Summary of Evaluation Of Rag Metrics For Question Answering in the Telecom Domain, by Sujoy Roychowdhury et al.
Evaluation of RAG Metrics for Question Answering in the Telecom Domain
by Sujoy Roychowdhury, Sumit Soman, H G Ranjani, Neeraj Gunda, Vansh Chhabra, Sai Krishna Bala
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 This paper proposes a modified version of the Retrieval Augmented Generation (RAG) Assessment (RAGAS) framework, which enables Large Language Models (LLMs) to perform Question Answering (QA) tasks in various domains. The modified package provides intermediate outputs of prompts and evaluates generated responses using faithfulness, context relevance, answer relevance, answer correctness, answer similarity, and factual correctness metrics. Expert evaluations of the output show challenges in applying this approach in the telecom domain. Analysis reveals that some metrics have higher values for correct retrieval, while others differ between base embeddings and domain-adapted ones via pre-training and fine-tuning. The paper concludes on the suitability and challenges of using these metrics for real-world telecom QA tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study improves a popular tool that helps big language models answer questions correctly. They modified this tool to provide more detailed information about how well the answers match the original question. The team tested this new version on telecom-related questions and found some challenges in using it for real-world tasks. They also discovered that certain measures of quality are higher when the answers are correct, while others differ depending on whether the model is using general or domain-specific knowledge. Overall, the study explores the strengths and limitations of this tool for answering complex questions. |
Keywords
» Artificial intelligence » Fine tuning » Question answering » Rag » Retrieval augmented generation
