https://github.com/jiaruifeng/taglas
공식 코드TAGLAS: An atlas of text-attributed graph datasets in the era of large graph and language models
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/jiaxin-pei/potato-prolific-dataset
공식 코드https://github.com/jiaxin-wen/misleadlm
공식 코드https://github.com/jieyuz2/taskmeanything
공식 코드https://github.com/jifan-chen/qa-verification-via-nli
공식 코드https://github.com/jihaonew/mm-instruct
공식 코드MM-Instruct: Generated Visual Instructions for Large Multimodal Model Alignment
https://github.com/jiho283/simulator
공식 코드https://github.com/jihuishan/flexible_evaluation_for_vqa_mm24
공식 코드Towards Flexible Evaluation for Generative Visual Question Answering
https://github.com/jimengshi/hypercube-rag
공식 코드https://github.com/jimmyxichen/sarlang-1m
공식 코드SARLANG-1M: A Benchmark for Vision-Language Modeling in SAR Image Understanding
https://github.com/jimxionggm/interactive-kbqa
공식 코드https://github.com/jind11/MMM-MCQA
공식 코드MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
https://github.com/jind11/MedQA
공식 코드https://github.com/jingfengsteven/acpo
공식 코드Atomic Consistency Preference Optimization for Long-Form Question Answering
https://github.com/jinghanY/physicalAttackImageComposition
공식 코드https://github.com/jingjing12110/mixphm
공식 코드MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-Resource Visual Question Answering
https://github.com/jingjing12110/x-ggm
공식 코드X-GGM: Graph Generative Modeling for Out-of-Distribution Generalization in Visual Question Answering
https://github.com/jingkang50/funqa
공식 코드https://github.com/jingli9111/GORU-tensorflow
공식 코드https://github.com/jingli9111/RUM
공식 코드https://github.com/jingli9111/RUM-Tensorflow
공식 코드https://github.com/jingtaozhan/repconc
공식 코드https://github.com/jingyangqiao/coin
공식 코드https://github.com/jinhaoduan/sar
공식 코드https://github.com/jinhaoduan/shifting-attention-to-relevance
공식 코드https://github.com/jinhaoduan/uprop
공식 코드UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making
https://github.com/jinheonbaek/kalmv
공식 코드https://github.com/jinlanfu/polyglot_prompt
공식 코드https://github.com/jinlhe/pefomed
공식 코드PeFoMed: Parameter Efficient Fine-tuning of Multimodal Large Language Models for Medical Imaging
https://github.com/jitaishik/revenue_comparisonhc
공식 코드A Revenue Function for Comparison-Based Hierarchical Clustering
https://github.com/jitinkrishnan/NASA-SE
공식 코드https://github.com/jiyt17/ida-vlm
공식 코드IDA-VLM: Towards Movie Understanding via ID-Aware Large Vision-Language Model
https://github.com/jjacampos/FeedbackWeightedLearning
공식 코드https://github.com/jjcherian/conformal-safety
공식 코드Large language model validity via enhanced conformal prediction methods
https://github.com/jjzha/fs1
공식 코드Scaling Reasoning can Improve Factuality in Large Language Models
https://github.com/jmerullo/limber
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.