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Open-Ended Question Answering
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Open-ended questions are defined as those that simply pose the question, without imposing any constraints on the format of the response. This distinguishes them from questions with a predetermined answer format.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/aparna-b/fairrankingrelevance
공식 코드https://github.com/apple/ml-cvnets
공식 코드RangeAugment: Efficient Online Augmentation with Range Learning
https://github.com/apple2373/action-general.icdl
공식 코드Action Recognition based on Cross-Situational Action-object Statistics
https://github.com/automl/autorl-landscape
공식 코드https://github.com/awwkl/brain_language_narratives
공식 코드Training language models to summarize narratives improves brain alignment
https://github.com/awwkl/brain_language_summarization
공식 코드Training language models to summarize narratives improves brain alignment
https://github.com/backpropper/cbc-emecom
공식 코드Capacity, Bandwidth, and Compositionality in Emergent Language Learning
https://github.com/baharanm/craig
공식 코드Coresets for Data-efficient Training of Machine Learning Models
https://github.com/bashlab/llasa
공식 코드LLaSA: A Multimodal LLM for Human Activity Analysis Through Wearable and Smartphone Sensors
https://github.com/bcdnlp/prd
공식 코드PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations
https://github.com/bigml-cs-ucla/sas-data-efficient-contrastive-learning
공식 코드https://github.com/braqiiit/qrealboost
공식 코드https://github.com/bunsenfeng/kalm
공식 코드https://github.com/bzhai/ubi-sleepnet
공식 코드https://github.com/chacha-chen/explanations-human-studies
공식 코드https://github.com/changlin31/BossNAS
공식 코드https://github.com/chengw07/InfoTS
공식 코드Time Series Contrastive Learning with Information-Aware Augmentations
https://github.com/cindyxinyiwang/TrDec_pytorch
공식 코드https://github.com/cisnlp/mplm-sim
공식 코드mPLM-Sim: Better Cross-Lingual Similarity and Transfer in Multilingual Pretrained Language Models
https://github.com/coastalcph/lex-glue
공식 코드LexGLUE: A Benchmark Dataset for Legal Language Understanding in English
https://github.com/cofe-ai/msg
공식 코드Masked Structural Growth for 2x Faster Language Model Pre-training
https://github.com/cristianocapone/biodreaming
공식 코드Towards biologically plausible Dreaming and Planning in recurrent spiking networks
https://github.com/davidzhangyuanhan/bamboo
공식 코드Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy
https://github.com/davidzoltowski/ssmdm
공식 코드Unifying and generalizing models of neural dynamics during decision-making
https://github.com/desy-ml/rl-vs-bo
공식 코드https://github.com/devnkong/FLAG
공식 코드Robust Optimization as Data Augmentation for Large-scale Graphs
https://github.com/dfki-nlp/fewie
공식 코드A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition
https://github.com/docwza/sumolights
공식 코드An Open-Source Framework for Adaptive Traffic Signal Control
https://github.com/dongquan11/CB-HS.SOPPP
공식 코드Path Planning Problems with Side Observations-When Colonels Play Hide-and-Seek
https://github.com/dpernes/modafm
공식 코드Tackling unsupervised multi-source domain adaptation with optimism and consistency
https://github.com/drorlab/DIPS
공식 코드End-to-End Learning on 3D Protein Structure for Interface Prediction
https://github.com/echostatements/decompose
공식 코드https://github.com/edisonni-hku/mtl4finance
공식 코드When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP
https://github.com/elkabzo/cont_disc_opt_dnn
공식 코드Continuous vs. Discrete Optimization of Deep Neural Networks
https://github.com/emalach/IsDeeperBetter
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.