Know What You Don't Know: Unanswerable Questions for SQuAD
원문 초록
Extractive reading comprehension systems can often locate the correct answer to a question in a context document, but they also tend to make unreliable guesses on questions for which the correct answer is not stated in the context. Existing datasets either focus exclusively on answerable questions, or use automatically generated unanswerable questions that are easy to identify. To address these weaknesses, we present SQuAD 2.0, the latest version of the Stanford Question Answering Dataset (SQuAD). SQuAD 2.0 combines existing SQuAD data with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD 2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering. SQuAD 2.0 is a challenging natural language understanding task for existing models: a strong neural system that gets 86% F1 on SQuAD 1.1 achieves only 66% F1 on SQuAD 2.0.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
LogiQA 2.0—An Improved Dataset for Logical Reasoning in Natural Language Understanding
Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models
Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities
SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models
Semantic similarity estimation for domain specific data using BERT and other techniques
From Roots to Rewards: Dynamic Tree Reasoning with RL
토론
이 논문과 연결된 포럼 글이 아직 없습니다.