https://github.com/soonwook34/crkt
공식 코드Enhancing Knowledge Tracing with Concept Map and Response Disentanglement
Papers with Code 아카이브
Misconceptions
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 1
- 보존된 평가 기록
- 2
Measures whether a model can discern popular misconceptions from the truth.
Example:
input: The daddy longlegs spider is the most venomous spider in the world.
choice: T
choice: F
answer: F
input: Karl Benz is correctly credited with the invention of the first modern automobile.
choice: T
choice: F
answer: T
Source: BIG-bench
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sylinrl/truthfulqa
공식 코드https://github.com/tail-unica/ai-literacy-primary-ed
공식 코드A Structured Unplugged Approach for Foundational AI Literacy in Primary Education
https://github.com/tanny411/gpt3-reliability-check
공식 코드Reliability Check: An Analysis of GPT-3's Response to Sensitive Topics and Prompt Wording
https://github.com/theonlyandreas/end-to-end-crowdsourcing
공식 코드End-to-End Annotator Bias Approximation on Crowdsourced Single-Label Sentiment Analysis
https://github.com/uds-lsv/bert-stable-fine-tuning
공식 코드On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines
https://github.com/umass-ml4ed/dialogue-kt
공식 코드Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs
https://github.com/umass-ml4ed/divert
공식 코드https://github.com/umass-ml4ed/feedback-gen-dpo
공식 코드Improving the Validity of Automatically Generated Feedback via Reinforcement Learning
https://github.com/umass-ml4ed/prompt_distractor_generation_naacl
공식 코드https://github.com/xingchenwan/MLRG_DeepCurvature
공식 코드https://github.com/yoonholee/clarify
공식 코드Clarify: Improving Model Robustness With Natural Language Corrections
https://github.com/zjukg/snag
공식 코드Noise-powered Multi-modal Knowledge Graph Representation Framework
https://github.com/zzyuuuu/lightccf
공식 코드Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering
https://github.com/ENSTA-U2IS-AI/torch-uncertainty
https://github.com/IvanIsCoding/GNN-for-Combinatorial-Optimization
https://github.com/NVIDIA/FasterTransformer
Factuality Enhanced Language Models for Open-Ended Text Generation
https://github.com/allenai/dolma
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
https://github.com/bramiozo/PubScience
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
https://github.com/distractor-generation/dg_survey
https://github.com/guijinSON/SPOT
When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification of Scientific Research
https://github.com/jiesutd/PyTorchSeqLabel
Design Challenges and Misconceptions in Neural Sequence Labeling
https://github.com/karpathy/llama2.c
https://github.com/klemens-floege/svn_ensembles
https://github.com/learn-co-curriculum/dsc-3-28-12-graph-connectivity-community-detection
https://github.com/learn-co-curriculum/dsc-graph-connectivity-community-detection
https://github.com/learn-co-students/dsc-3-28-12-graph-connectivity-community-detection-bain-trial-jan19
https://github.com/learn-co-students/dsc-3-28-12-graph-connectivity-community-detection-data-science
https://github.com/learn-co-students/dsc-3-28-12-graph-connectivity-community-detection-data-science-demo
https://github.com/learn-co-students/dsc-3-28-12-graph-connectivity-community-detection-demo-online-ds-000
https://github.com/learn-co-students/dsc-3-28-12-graph-connectivity-community-detection-ds-onboarding
https://github.com/learn-co-students/dsc-3-28-12-graph-connectivity-community-detection-nyc-career-ds-102218
https://github.com/learn-co-students/dsc-3-28-12-graph-connectivity-community-detection-online-ds-ft-011419
https://github.com/learn-co-students/dsc-3-28-12-graph-connectivity-community-detection-online-ds-ft-021119
https://github.com/learn-co-students/dsc-3-28-12-graph-connectivity-community-detection-online-ds-ft-031119
https://github.com/learn-co-students/dsc-3-28-12-graph-connectivity-community-detection-online-ds-ft-100118
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.