KaPQA: Knowledge-Augmented Product Question-Answering

• URL: http://arxiv.org/abs/2407.16073v1
• Date: Mon, 22 Jul 2024 22:14:56 GMT
• Title: KaPQA: Knowledge-Augmented Product Question-Answering
• Authors: Swetha Eppalapally, Daksh Dangi, Chaithra Bhat, Ankita Gupta, Ruiyi Zhang, Shubham Agarwal, Karishma Bagga, Seunghyun Yoon, Nedim Lipka, Ryan A. Rossi, Franck Dernoncourt,
• Abstract summary: We introduce two product question-answering (QA) datasets focused on Adobe Acrobat and Photoshop products. We also propose a novel knowledge-driven RAG-QA framework to enhance the performance of the models in the product QA task.
• Score: 59.096607961704656
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Question-answering for domain-specific applications has recently attracted much interest due to the latest advancements in large language models (LLMs). However, accurately assessing the performance of these applications remains a challenge, mainly due to the lack of suitable benchmarks that effectively simulate real-world scenarios. To address this challenge, we introduce two product question-answering (QA) datasets focused on Adobe Acrobat and Photoshop products to help evaluate the performance of existing models on domain-specific product QA tasks. Additionally, we propose a novel knowledge-driven RAG-QA framework to enhance the performance of the models in the product QA task. Our experiments demonstrated that inducing domain knowledge through query reformulation allowed for increased retrieval and generative performance when compared to standard RAG-QA methods. This improvement, however, is slight, and thus illustrates the challenge posed by the datasets introduced.

Related papers

• Leveraging the Domain Adaptation of Retrieval Augmented Generation Models for Question Answering and Reducing Hallucination [3.879723687090678]
  Retrieval Augmented Generation Model stands out to be highly effective on downstream applications like Question Answering. Recently, RAG-end2end model further optimized the architecture and achieved notable performance improvements on domain adaptation. In this paper, we investigated the performance of diverse RAG and RAG-like architectures through domain adaptation.
  arXiv  Detail & Related papers  (2024-10-23T11:32:46Z)
• DEXTER: A Benchmark for open-domain Complex Question Answering using LLMs [3.24692739098077]
  Open-domain complex Question Answering (QA) is a difficult task with challenges in evidence retrieval and reasoning. We evaluate state-of-the-art pre-trained dense and sparse retrieval models in an open-domain setting. We observe that late interaction models and surprisingly lexical models like BM25 perform well compared to other pre-trained dense retrieval models.
  arXiv  Detail & Related papers  (2024-06-24T22:09:50Z)
• Towards Better Question Generation in QA-based Event Extraction [3.699715556687871]
  Event Extraction (EE) aims to extract event-related information from unstructured texts. The quality of the questions dramatically affects the extraction accuracy. We propose a reinforcement learning method, RLQG, for QA-based EE.
  arXiv  Detail & Related papers  (2024-05-17T03:52:01Z)
• HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA [13.000411428297813]
  We present HiQA, an advanced multi-document question-answering (MDQA) framework that integrates cascading metadata into content and a multi-route retrieval mechanism. We also release a benchmark called MasQA to evaluate and research in MDQA.
  arXiv  Detail & Related papers  (2024-02-01T02:24:15Z)
• 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)
• QontSum: On Contrasting Salient Content for Query-focused Summarization [22.738731393540633]
  Query-focused summarization (QFS) is a challenging task in natural language processing that generates summaries to address specific queries. This paper highlights the role of QFS in Grounded Answer Generation (GAR) We propose QontSum, a novel approach for QFS that leverages contrastive learning to help the model attend to the most relevant regions of the input document.
  arXiv  Detail & Related papers  (2023-07-14T19:25:35Z)
• 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)
• Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs [62.71505254770827]
  We propose a conditional variational autoencoder (HCVAE) for generating QA pairs given unstructured texts as contexts. Our model obtains impressive performance gains over all baselines on both tasks, using only a fraction of data for training.
  arXiv  Detail & Related papers  (2020-05-28T08:26:06Z)
• Harvesting and Refining Question-Answer Pairs for Unsupervised QA [95.9105154311491]
  We introduce two approaches to improve unsupervised Question Answering (QA) First, we harvest lexically and syntactically divergent questions from Wikipedia to automatically construct a corpus of question-answer pairs (named as RefQA) Second, we take advantage of the QA model to extract more appropriate answers, which iteratively refines data over RefQA.
  arXiv  Detail & Related papers  (2020-05-06T15:56:06Z)
• Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering [98.48363619128108]
  We propose an unsupervised approach to training QA models with generated pseudo-training data. We show that generating questions for QA training by applying a simple template on a related, retrieved sentence rather than the original context sentence improves downstream QA performance.
  arXiv  Detail & Related papers  (2020-04-24T17:57:45Z)

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.