Papers Question-Answer-Generation
“Question-Answer-Generation” 태그가 달린 논문 37편 · 필터 해제
FinLMM-R1: Enhancing Financial Reasoning in LMM through Scalable Data and Reward Design
Large Multimodal Models (LMMs) demonstrate significant cross-modal reasoning capabilities. However, financial applications face challenges due to the lack of high-quality multimodal reasoning datasets and the inefficienc…
Answer GenerationArithmetic ReasoningMultimodal ReasoningQuestion-Answer-Generation+1KG-QAGen: A Knowledge-Graph-Based Framework for Systematic Question Generation and Long-Context LLM Evaluation
The increasing context length of modern language models has created a need for evaluating their ability to retrieve and process information across extensive documents. While existing benchmarks test long-context capabili…
Answer GenerationImplicit RelationsQuestion-Answer-GenerationQuestion Generation+1MoEMoE: Question Guided Dense and Scalable Sparse Mixture-of-Expert for Multi-source Multi-modal Answering
Question Answering (QA) and Visual Question Answering (VQA) are well-studied problems in the language and vision domain. One challenging scenario involves multiple sources of information, each of a different modality, wh…
Answer GenerationMixture-of-ExpertsQuestion-Answer-GenerationQuestion Answering+2QA-Expand: Multi-Question Answer Generation for Enhanced Query Expansion in Information Retrieval
Query expansion is widely used in Information Retrieval (IR) to improve search outcomes by enriching queries with additional contextual information. Although recent Large Language Model (LLM) based methods generate pseud…
Answer GenerationInformation RetrievalLanguage ModelingLanguage Modelling+3HMGIE: Hierarchical and Multi-Grained Inconsistency Evaluation for Vision-Language Data Cleansing
Visual-textual inconsistency (VTI) evaluation plays a crucial role in cleansing vision-language data. Its main challenges stem from the high variety of image captioning datasets, where differences in content can create a…
Answer GenerationGraph GenerationImage CaptioningQuestion-Answer-GenerationWeQA: A Benchmark for Retrieval Augmented Generation in Wind Energy Domain
In the rapidly evolving landscape of Natural Language Processing (NLP) and text generation, the emergence of Retrieval Augmented Generation (RAG) presents a promising avenue for improving the quality and reliability of g…
Answer GenerationBenchmarkingLanguage ModelingLanguage Modelling+6Towardseffective teaching assistants: From intent-based chatbots to LLM-poweredteachingassistants
As chatbot technology undergoes a transformative phase in the era of artificial intelligence (AI), the integration of advanced AI models emerges as a focal point for reshaping conversational agents within the education s…
Answer GenerationChatbotLarge Language ModelQuestion-Answer-Generation+1LLaVA-Surg: Towards Multimodal Surgical Assistant via Structured Surgical Video Learning
Multimodal large language models (LLMs) have achieved notable success across various domains, while research in the medical field has largely focused on unimodal images. Meanwhile, current general-domain multimodal model…
Answer GenerationQuestion-Answer-GenerationQuestion AnsweringVideo Question AnsweringQAEA-DR: A Unified Text Augmentation Framework for Dense Retrieval
In dense retrieval, embedding long texts into dense vectors can result in information loss, leading to inaccurate query-text matching. Additionally, low-quality texts with excessive noise or sparse key information are un…
Answer GenerationEvent ExtractionQuestion-Answer-GenerationRetrieval+5Explicit Diversity Conditions for Effective Question Answer Generation with Large Language Models
Question Answer Generation (QAG) is an effective data augmentation technique to improve the accuracy of question answering systems, especially in low-resource domains. While recent pretrained and large language model-bas…
Answer GenerationData AugmentationDiversityLanguage Modeling+4On Few-Shot Prompting for Controllable Question-Answer Generation in Narrative Comprehension
Question Generation aims to automatically generate questions based on a given input provided as context. A controllable question generation scheme focuses on generating questions with specific attributes, allowing better…
Answer GenerationDiversityQuestion-Answer-GenerationQuestion Generation+1Automatic Question-Answer Generation for Long-Tail Knowledge
Pretrained Large Language Models (LLMs) have gained significant attention for addressing open-domain Question Answering (QA). While they exhibit high accuracy in answering questions related to common knowledge, LLMs enco…
Answer GenerationKnowledge GraphsOpen-Domain Question AnsweringQuestion-Answer-Generation+1Graph Guided Question Answer Generation for Procedural Question-Answering
In this paper, we focus on task-specific question answering (QA). To this end, we introduce a method for generating exhaustive and high-quality training data, which allows us to train compact (e.g., run on a mobile devic…
Answer GenerationQuestion-Answer-GenerationQuestion AnsweringA Comparative and Experimental Study on Automatic Question Answering Systems and its Robustness against Word Jumbling
Question answer generation using Natural Language Processing models is ubiquitous in the world around us. It is used in many use cases such as the building of chat bots, suggestive prompts in google search and also as a …
Answer GenerationQuestion-Answer-GenerationQuestion AnsweringDCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding
Visually-situated languages such as charts and plots are omnipresent in real-world documents. These graphical depictions are human-readable and are often analyzed in visually-rich documents to address a variety of questi…
Answer GenerationChart Question AnsweringCommon Sense ReasoningDocument Layout Analysis+3Weakly Supervised Visual Question Answer Generation
Growing interest in conversational agents promote twoway human-computer communications involving asking and answering visual questions have become an active area of research in AI. Thus, generation of visual questionansw…
Answer GenerationDependency ParsingQuestion-Answer-GenerationQuestion Generation+2Information Association for Language Model Updating by Mitigating LM-Logical Discrepancy
Large Language Models~(LLMs) struggle with providing current information due to the outdated pre-training data. Existing methods for updating LLMs, such as knowledge editing and continual fine-tuning, have significant dr…
Answer GenerationArticlesknowledge editingLanguage Modeling+3Combining Data Generation and Active Learning for Low-Resource Question Answering
Neural approaches have become very popular in Question Answering (QA), however, they require a large amount of annotated data. In this work, we propose a novel approach that combines data augmentation via question-answer…
Active LearningAnswer GenerationData AugmentationQuestion-Answer-Generation+2Multi-Type Conversational Question-Answer Generation with Closed-ended and Unanswerable Questions
Conversational question answering (CQA) facilitates an incremental and interactive understanding of a given context, but building a CQA system is difficult for many domains due to the problem of data scarcity. In this pa…
Answer GenerationConversational Question AnsweringQuestion-Answer-GenerationQuestion AnsweringType-dependent Prompt CycleQAG : Cycle Consistency for Multi-hop Question Generation
Multi-hop question generation (QG) is the process of generating answer related questions, which requires aggregating multiple pieces of information and reasoning from different parts of the texts. This is opposed to sing…
Answer GenerationLogical ReasoningQuestion-Answer-GenerationQuestion Answering+3