Summary of The Battle Of Llms: a Comparative Study in Conversational Qa Tasks, by Aryan Rangapur et al.
The Battle of LLMs: A Comparative Study in Conversational QA Tasks
by Aryan Rangapur, Aman Rangapur
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comparative study of large language models, specifically ChatGPT, GPT-4, Gemini, Mixtral, and Claude. These models have gained popularity in various domains such as customer service, education, healthcare, and finance. The research evaluates the performance of these models across different Conversational QA corpora, computing evaluation scores to compare their overall performance. The study highlights instances where the models provided inaccurate answers, offering insights into potential areas for improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper compares large language models like ChatGPT, GPT-4, Gemini, Mixtral, and Claude. These models are used in things like customer service and education. The researchers looked at how well these models did on different tasks and found some places where they got answers wrong. This helps us understand what’s good and bad about these models. |
Keywords
» Artificial intelligence » Claude » Gemini » Gpt
