Summary of Towards Optimizing and Evaluating a Retrieval Augmented Qa Chatbot Using Llms with Human in the Loop, by Anum Afzal et al.
Towards Optimizing and Evaluating a Retrieval Augmented QA Chatbot using LLMs with Human in the Loop
by Anum Afzal, Alexander Kowsik, Rajna Fani, Florian Matthes
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| 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 novel application of Large Language Models (LLMs) is explored in this paper, focusing on developing an HR support chatbot for addressing employee inquiries. By integrating a human-in-the-loop approach during dataset collection, prompt optimization, and output evaluation, the authors enhanced the LLM-driven chatbot’s response quality. The study finds that GPT-4 outperforms other models, overcoming data inconsistencies through internal reasoning capabilities. Furthermore, reference-free evaluation metrics such as G-Eval and Prometheus demonstrate reliability aligned with human evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a special kind of computer model called a Large Language Model to help people working in Human Resources answer questions from employees. The researchers worked closely with experts from SAP to make the model better by adding some human input at different stages of development. They found that one specific model, GPT-4, does a great job and can even fix mistakes by thinking carefully about what it’s saying. They also tested special ways to measure how well the model is doing, and these methods are similar to how humans evaluate things. |
Keywords
» Artificial intelligence » Gpt » Large language model » Optimization » Prompt
