Papers with Code 아카이브
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 )
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yilihsu/asktoknowmore
공식 코드Ask to Know More: Generating Counterfactual Explanations for Fake Claims
https://github.com/yilunzhao/robut
공식 코드RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations
https://github.com/yinghaohu/legalhalbench
공식 코드Fine-tuning Large Language Models for Improving Factuality in Legal Question Answering
https://github.com/yingweima2022/codellm
공식 코드https://github.com/yinwenpeng/Answer_Selection
공식 코드ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs
https://github.com/yinwenpeng/SciTail
공식 코드End-Task Oriented Textual Entailment via Deep Explorations of Inter-Sentence Interactions
https://github.com/yinzhangyue/AoR
공식 코드https://github.com/yinzhangyue/c2fm
공식 코드https://github.com/yinzhangyue/smoothing-r3
공식 코드https://github.com/yinzhu-quan/lm-evaluation-harness
공식 코드https://github.com/yiranwu0/mathchat
공식 코드MathChat: Converse to Tackle Challenging Math Problems with LLM Agents
https://github.com/yixiangch/mrd-rag
공식 코드MRD-RAG: Enhancing Medical Diagnosis with Multi-Round Retrieval-Augmented Generation
https://github.com/yixuan423/fakebench
공식 코드FakeBench: Probing Explainable Fake Image Detection via Large Multimodal Models
https://github.com/yiyunya/RODA
공식 코드Reverse Operation based Data Augmentation for Solving Math Word Problems
https://github.com/yjch00/plural
공식 코드https://github.com/ykotseruba/snap
공식 코드SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks
https://github.com/yl-1993/learn-to-cluster
공식 코드https://github.com/yl3800/eigv
공식 코드Equivariant and Invariant Grounding for Video Question Answering
https://github.com/yl3800/igv
공식 코드https://github.com/yl3800/transtr
공식 코드Discovering Spatio-Temporal Rationales for Video Question Answering
https://github.com/ymcui/ExpMRC
공식 코드ExpMRC: Explainability Evaluation for Machine Reading Comprehension
https://github.com/ymcui/mrc-model-analysis
공식 코드Multilingual Multi-Aspect Explainability Analyses on Machine Reading Comprehension Models
https://github.com/ynklab/JBBQ_data
공식 코드https://github.com/yoichi1484/knowledge-sanitization
공식 코드https://github.com/yonatanbitton/AutoGenOfContrastSetsFromSceneGraphs
공식 코드https://github.com/yonatanbitton/wysiwyr
공식 코드What You See is What You Read? Improving Text-Image Alignment Evaluation
https://github.com/youblei/prompt-space
공식 코드Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models
https://github.com/youganglyu/knowtuning
공식 코드KnowTuning: Knowledge-aware Fine-tuning for Large Language Models
https://github.com/youlandasu/choice-fusion
공식 코드Choice Fusion as Knowledge for Zero-Shot Dialogue State Tracking
https://github.com/youshanzhang/sparrowvqe
공식 코드SparrowVQE: Visual Question Explanation for Course Content Understanding
https://github.com/yrbobo/mqs-ecl
공식 코드Medical Question Summarization with Entity-driven Contrastive Learning
https://github.com/ys-zong/foolyourvllms
공식 코드Fool Your (Vision and) Language Model With Embarrassingly Simple Permutations
https://github.com/ys-zong/vl-icl
공식 코드VL-ICL Bench: The Devil in the Details of Multimodal In-Context Learning
https://github.com/ysngki/mixencoder
공식 코드Once is Enough: A Light-Weight Cross-Attention for Fast Sentence Pair Modeling
https://github.com/ysymyth/ReAct
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.