MetaQA: Combining Expert Agents for Multi-Skill Question Answering

• URL: http://arxiv.org/abs/2112.01922v1
• Date: Fri, 3 Dec 2021 14:05:52 GMT
• Title: MetaQA: Combining Expert Agents for Multi-Skill Question Answering
• Authors: Haritz Puerto, G\"ozde G\"ul \c{S}ahin, Iryna Gurevych
• Abstract summary: We argue that despite the promising results of multi-dataset models, some domains or QA formats might require specific architectures. We propose to combine expert agents with a novel, flexible, and training-efficient architecture that considers questions, answer predictions, and answer-prediction confidence scores.
• Score: 49.35261724460689
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The recent explosion of question answering (QA) datasets and models has increased the interest in the generalization of models across multiple domains and formats by either training models on multiple datasets or by combining multiple models. We argue that despite the promising results of multi-dataset models, some domains or QA formats may require specific architectures, and thus the adaptability of these models might be limited. In addition, current approaches for combining models disregard cues such as question-answer compatibility. In this work, we propose to combine expert agents with a novel, flexible, and training-efficient architecture that considers questions, answer predictions, and answer-prediction confidence scores to select the best answer among a list of answer candidates. Through quantitative and qualitative experiments we show that our model i) creates a collaboration between agents that outperforms previous multi-agent and multi-dataset approaches in both in-domain and out-of-domain scenarios, ii) is extremely data-efficient to train, and iii) can be adapted to any QA format.

Related papers

• UNK-VQA: A Dataset and a Probe into the Abstention Ability of Multi-modal Large Models [55.22048505787125]
  This paper contributes a comprehensive dataset, called UNK-VQA. We first augment the existing data via deliberate perturbations on either the image or question. We then extensively evaluate the zero- and few-shot performance of several emerging multi-modal large models.
  arXiv  Detail & Related papers  (2023-10-17T02:38:09Z)
• Towards Robust Multi-Modal Reasoning via Model Selection [7.6621866737827045]
  LLM serves as the "brain" of the agent, orchestrating multiple tools for collaborative multi-step task solving. We propose the $textitM3$ framework as a plug-in with negligible runtime overhead at test-time. Our experiments reveal that our framework enables dynamic model selection, considering both user inputs and subtask dependencies.
  arXiv  Detail & Related papers  (2023-10-12T16:06:18Z)
• An Empirical Comparison of LM-based Question and Answer Generation Methods [79.31199020420827]
  Question and answer generation (QAG) consists of generating a set of question-answer pairs given a context. In this paper, we establish baselines with three different QAG methodologies that leverage sequence-to-sequence language model (LM) fine-tuning. Experiments show that an end-to-end QAG model, which is computationally light at both training and inference times, is generally robust and outperforms other more convoluted approaches.
  arXiv  Detail & Related papers  (2023-05-26T14:59:53Z)
• Chain-of-Skills: A Configurable Model for Open-domain Question Answering [79.8644260578301]
  The retrieval model is an indispensable component for real-world knowledge-intensive tasks. Recent work focuses on customized methods, limiting the model transferability and scalability. We propose a modular retriever where individual modules correspond to key skills that can be reused across datasets.
  arXiv  Detail & Related papers  (2023-05-04T20:19:39Z)
• UKP-SQuARE v3: A Platform for Multi-Agent QA Research [48.92308487624824]
  We extend UKP-SQuARE, an online platform for Question Answering (QA) research, to support three families of multi-agent systems. We conduct experiments to evaluate their inference speed and discuss the performance vs. speed trade-off compared to multi-dataset models.
  arXiv  Detail & Related papers  (2023-03-31T15:07:36Z)
• ManyModalQA: Modality Disambiguation and QA over Diverse Inputs [73.93607719921945]
  We present a new multimodal question answering challenge, ManyModalQA, in which an agent must answer a question by considering three distinct modalities. We collect our data by scraping Wikipedia and then utilize crowdsourcing to collect question-answer pairs.
  arXiv  Detail & Related papers  (2020-01-22T14:39:28Z)

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.