https://github.com/zhjohnchan/ptunifier
공식 코드Towards Unifying Medical Vision-and-Language Pre-training via Soft Prompts
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/zhongfendeng/jpave
공식 코드https://github.com/zhongwanjun/proqa
공식 코드ProQA: Structural Prompt-based Pre-training for Unified Question Answering
https://github.com/zhougroup/BAM
공식 코드https://github.com/zhourobotics/reasondrive
공식 코드https://github.com/zhousheng97/egotextvqa
공식 코드EgoTextVQA: Towards Egocentric Scene-Text Aware Video Question Answering
https://github.com/zhousheng97/vitxt-gqa
공식 코드Scene-Text Grounding for Text-Based Video Question Answering
https://github.com/zhouzihao501/nlpcc2023-shared-task3-chinesemwp
공식 코드https://github.com/zhuang-li/scar
공식 코드https://github.com/zhudotexe/fanoutqa
공식 코드FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models
https://github.com/zhuemann/contextualsegmentation
공식 코드ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of Pneumothorax
https://github.com/zhukun1020/noisefilter_ib
공식 코드https://github.com/zhuyunqi96/LoraLPrun
공식 코드Parameter-Efficient Fine-Tuning with Layer Pruning on Free-Text Sequence-to-Sequence Modeling
https://github.com/zhwwwww/attribute-comprehension-of-vlms
공식 코드Evaluating Attribute Comprehension in Large Vision-Language Models
https://github.com/zhxlia/multitat
공식 코드MULTITAT: Benchmarking Multilingual Table-and-Text Question Answering
https://github.com/zhxlia/murre
공식 코드MURRE: Multi-Hop Table Retrieval with Removal for Open-Domain Text-to-SQL
https://github.com/zhxlia/scitat
공식 코드https://github.com/ziegler-ingo/CRAFT
공식 코드https://github.com/ziems/llm-url
공식 코드Large Language Models are Built-in Autoregressive Search Engines
https://github.com/zifengding/forecasttkgqa
공식 코드https://github.com/zihangdai/xlnet
공식 코드XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/zihanzhaosjtu/librisqa
공식 코드LibriSQA: A Novel Dataset and Framework for Spoken Question Answering with Large Language Models
https://github.com/zihao-ai/vdc
공식 코드https://github.com/zihao-wang/number-nli
공식 코드A Neural-Symbolic Approach to Natural Language Understanding
https://github.com/zihuaevan/acfmodel
공식 코드Adaptively Clustering Neighbor Elements for Image-Text Generation
https://github.com/zinengtang/tvlt
공식 코드https://github.com/ziplab/longvlm
공식 코드LongVLM: Efficient Long Video Understanding via Large Language Models
https://github.com/ziqipang/lm4visualencoding
공식 코드Frozen Transformers in Language Models Are Effective Visual Encoder Layers
https://github.com/zivenzhu/multi-chart-qa
공식 코드MultiChartQA: Benchmarking Vision-Language Models on Multi-Chart Problems
https://github.com/ziyin-dl/word-embedding-dimensionality-selection
공식 코드https://github.com/ziyuguo99/point-bind_point-llm
공식 코드https://github.com/zju-vipa/odyssey
공식 코드https://github.com/zjukg/conferenceqa
공식 코드Reliable Academic Conference Question Answering: A Study Based on Large Language Model
https://github.com/zjukg/kgtransformer
공식 코드Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer
https://github.com/zjukg/knowpat
공식 코드Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering
https://github.com/zjukg/lpkg
공식 코드Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.