Research on Automatic Question Answering System

WILO-BiLSTM: Optimization Based Bidirectional Long Short-Term Memory Model for Intelligent Virtual Assistant System

The proposed WILO-BiLSTM model can perform superior to the conventional approaches and its performance results in terms of METEOR, BLEU, ROUGE, and SPICE score at training data 90% is 0.28, 0.50, 0.56, and 26.93 for the SquAD dataset, respectively.

Large Language Model Technologies: Progress, Problems and Prospects

Large language models (LLMs) are built on the classic Transformer architecture and have become a core driving force for the rapid development of modern artificial intelligence. This paper presents a systematic review of LLMs, elaborating on their fundamental working principles, mainstream open-source models, effective lightweight optimization methods, retrieval-augmented generation frameworks and key human-value-aligned technologies. Nowadays, LLMs have been widely applied in practice. Typical scenarios include intelligent text generation, professional knowledge-based question answering and automated code generation, delivering remarkable value to both industries and academia. However, their large-scale industrial application is still restricted by multiple challenges. The major issues involve content hallucination, poor model interpretability, excessive computing resource consumption, potential ethical risks and unsatisfactory multimodal integration capability. This paper also forecasts the future development directions of LLMs, such as lightweight deployment on edge devices, safety-focused human value alignment, in-depth cross-modal fusion and customized large models for vertical industries. Additionally, it collects a number of representative cases, which can offer solid references and practical guidance for relevant researchers and engineering practitioners to carry out further studies.

Restrictive Training (QuAART): Question Answering for the Rapid Development of New Knowledge Extraction Pipelines

This work hypothesizes that using models trained only on generic question answering data (e.g. SQuAD) is a good starting point for domain specific entity extraction, and explores whether the addition of small amounts of training data can help lift model performance.

Intelligent Prompt Construction for Large Language Models in Knowledge-based Visual Question Answering

Large Language Models (LLMs) have demonstrated strong capabilities in knowledge-based Visual Question Answering (VQA). However, existing prompt construction methods are often rigid and fail to fully exploit the reasoning potential of LLMs. To address this limitation, we propose the Intelligent Prompt Construction Framework (IPCF), which equips an autonomous agent with the ability to dynamically generate task-specific prompts. IPCF consists of a planner and a toolbox: the planner, powered by an LLM, enables autonomous decision-making, while the toolbox provides three tools—the vanilla VQA model for inspiration, the LLM for knowledge injection, and a knowledge base for information retrieval. This architecture allows the agent to flexibly determine when and how to invoke each tool and to construct adaptive prompts accordingly. Experimental results show that IPCF achieves performance gains of 2.6 and 1.9 points over existing baselines on the OK-VQA and A-OKVQA datasets, respectively.

AutoDavis: Automatic and Dynamic Evaluation Protocol of Large Vision-Language Models on Visual Question-Answering

AutoDavis is introduced, a first-of-its-kind automatic and dynamic evaluation protocol that enables on-demand benchmarking of LVLMs across specific capability dimensions and shows effectiveness and reliability, offering a new paradigm for dynamic benchmarking of multimodal intelligence.

Generating then Refining for Reliable Knowledge Base Question Answering