Summary of Strategic Prompting For Conversational Tasks: a Comparative Analysis Of Large Language Models Across Diverse Conversational Tasks, by Ratnesh Kumar Joshi et al.
Strategic Prompting for Conversational Tasks: A Comparative Analysis of Large Language Models Across Diverse Conversational Tasks
by Ratnesh Kumar Joshi, Priyanshu Priya, Vishesh Desai, Saurav Dudhate, Siddhant Senapati, Asif Ekbal, Roshni Ramnani, Anutosh Maitra, Shubhashis Sengupta
First submitted to arxiv on: 26 Nov 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 A comprehensive study evaluates the capabilities and limitations of five prominent Large Language Models (LLMs) – Llama, OPT, Falcon, Alpaca, and MPT – in various conversational tasks, including reservation, empathetic response generation, mental health and legal counseling, persuasion, and negotiation. The evaluation employs a multi-criteria approach combining automatic and human metrics to assess the models’ performance. While no single model excels universally, their performance varies significantly depending on task-specific requirements. This highlights the importance of considering task characteristics when selecting suitable LLMs for conversational applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A group of researchers studied how well five popular language models work at having conversations. They tested these models in different scenarios like making reservations or giving advice. The study used a mix of computer-based and human evaluations to see how well the models performed. What they found was that no one model is best for all situations – each has its strengths and weaknesses. This means that when we want language models to help with conversations, we need to think about what kind of conversation it will be and choose the right model for the job. |
Keywords
» Artificial intelligence » Llama
