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What Disease Does This Patient Have? A Large-Scale Open Domain Question Answering Dataset from Medical Exams
Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. We implement both rule-based and popular neural methods by sequentially combining a document retriever and a machine comprehension model. Through experiments, we find that even the current best method can only achieve 36.7%, 42.0%, and 70.1% of test accuracy on the English, traditional Chinese, and simplified Chinese questions, respectively. We expect MedQA to present great challenges to existing OpenQA systems and hope that it can serve as a platform to promote much stronger OpenQA models from the NLP community in the future.
Journal Article
Chatting with ChatGPT : the collection 1
ChatGPT is an artificial intelligence (AI) chatbot, a software application that aims to mimic human conversation through text or voice interactions, launched as a prototype on November 30, 2022, garnering attention for its detailed responses and articulate answers across many domains of knowledge. ChatGPT can write and debug computer programs, mimic the style of celebrity CEOs and write business pitches, compose music, teleplays, fairy tales and student essays, answer test questions (sometimes, depending on the test, at a level above the average human test-taker), write poetry and song lyrics, translate and summarize text, emulate a Linux system; simulate entire chat rooms, play games like tic-tac-toe and simulate an ATM. This is a collection of questions and answers from ChatGPT.
Book
The state of the art in open domain complex question answering: a survey
Research on question answering (QA) systems has a long tradition. QA systems, as widely used systems in various applications, seek to find the answers to the given questions through the available resources. These systems are expected to be capable of answering various types of questions, including simple questions whose answers can be found in a single passage or sentence and complex questions which need more complicated reasoning to find the answer or their answer should be found by traversing several relations. Nowadays, answering complex questions from texts or structured data is a challenge in QA systems. In this paper, we have a comparative study on QA approaches and systems for answering complex questions. For this purpose, firstly, this paper discusses what a complex question is and surveys different types of constraints that may appear in complex questions. Furthermore, it addresses the challenges of these types of questions, the methods proposed to deal with them, and benchmark datasets used to evaluate their strengths and weaknesses.
Journal Article
RS-LLaVA: A Large Vision-Language Model for Joint Captioning and Question Answering in Remote Sensing Imagery
In this paper, we delve into the innovative application of large language models (LLMs) and their extension, large vision-language models (LVLMs), in the field of remote sensing (RS) image analysis. We particularly emphasize their multi-tasking potential with a focus on image captioning and visual question answering (VQA). In particular, we introduce an improved version of the Large Language and Vision Assistant Model (LLaVA), specifically adapted for RS imagery through a low-rank adaptation approach. To evaluate the model performance, we create the RS-instructions dataset, a comprehensive benchmark dataset that integrates four diverse single-task datasets related to captioning and VQA. The experimental results confirm the model’s effectiveness, marking a step forward toward the development of efficient multi-task models for RS image analysis.
Journal Article
Research on the construction and application of retrieval enhanced generation (RAG) model based on knowledge graph
Generative pre-trained language models have demonstrated strong capabilities in natural language processing tasks, but they still suffer from “fact hallucination” and weak knowledge timeliness in open-domain question answering and text generation. To improve the accuracy and knowledge consistency of generated content, this paper proposes a Knowledge Graph-based Retrieval Enhanced Generation Model (KG-RAG), which integrates structured knowledge graphs into traditional RAG architectures to enhance the model’s ability to understand semantic and inferential relationships. The model designs a dual-channel retrieval mechanism: on one hand, it uses Dense Passage Retrieval (DPR) for vectorized retrieval of unstructured texts; on the other hand, it employs graph neural networks (GNN) to structurally retrieve semantic paths within the knowledge graph, and through path attention mechanisms, it filters out the most relevant entity relationship chains to guide the knowledge injection module. Experimental results on the Natural Questions and PubMedQA datasets show that KG-RAG outperforms the original RAG model across multiple evaluation metrics. On the Natural Questions dataset, the ROUGE-L score of the KG-RAG model improves from 41.2 to 46.9, the BLEU score rises from 31.5 to 38.7, and the FactScore increases by 13.6%, significantly enhancing the knowledge consistency of the generated text. In the PubMedQA task, KG-RAG achieves an accuracy rate of 81% in medical question answering. 3%, an improvement of 6.8% points over RAG, demonstrates its advantage in knowledge reasoning within specialized fields. Furthermore, case studies show that KG-RAG can effectively integrate entity paths from knowledge graphs to generate more logical and factual answers in complex question-answering tasks. This method has broad application prospects in intelligent question-answering systems, multi-turn conversations, and educational Q&A scenarios. Future research will consider introducing dynamic knowledge update mechanisms and multimodal graph information to further enhance the capabilities and adaptability of KG-RAG in real-world tasks.
