This paper reviews the state-of-the-art of language models architectures and strategies for"
complex" question-answering (QA, CQA, CPS) with a focus on hybridization. Large …

Transformer encoders contextualize token representations by attending to all other tokens at
each layer, leading to quadratic increase in compute effort with the input length. In practice …

Retrieval-enhanced machine learning (REML) refers to the use of information retrieval
methods to support reasoning and inference in machine learning tasks. Although relatively …

Large language models record impressive performance on many natural language
processing tasks. However, their knowledge capacity is limited to the pretraining corpus …

Large Language Models (LLMs) have demonstrated superior results across a wide range of
tasks, while retrieval has long been established as an effective means of obtaining task …

Human memory is inherently prone to forgetting. To address this, multimodal embedding
models have been introduced, which transform diverse real-world data into a unified …

요 약오픈 도메인 질의응답 (Open-domain Question Answering) 은 주어진 질문에 대해 방대한
지식 소스에서 정확한 답변을추출하는 과제이다. 기존 검색 증강 언어 모델 (Retrieval …