGPT, Gemini, Mixtral, Claude, GPT-4, and others . The study assesses the effectiveness of these models in generating responses for conversational QA tasks, highlighting their capabilities and limitations . The research delves into the responses generated by these models across different Conversational QA corpora, meticulously computing evaluation scores to compare their overall performance . The findings indicate that ChatGPT, Gemini, Mixtral, and Claude show promise for conversational QA tasks, with notable improvements observed in the latest GPT-4 model . The study also emphasizes the importance of enhancing the accuracy and specificity of responses generated by these models for practical applications .


What work can be continued in depth?

Further research in the field of large language models (LLMs) can focus on several areas to enhance their capabilities and address existing challenges. One avenue for continued work is the exploration of incorporating external knowledge sources, such as knowledge bases, to improve the accuracy and specificity of responses generated by LLMs . Additionally, investigating alternative approaches for fine-tuning these models specifically for conversational QA tasks could lead to more favorable outcomes . Moreover, future research could delve into refining the hyperparameter settings of LLMs, such as experimenting with different configurations like the max_length input, to optimize their performance . Another aspect that warrants further exploration is the evaluation of the responses generated by LLMs using a variety of metrics like BLEU, ROUGE, METEOR, and Jaccard scores to assess their accuracy, fluency, and coherence . This comprehensive evaluation can provide valuable insights into the strengths and weaknesses of LLMs in conversational QA tasks, guiding improvements in their performance .

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