Summary of Measuring Retrieval Complexity in Question Answering Systems, by Matteo Gabburo et al.
Measuring Retrieval Complexity in Question Answering Systems
by Matteo Gabburo, Nicolaas Paul Jedema, Siddhant Garg, Leonardo F. R. Ribeiro, Alessandro Moschitti
First submitted to arxiv on: 5 Jun 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 This paper investigates which types of questions are difficult for retrieval-based Question Answering (QA) systems to answer. The authors propose two main contributions: first, they develop a novel metric called Retrieval Complexity (RC), which measures the difficulty of answering questions based on how complete the retrieved documents are. Second, they design an unsupervised pipeline that accurately estimates RC scores given any retrieval system. Experimental results show that their proposed pipeline outperforms alternative estimators, including Large Language Models (LLMs), on six challenging QA benchmarks. The authors also find a strong correlation between RC scores and both QA performance and expert judgment across five of the six studied benchmarks. This suggests that RC is an effective measure of question difficulty. Furthermore, they categorize high-RC questions and show that these complex questions span a broad range of question shapes, including multi-hop, compositional, and temporal QA. Their system has the potential to significantly impact retrieval-based systems by helping identify more challenging questions on existing datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at what makes some questions hard for machines to answer. The authors create two new tools: one that measures how hard it is to find the right answers (called Retrieval Complexity, or RC) and another pipeline that uses these measurements to help computers understand which questions are tricky. They test their ideas on six big question-answering challenges and show that they work better than other ways of doing things. The authors also discover that RC scores are closely tied to how well a machine does at answering questions, as well as what experts think about the difficulty of those questions. This means that RC can help identify which questions are really hard for machines to answer. By looking at these difficult questions, the authors find that they come in many different forms, like ones that require jumping between ideas or understanding complex relationships. |
Keywords
» Artificial intelligence » Question answering » Unsupervised
