Evaluation of RAG Metrics for Question Answering in the Telecom Domain

• URL: http://arxiv.org/abs/2407.12873v1
• Date: Mon, 15 Jul 2024 17:40:15 GMT
• Title: Evaluation of RAG Metrics for Question Answering in the Telecom Domain
• Authors: Sujoy Roychowdhury, Sumit Soman, H G Ranjani, Neeraj Gunda, Vansh Chhabra, Sai Krishna Bala,
• Abstract summary: Retrieval Augmented Generation (RAG) is widely used to enable Large Language Models (LLMs) perform Question Answering (QA) tasks. This work is a modified version of this package for few metrics (faithfulness, context relevance, answer relevance, answer correctness, answer similarity and factual correctness) through which we provide the intermediate outputs of the prompts. Next, we analyse the expert evaluations of the output of the modified RAGAS package and observe the challenges of using it in the telecom domain.
• Score: 0.650923326742559
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval Augmented Generation (RAG) is widely used to enable Large Language Models (LLMs) perform Question Answering (QA) tasks in various domains. However, RAG based on open-source LLM for specialized domains has challenges of evaluating generated responses. A popular framework in the literature is the RAG Assessment (RAGAS), a publicly available library which uses LLMs for evaluation. One disadvantage of RAGAS is the lack of details of derivation of numerical value of the evaluation metrics. One of the outcomes of this work is a modified version of this package for few metrics (faithfulness, context relevance, answer relevance, answer correctness, answer similarity and factual correctness) through which we provide the intermediate outputs of the prompts by using any LLMs. Next, we analyse the expert evaluations of the output of the modified RAGAS package and observe the challenges of using it in the telecom domain. We also study the effect of the metrics under correct vs. wrong retrieval and observe that few of the metrics have higher values for correct retrieval. We also study for differences in metrics between base embeddings and those domain adapted via pre-training and fine-tuning. Finally, we comment on the suitability and challenges of using these metrics for in-the-wild telecom QA task.

Related papers

• Self-Calibrated Listwise Reranking with Large Language Models [137.6557607279876]
  Large language models (LLMs) have been employed in reranking tasks through a sequence-to-sequence approach. This reranking paradigm requires a sliding window strategy to iteratively handle larger candidate sets. We propose a novel self-calibrated listwise reranking method, which aims to leverage LLMs to produce global relevance scores for ranking.
  arXiv  Detail & Related papers  (2024-11-07T10:31:31Z)
• Evaluating ChatGPT on Nuclear Domain-Specific Data [0.0]
  This paper examines the application of ChatGPT, a large language model (LLM), for question-and-answer (Q&A) tasks in the highly specialized field of nuclear data. The primary focus is on evaluating ChatGPT's performance on a curated test dataset. The findings underscore the improvement in performance when incorporating a RAG pipeline in an LLM.
  arXiv  Detail & Related papers  (2024-08-26T08:17:42Z)
• W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answering [28.79851078451609]
  Large Language Models (LLMs) often struggle to generate factual answers relying solely on their internal (parametric) knowledge. To address this limitation, Retrieval-Augmented Generation (RAG) systems enhance LLMs by retrieving relevant information from external sources. We propose W-RAG by utilizing the ranking capabilities of LLMs to create weakly labeled data for training dense retrievers.
  arXiv  Detail & Related papers  (2024-08-15T22:34:44Z)
• BERGEN: A Benchmarking Library for Retrieval-Augmented Generation [26.158785168036662]
  Retrieval-Augmented Generation allows to enhance Large Language Models with external knowledge. Inconsistent benchmarking poses a major challenge in comparing approaches and understanding the impact of each component in the pipeline. In this work, we study best practices that lay the groundwork for a systematic evaluation of RAG and present BERGEN, an end-to-end library for reproducible research standardizing RAG experiments.
  arXiv  Detail & Related papers  (2024-07-01T09:09:27Z)
• R-Eval: A Unified Toolkit for Evaluating Domain Knowledge of Retrieval Augmented Large Language Models [51.468732121824125]
  Large language models have achieved remarkable success on general NLP tasks, but they may fall short for domain-specific problems. Existing evaluation tools only provide a few baselines and evaluate them on various domains without mining the depth of domain knowledge. In this paper, we address the challenges of evaluating RALLMs by introducing the R-Eval toolkit, a Python toolkit designed to streamline the evaluation of different RAGs.
  arXiv  Detail & Related papers  (2024-06-17T15:59:49Z)
• RaFe: Ranking Feedback Improves Query Rewriting for RAG [83.24385658573198]
  We propose a framework for training query rewriting models free of annotations. By leveraging a publicly available reranker, oursprovides feedback aligned well with the rewriting objectives.
  arXiv  Detail & Related papers  (2024-05-23T11:00:19Z)
• REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering [122.62012375722124]
  In existing methods, large language models (LLMs) cannot precisely assess the relevance of retrieved documents. We propose REAR, a RElevance-Aware Retrieval-augmented approach for open-domain question answering (QA)
  arXiv  Detail & Related papers  (2024-02-27T13:22:51Z)
• CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models [49.16989035566899]
  Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources. This paper constructs a large-scale and more comprehensive benchmark, and evaluates all the components of RAG systems in various RAG application scenarios.
  arXiv  Detail & Related papers  (2024-01-30T14:25:32Z)
• Systematic Assessment of Factual Knowledge in Large Language Models [48.75961313441549]
  This paper proposes a framework to assess the factual knowledge of large language models (LLMs) by leveraging knowledge graphs (KGs) Our framework automatically generates a set of questions and expected answers from the facts stored in a given KG, and then evaluates the accuracy of LLMs in answering these questions.
  arXiv  Detail & Related papers  (2023-10-18T00:20:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.