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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
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
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
A comprehensive survey of techniques for developing an Arabic question answering system
The question-answering system (QAS) aims to produce a response to a query using information from a text corpus. Arabic is a complex language. However, it has more than 450 million native speakers across the globe. The Saudi Arabian government encourages organizations to automate their routine activities to provide adequate services to their stakeholders. The performance of current Arabic QASs is limited to the specific domain. An effective QAS retrieves relevant responses from structured and unstructured data based on the user query. Many QAS studies categorized QASs according to factors, including user queries, dataset characteristics, and the nature of the responses. A more comprehensive examination of QASs is required to improve the QAS development according to the present QAS requirements. The current literature presents the features and classifications of the Arabic QAS. There is a lack of studies to report the techniques of Arabic QAS development. Thus, this study suggests a systematic literature review of strategies for developing Arabic QAS. A total of 617 articles were collected, and 40 papers were included in the proposed review. The outcome reveals the importance of the dataset and the deep learning techniques used to improve the performance of the QAS. The existing systems depend on supervised learning methods that lower QAS performance. In addition, the recent development of machine learning techniques encourages researchers to develop unsupervised QAS.
Journal Article
Educational QA System-Oriented Answer Selection Model Based on Focus Fusion of Multi-Perspective Word Matching
Question-answering systems have become an important tool for learning and knowledge acquisition. However, current answer selection models often rely on representing features using whole sentences, which leads to neglecting individual words and losing important information. To address this challenge, the paper proposes a novel answer selection model based on focus fusion of multi-perspective word matching. First, according to the different combination relationships between sentences, focus distribution in terms of words is obtained from the matching perspectives of serial, parallel, and transfer. Then, the sentence’s key position information is inferred from its focus distribution. Finally, a method of aligning key information points is designed to fuse the focus distribution for each perspective, which obtains match scores for each candidate answer to the question. Experimental results show that the proposed model significantly outperforms the Transformer encoder fine-tuned model based on contextual embedding, achieving a 4.07% and 5.51% increase in MAP and a 1.63% and 4.86% increase in MRR, respectively.
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
Survey on Answer Validation for Indonesian Question Answering System (IQAS)
Research on Question Answering System (QAS) has been done mainly in English. Unfortunately, for Indonesian, it is still rarely explored whereas Indonesian is the official language used more than 250 million people. Research in the area of Indonesian Question Answering System (IQAS) began in 2005s, and since then only few number of IQAS have been developed. One of the important issues in IQAS is Answer Validation (AV), which is a system that can determine the correctness of QAS. To identify the future scope of research in this area, the need of comprehensive survey on IQAS and AV arises naturally. The goals of this survey are to find the cutting-edge method used in AV and to prove that AV has not been implemented on IQAS. Based on the results, we suggest new opportunities and research challenges for IQAS community.
Journal Article
Towards a neuroscience of active sampling and curiosity
In natural behaviour, animals actively interrogate their environments using endogenously generated ‘question-and-answer’ strategies. However, in laboratory settings participants typically engage with externally imposed stimuli and tasks, and the mechanisms of active sampling remain poorly understood. We review a nascent neuroscientific literature that examines active-sampling policies and their relation to attention and curiosity. We distinguish between information sampling, in which organisms reduce uncertainty relevant to a familiar task, and information search, in which they investigate in an open-ended fashion to discover new tasks. We review evidence that both sampling and search depend on individual preferences over cognitive states, including attitudes towards uncertainty, learning progress and types of information. We propose that, although these preferences are non-instrumental and can on occasion interfere with external goals, they are important heuristics that allow organisms to cope with the high complexity of both sampling and search, and generate curiosity-driven investigations in large, open environments in which rewards are sparse and ex ante unknown.
Journal Article