https://github.com/gengzezhou/same
공식 코드SAME: Learning Generic Language-Guided Visual Navigation with State-Adaptive Mixture of Experts
Papers with Code 아카이브
General Knowledge
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 7
- 평가표
- 1
- 보존된 평가 기록
- 2
This task aims to evaluate the ability of a model to answer general-knowledge questions.
Source: BIG-bench
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/geobrain-ai/geogalactica
공식 코드GeoGalactica: A Scientific Large Language Model in Geoscience
https://github.com/giacoballoccu/explanation-quality-recsys
공식 코드https://github.com/google-research-datasets/QED
공식 코드QED: A Framework and Dataset for Explanations in Question Answering
https://github.com/google-research/google-research
공식 코드Fairness in Streaming Submodular Maximization: Algorithms and Hardness
https://github.com/google-research/language
공식 코드https://github.com/google-research/tapas
공식 코드Open Domain Question Answering over Tables via Dense Retrieval
https://github.com/gospelnnadi/text-summarization-sota-experiment
공식 코드Survey on Abstractive Text Summarization: Dataset, Models, and Metrics
https://github.com/graph-and-geometric-learning/helm
공식 코드HELM: Hyperbolic Large Language Models via Mixture-of-Curvature Experts
https://github.com/guangyaodou/SSU_Unlearn
공식 코드Avoiding Copyright Infringement via Large Language Model Unlearning
https://github.com/gudovskiy/contextflow
공식 코드https://github.com/h19920918/emr
공식 코드Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming Data
https://github.com/hadoov/GHRS
공식 코드GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation
https://github.com/haetae-project/hae-rae-bench
공식 코드HAE-RAE Bench: Evaluation of Korean Knowledge in Language Models
https://github.com/hao-cheng/ds_doc_qa
공식 코드https://github.com/haoang97/causalprobe-2024
공식 코드Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
https://github.com/haotianxia/SPORTU
공식 코드SPORTU: A Comprehensive Sports Understanding Benchmark for Multimodal Large Language Models
https://github.com/haotianxia/sportqa
공식 코드SportQA: A Benchmark for Sports Understanding in Large Language Models
https://github.com/hccngu/metnet
공식 코드Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition
https://github.com/hitsz-tmg/cognitive-visual-language-mapper
공식 코드A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question Answering
https://github.com/hitsz-tmg/explaincpe
공식 코드ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist Examination
https://github.com/hkust-nlp/dart-math
공식 코드DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving
https://github.com/hoqolo/sdstrack
공식 코드https://github.com/hugozhl/mevi
공식 코드https://github.com/huminghao16/RE3QA
공식 코드Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension
https://github.com/hurricanelx/lrfr
공식 코드https://github.com/hustCYQ/RAD-dataset
공식 코드RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detection
https://github.com/iai-group/iqn-kgqa
공식 코드https://github.com/ictnlp/NA-MNMT
공식 코드Importance-based Neuron Allocation for Multilingual Neural Machine Translation
https://github.com/ielab/asyncval
공식 코드Asyncval: A Toolkit for Asynchronously Validating Dense Retriever Checkpoints during Training
https://github.com/ielab/denseretriever-knowledge-acquisition
공식 코드Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition
https://github.com/intfloat/commonsense-rc
공식 코드https://github.com/ipouyall/benchmarking_chatgpt_for_persian
공식 코드Benchmarking Large Language Models for Persian: A Preliminary Study Focusing on ChatGPT
https://github.com/is2ai/kazqad
공식 코드https://github.com/jd-ai-research-nlp/ror
공식 코드RoR: Read-over-Read for Long Document Machine Reading Comprehension
https://github.com/jerrrynie/diverseqa
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.