Answer Generation for Retrieval-based Question Answering Systems
Recent advancements in transformer-based models have greatly improved the ability of Question Answering (QA) systems to provide correct answers; in particular, answer sentence selection (AS2) models, core components of retrieval-based systems, have achieved impressive results. While generally effective, these models fail to provide a satisfying answer when all retrieved candidates are of poor quality, even if they contain correct information. In AS2, models are trained to select the best answer sentence among a set of candidates retrieved for a given question. In this work, we propose to generate answers from a set of AS2 top candidates. Rather than selecting the best candidate, we train a sequence to sequence transformer model to generate an answer from a candidate set. Our tests on three English AS2 datasets show improvement up to 32 absolute points in accuracy over the state of the art.
Code (0)
등록된 구현이 없습니다.
Tasks
Answer GenerationQuestion AnsweringRetrievalSentenceSimilar Papers 제목 키워드 기반
Assessing the Robustness of Retrieval-Augmented Generation Systems in K-12 Educational Question Answering with Knowledge Discrepancies
Retrieval-Augmented Generation (RAG) systems have demonstrated remarkable potential as question answering systems in the K-12 Education domain, where knowledge is typically queried within the restricted scope of authorit…
Question AnsweringRAGRetrievalRetrieval-augmented GenerationRetrieval Augmented Visual Question Answering with Outside Knowledge
Outside-Knowledge Visual Question Answering (OK-VQA) is a challenging VQA task that requires retrieval of external knowledge to answer questions about images. Recent OK-VQA systems use Dense Passage Retrieval (DPR) to re…
Answer GenerationDiagnosticPassage RetrievalQuestion Answering+3AccurateRAG: A Framework for Building Accurate Retrieval-Augmented Question-Answering Applications
We introduce AccurateRAG -- a novel framework for constructing high-performance question-answering applications based on retrieval-augmented generation (RAG). Our framework offers a pipeline for development efficiency wi…
RAG-BioQA: A Retrieval-Augmented Generation Framework for Long-Form Biomedical Question Answering
The rapidly growth of biomedical literature creates challenges acquiring specific medical information. Current biomedical question-answering systems primarily focus on short-form answers, failing to provide comprehensive…
Question AnsweringAnswer GenerationRetrieval Augmented Generation Framework for the Nepali Legal Domain Question Answering
Legal domains in high-resource languages like English have widely adopted artificial intelligence for legal question answering. However, data scarcity in low resource languages such as Nepali has limited the training of …
Question AnsweringAnswer Generation