Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 19
- 평가표
- 153
- 보존된 평가 기록
- 1,781
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 )
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/asadifar/HCqa
공식 코드HCqa: Hybrid and Complex Question Answering on Textual Corpus and Knowledge Graph
https://github.com/asahi417/lm-question-generation
공식 코드Generative Language Models for Paragraph-Level Question Generation
https://github.com/asaparov/fictionalgeoqa
공식 코드Towards General Natural Language Understanding with Probabilistic Worldbuilding
https://github.com/asaparov/prontoqa
공식 코드Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought
https://github.com/asaparov/pwl
공식 코드Towards General Natural Language Understanding with Probabilistic Worldbuilding
https://github.com/asappresearch/sru
공식 코드https://github.com/asgaardlab/LLMxBugs
공식 코드Large Language Models are Pretty Good Zero-Shot Video Game Bug Detectors
https://github.com/asgsaeid/cad_vqa
공식 코드How to Determine the Preferred Image Distribution of a Black-Box Vision-Language Model?
https://github.com/ashkamath/mdetr
공식 코드MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding
https://github.com/ashwindaswanibu/qaqa-synthetic-dataset
공식 코드Syn-QA2: Evaluating False Assumptions in Long-tail Questions with Synthetic QA Datasets
https://github.com/astridesa/edueval
공식 코드Large Language Models on Wikipedia-Style Survey Generation: an Evaluation in NLP Concepts
https://github.com/atr-dbi/scanqa
공식 코드ScanQA: 3D Question Answering for Spatial Scene Understanding
https://github.com/atsumiyai/upd
공식 코드Unsolvable Problem Detection: Evaluating Trustworthiness of Vision Language Models
https://github.com/atul04/PiazzaVirtualAssistant
공식 코드https://github.com/aub-mind/araBERT
공식 코드AraBERT: Transformer-based Model for Arabic Language Understanding
https://github.com/audiollms/singlish
공식 코드Advancing Singlish Understanding: Bridging the Gap with Datasets and Multimodal Models
https://github.com/aurooj/MMFT-BERT
공식 코드MMFT-BERT: Multimodal Fusion Transformer with BERT Encodings for Visual Question Answering
https://github.com/aurooj/WeakGroundedVQA_Capsules
공식 코드Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsules
https://github.com/aurooj/shg-vqa
공식 코드Learning Situation Hyper-Graphs for Video Question Answering
https://github.com/aurooj/wsg-vqa-vltransformers
공식 코드Weakly Supervised Grounding for VQA in Vision-Language Transformers
https://github.com/automl/autorl-landscape
공식 코드https://github.com/avariengien/causal-checker
공식 코드Look Before You Leap: A Universal Emergent Decomposition of Retrieval Tasks in Language Models
https://github.com/aviclu/CD-LM
공식 코드https://github.com/aviclu/cdlm
공식 코드https://github.com/aviclu/long-context-qa-contrast
공식 코드Long Context Question Answering via Supervised Contrastive Learning
https://github.com/aviclu/peekacross
공식 코드Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering
https://github.com/aviclu/seq-contrast-qa
공식 코드Long Context Question Answering via Supervised Contrastive Learning
https://github.com/avikalp7/VQAtoCQA
공식 코드Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms
https://github.com/avinashsai/mml
공식 코드https://github.com/awalesushil/DBLP-QuAD
공식 코드DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graph
https://github.com/aws/fmeval
공식 코드https://github.com/awslabs/decode-answer-logical-form
공식 코드DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases
https://github.com/awslabs/durepa-hybrid-qa
공식 코드Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering
https://github.com/awslabs/optimizing-multitask-training-through-dynamic-pipelines
공식 코드DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines
https://github.com/awslabs/rag-qa-arena
공식 코드RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.