Summary of Towards Robust Evaluation: a Comprehensive Taxonomy Of Datasets and Metrics For Open Domain Question Answering in the Era Of Large Language Models, by Akchay Srivastava et al.
Towards Robust Evaluation: A Comprehensive Taxonomy of Datasets and Metrics for Open Domain Question Answering in the Era of Large Language Models
by Akchay Srivastava, Atif Memon
First submitted to arxiv on: 19 Jun 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 The proposed study examines the current landscape of Open Domain Question Answering (ODQA) benchmarking, reviewing 52 datasets and 20 evaluation techniques across textual and multimodal modalities. The researchers introduce a novel taxonomy for ODQA datasets that incorporates both modality and difficulty of question types, and present a structured organization of ODQA evaluation metrics along with a critical analysis of their inherent trade-offs. This study aims to empower researchers by providing a framework for the robust evaluation of modern question-answering systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Open Domain Question Answering is a type of natural language processing that involves building systems that answer factual questions using large-scale knowledge corpora. Recent advances have been made possible by the combination of large-scale training datasets, deep learning techniques, and the rise of large language models. The study presents an overview of the current state of ODQA benchmarking, highlighting the importance of standardized metrics for comparing different systems and tracking advancements in the field. |
Keywords
» Artificial intelligence » Deep learning » Natural language processing » Question answering » Tracking
