Papers with Code 아카이브
Common Sense Reasoning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 17
- 평가표
- 24
- 보존된 평가 기록
- 397
Common sense reasoning tasks are intended to require the model to go beyond pattern recognition. Instead, the model should use "common sense" or world knowledge to make inferences.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yinxusen/deepword
공식 코드https://github.com/ysh-1998/csrec
공식 코드Common Sense Enhanced Knowledge-based Recommendation with Large Language Model
https://github.com/yueyang1996/z-lavi
공식 코드Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination
https://github.com/yuqingd/ellm
공식 코드Guiding Pretraining in Reinforcement Learning with Large Language Models
https://github.com/yurongyou/modest
공식 코드Learning to Detect Mobile Objects from LiDAR Scans Without Labels
https://github.com/yxKryptonite/OpenFMNav
공식 코드OpenFMNav: Towards Open-Set Zero-Shot Object Navigation via Vision-Language Foundation Models
https://github.com/zehao-wang/lad
공식 코드Layout-aware Dreamer for Embodied Referring Expression Grounding
https://github.com/zhangyx0417/ac_reason
공식 코드AC-Reason: Towards Theory-Guided Actual Causality Reasoning with Large Language Models
https://github.com/zhehuazhou/llm_reward_design
공식 코드https://github.com/zjunlp/Prompt4ReasoningPapers
공식 코드https://github.com/zli12321/videohallu
공식 코드VideoHallu: Evaluating and Mitigating Multi-modal Hallucinations on Synthetic Video Understanding
https://gitlab.com/sm-ai-team/simplemind
공식 코드https://huggingface.co/facebook/muppet-roberta-base
공식 코드Muppet: Massive Multi-task Representations with Pre-Finetuning
https://huggingface.co/facebook/muppet-roberta-large
공식 코드Muppet: Massive Multi-task Representations with Pre-Finetuning
https://worksheets.codalab.org/worksheets/0xc8aa93946ffc48a69e59041144491fe1
공식 코드https://worksheets.codalab.org/worksheets/0xf215deb05edf44a2ac353c711f52a25f
공식 코드QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
https://github.com/10Exahertz/Text-ResNet-on-Sentimental-LIAR-Fake-News
https://github.com/1wy/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-11
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-12
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-150
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-156
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
https://github.com/2023-MindSpore-1/ms-code-163
https://github.com/2023-MindSpore-1/ms-code-164
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/2023-MindSpore-1/ms-code-219/tree/main/albert
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/2023-MindSpore-1/ms-code-56
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/trn
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/llama
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/deberta
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/deberta_v2
https://github.com/2024-MindSpore-1/Code3/tree/main/scBERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2miatran/Natural-Language-Processing
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/810410738/DKN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.