Journal Article
TASTA: Text‐Assisted Spatial and Temporal Attention Network for Video Question Answering
Video question answering (VideoQA) is a typical task that integrates language and vision. The key for VideoQA is to extract relevant and effective visual information for answering a specific question. Information selection is believed to be necessary for this task due to the large amount of irrelevant information in the video, and explicitly learning an attention model can be a reasonable and effective solution for the selection. Herein, a novel VideoQA model called Text‐Assisted Spatial and Temporal Attention Network (TASTA) is proposed, which shows the great potential of explicitly modeling attention. TASTA is made to be simple, small, clean, and efficient for clear performance justification and possible easy extension. Its success is mainly from two new strategies of better using the textual information. Experimental results on a large and most representative dataset, TGIF‐QA, show the significant superiority of TASTA w.r.t. the state‐of‐the‐art and demonstrate the effectiveness of its key components via ablation studies. This study proposes Text‐Assisted Spatial and Temporal Attention Network (TASTA) for video question answering. Carefully designed textual guidance from question–answer pairs and attentively fusing of spatial and temporal information contribute to the outstanding performance. Model effectivity and generality are verified by ablation studies. Extensive use in intelligent manufacturing for querying system status in natural language based on videos is promising.
Journal Article
CoQUAD: a COVID-19 question answering dataset system, facilitating research, benchmarking, and practice
Background
Due to the growing amount of COVID-19 research literature, medical experts, clinical scientists, and researchers frequently struggle to stay up to date on the most recent findings. There is a pressing need to assist researchers and practitioners in mining and responding to COVID-19-related questions on time.
Methods
This paper introduces CoQUAD, a question-answering system that can extract answers related to COVID-19 questions in an efficient manner. There are two datasets provided in this work: a reference-standard dataset built using the CORD-19 and LitCOVID initiatives, and a gold-standard dataset prepared by the experts from a public health domain. The CoQUAD has a Retriever component trained on the BM25 algorithm that searches the reference-standard dataset for relevant documents based on a question related to COVID-19. CoQUAD also has a Reader component that consists of a Transformer-based model, namely MPNet, which is used to read the paragraphs and find the answers related to a question from the retrieved documents. In comparison to previous works, the proposed CoQUAD system can answer questions related to early, mid, and post-COVID-19 topics.
Results
Extensive experiments on CoQUAD Retriever and Reader modules show that CoQUAD can provide effective and relevant answers to any COVID-19-related questions posed in natural language, with a higher level of accuracy. When compared to state-of-the-art baselines, CoQUAD outperforms the previous models, achieving an exact match ratio score of 77.50% and an F1 score of 77.10%.
Conclusion
CoQUAD is a question-answering system that mines COVID-19 literature using natural language processing techniques to help the research community find the most recent findings and answer any related questions.
Journal Article
Towards End-to-End Multilingual Question Answering
Multilingual question answering (MLQA) is a critical part of an accessible natural language interface. However, current solutions demonstrate performance far below that of monolingual systems. We believe that deep learning approaches are likely to improve performance in MLQA drastically. This work aims to discuss the current state-of-the-art and remaining challenges. We outline requirements and suggestions for practical parallel data collection and describe existing methods, benchmarks and datasets. We also demonstrate that a simple translation of texts can be inadequate in case of Arabic, English and German languages (on InsuranceQA and SemEval datasets), and thus more sophisticated models are required. We hope that our overview will re-ignite interest in multilingual question answering, especially with regard to neural approaches.
Journal Article