https://github.com/cvdigitalai/katecheo
공식 코드Katecheo: A Portable and Modular System for Multi-Topic Question Answering
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Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.
( Image credit: SQuAD )
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Katecheo: A Portable and Modular System for Multi-Topic Question Answering
Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation
ViperGPT: Visual Inference via Python Execution for Reasoning
Uncertainty-Aware Text-to-Program for Question Answering on Structured Electronic Health Records
CS1QA: A Dataset for Assisting Code-based Question Answering in an Introductory Programming Course
An Empirical Study of Data Ability Boundary in LLMs' Math Reasoning
Temporal Knowledge Question Answering via Abstract Reasoning Induction
An Adaptive Framework for Generating Systematic Explanatory Answer in Online Q&A Platforms
ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering
Semantic Parsing with Candidate Expressions for Knowledge Base Question Answering
Neural Multi-Step Reasoning for Question Answering on Semi-Structured Tables
VoxelPrompt: A Vision-Language Agent for Grounded Medical Image Analysis
CLEVR-Math: A Dataset for Compositional Language, Visual and Mathematical Reasoning
DocVXQA: Context-Aware Visual Explanations for Document Question Answering
Hire Me or Not? Examining Language Model's Behavior with Occupation Attributes
Unlocking Temporal Question Answering for Large Language Models with Tailor-Made Reasoning Logic
Towards Benchmarking and Improving the Temporal Reasoning Capability of Large Language Models
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs
DUAL: Discrete Spoken Unit Adaptive Learning for Textless Spoken Question Answering
Transition-based Semantic Role Labeling with Pointer Networks
Evaluating Explanations: How much do explanations from the teacher aid students?
TrustScore: Reference-Free Evaluation of LLM Response Trustworthiness
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.