https://github.com/YangYongJin/APEX
공식 코드Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language Models
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/ZeroNLP/Eraser
공식 코드Eraser: Jailbreaking Defense in Large Language Models via Unlearning Harmful Knowledge
https://github.com/Zhaolu-K/HSSBench
공식 코드HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models
https://github.com/abdoelsayed2016/grg
공식 코드Generator-Retriever-Generator Approach for Open-Domain Question Answering
https://github.com/abhijeetptl5/historoi
공식 코드Efficient Quality Control of Whole Slide Pathology Images with Human-in-the-loop Training
https://github.com/adhyyannarang/online_bp
공식 코드Online SuBmodular + SuPermodular (BP) Maximization with Bandit Feedback
https://github.com/adianliusie/mcrc
공식 코드https://github.com/adobe-research/polar-vl
공식 코드Improving Personalized Search with Regularized Low-Rank Parameter Updates
https://github.com/agrzheng/GRADA
공식 코드GRADA: Graph-based Reranker against Adversarial Documents Attack
https://github.com/ai4life-group/med-safety-bench
공식 코드MedSafetyBench: Evaluating and Improving the Medical Safety of Large Language Models
https://github.com/ais-bonn/prompt_engineering
공식 코드A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service Robotics
https://github.com/aitsc/glmkd
공식 코드GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language Model
https://github.com/aldrich2y/mianet
공식 코드MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation
https://github.com/alexa/wqa_tanda
공식 코드TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection
https://github.com/alibaba/AliceMind/tree/main/StructBERT
공식 코드https://github.com/aliborji/d2o
공식 코드Diverse, Difficult, and Odd Instances (D2O): A New Test Set for Object Classification
https://github.com/allenai/document-qa
공식 코드https://github.com/allenai/easy-to-hard-generalization
공식 코드The Unreasonable Effectiveness of Easy Training Data for Hard Tasks
https://github.com/allenai/longformer
공식 코드https://github.com/allenai/semantic_fragments
공식 코드What Does My QA Model Know? Devising Controlled Probes using Expert Knowledge
https://github.com/amzn/refuel-open-domain-qa
공식 코드Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction
https://github.com/apple/corenet
공식 코드https://github.com/apple/ml-mkqa
공식 코드MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering
https://github.com/aritra23/dragonverseqa
공식 코드DragonVerseQA: Open-Domain Long-Form Context-Aware Question-Answering
https://github.com/ark-130994/gw-solvers
공식 코드Uncovering Challenges of Solving the Continuous Gromov-Wasserstein Problem
https://github.com/as18cia/thesis_work
공식 코드https://github.com/auniquesun/point-prc
공식 코드Point-PRC: A Prompt Learning Based Regulation Framework for Generalizable Point Cloud Analysis
https://github.com/baidu/DuReader
공식 코드https://github.com/bashlab/llasa
공식 코드LLaSA: A Multimodal LLM for Human Activity Analysis Through Wearable and Smartphone Sensors
https://github.com/beierzhu/prompt-align
공식 코드https://github.com/beyondhtx/qprompt
공식 코드Quantized Prompt for Efficient Generalization of Vision-Language Models
https://github.com/biomedia-mira/masf
공식 코드Domain Generalization via Model-Agnostic Learning of Semantic Features
https://github.com/blcuicall/omgeval
공식 코드OMGEval: An Open Multilingual Generative Evaluation Benchmark for Large Language Models
https://github.com/bodasadallah/decrypting-crosswords
공식 코드https://github.com/camwew/Cellular-Recognition_DA_CC
공식 코드Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View
https://github.com/centerforaisafety/safetywashing
공식 코드Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.