Recent Advances in Multi-Choice Machine Reading Comprehension: A Survey on Methods and Datasets

• URL: http://arxiv.org/abs/2408.02114v1
• Date: Sun, 4 Aug 2024 18:57:21 GMT
• Title: Recent Advances in Multi-Choice Machine Reading Comprehension: A Survey on Methods and Datasets
• Authors: Shima Foolad, Kourosh Kiani, Razieh Rastgoo,
• Abstract summary: The analysis delves into 30 existing cloze-style and multiple-choice MRC benchmark datasets. The paper categorizes recent methodologies into Fine-tuned and Prompt-tuned methods.
• Score: 19.021200954913482
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper provides a thorough examination of recent developments in the field of multi-choice Machine Reading Comprehension (MRC). Focused on benchmark datasets, methodologies, challenges, and future trajectories, our goal is to offer researchers a comprehensive overview of the current landscape in multi-choice MRC. The analysis delves into 30 existing cloze-style and multiple-choice MRC benchmark datasets, employing a refined classification method based on attributes such as corpus style, domain, complexity, context style, question style, and answer style. This classification system enhances our understanding of each dataset's diverse attributes and categorizes them based on their complexity. Furthermore, the paper categorizes recent methodologies into Fine-tuned and Prompt-tuned methods. Fine-tuned methods involve adapting pre-trained language models (PLMs) to a specific task through retraining on domain-specific datasets, while prompt-tuned methods use prompts to guide PLM response generation, presenting potential applications in zero-shot or few-shot learning scenarios. By contributing to ongoing discussions, inspiring future research directions, and fostering innovations, this paper aims to propel multi-choice MRC towards new frontiers of achievement.

Related papers

• A Survey of Small Language Models [104.80308007044634]
  Small Language Models (SLMs) have become increasingly important due to their efficiency and performance to perform various language tasks with minimal computational resources. We present a comprehensive survey on SLMs, focusing on their architectures, training techniques, and model compression techniques.
  arXiv  Detail & Related papers  (2024-10-25T23:52:28Z)
• Are Large Language Models Good Classifiers? A Study on Edit Intent Classification in Scientific Document Revisions [62.12545440385489]
  Large language models (LLMs) have brought substantial advancements in text generation, but their potential for enhancing classification tasks remains underexplored. We propose a framework for thoroughly investigating fine-tuning LLMs for classification, including both generation- and encoding-based approaches. We instantiate this framework in edit intent classification (EIC), a challenging and underexplored classification task.
  arXiv  Detail & Related papers  (2024-10-02T20:48:28Z)
• Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models [33.488331159912136]
  Instruction tuning plays a critical role in aligning large language models (LLMs) with human preference. Data assessment and selection methods have been proposed in the fields of natural language processing (NLP) and deep learning. We present a comprehensive review on existing literature of data assessment and selection especially for instruction tuning of LLMs.
  arXiv  Detail & Related papers  (2024-08-04T16:50:07Z)
• Towards Robust Evaluation: A Comprehensive Taxonomy of Datasets and Metrics for Open Domain Question Answering in the Era of Large Language Models [0.0]
  Open Domain Question Answering (ODQA) within natural language processing involves building systems that answer factual questions using large-scale knowledge corpora. High-quality datasets are used to train models on realistic scenarios. Standardized metrics facilitate comparisons between different ODQA systems.
  arXiv  Detail & Related papers  (2024-06-19T05:43:02Z)
• Unsupervised Multimodal Clustering for Semantics Discovery in Multimodal Utterances [24.142013877384603]
  This paper introduces a novel unsupervised multimodal clustering method (UMC), making a pioneering contribution to this field. UMC introduces a unique approach to constructing augmentation views for multimodal data, which are then used to perform pre-training. We show remarkable improvements of 2-6% scores in clustering metrics over state-of-the-art methods, marking the first successful endeavor in this domain.
  arXiv  Detail & Related papers  (2024-05-21T13:24:07Z)
• A Language Model based Framework for New Concept Placement in Ontologies [33.31096456574208]
  We investigate the task of inserting new concepts extracted from texts into an ontology using language models. We propose to leverage neural methods, where we apply embedding-based methods and contrastive learning. We evaluate the methods on recent datasets created using the SNOMED CT and the MedMentions entity linking benchmark.
  arXiv  Detail & Related papers  (2024-02-27T21:27:35Z)
• Exploring Precision and Recall to assess the quality and diversity of LLMs [82.21278402856079]
  We introduce a novel evaluation framework for Large Language Models (LLMs) such as textscLlama-2 and textscMistral. This approach allows for a nuanced assessment of the quality and diversity of generated text without the need for aligned corpora.
  arXiv  Detail & Related papers  (2024-02-16T13:53:26Z)
• Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison [54.357707168883024]
  Few-Shot Object Detection (FSOD) mimics the humans' ability of learning to learn. FSOD intelligently transfers the learned generic object knowledge from the common heavy-tailed, to the novel long-tailed object classes. We give an overview of FSOD, including the problem definition, common datasets, and evaluation protocols.
  arXiv  Detail & Related papers  (2022-03-27T04:11:28Z)
• Deep Learning Schema-based Event Extraction: Literature Review and Current Trends [60.29289298349322]
  Event extraction technology based on deep learning has become a research hotspot. This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models.
  arXiv  Detail & Related papers  (2021-07-05T16:32:45Z)
• Multitask Learning for Class-Imbalanced Discourse Classification [74.41900374452472]
  We show that a multitask approach can improve 7% Micro F1-score upon current state-of-the-art benchmarks. We also offer a comparative review of additional techniques proposed to address resource-poor problems in NLP.
  arXiv  Detail & Related papers  (2021-01-02T07:13:41Z)
• Multi-document Summarization via Deep Learning Techniques: A Survey [29.431160110691607]
  We propose a novel taxonomy to summarize the design strategies of neural networks. We highlight the differences between various objective functions that are rarely discussed in the existing literature.
  arXiv  Detail & Related papers  (2020-11-10T00:35:46Z)

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.