https://github.com/fdistinctions/ijcai18
공식 코드Empirical Analysis of Foundational Distinctions in Linked Open Data
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/francois-rd/accord
공식 코드$\texttt{ACCORD}$: Closing the Commonsense Measurability Gap
https://github.com/gautierdag/piglet-vis
공식 코드Learning the Effects of Physical Actions in a Multi-modal Environment
https://github.com/gcollell/spatial-commonsense
공식 코드Acquiring Common Sense Spatial Knowledge through Implicit Spatial Templates
https://github.com/ghazalmilani/one-shot-learning-from-text-iclp2023
공식 코드Towards One-Shot Learning for Text Classification using Inductive Logic Programming
https://github.com/ghupppp/memorysharingllm
공식 코드https://github.com/giannispana/ails-ntua-at-semeval-2024-task-9-brainteaser
공식 코드https://github.com/gingasan/creat
공식 코드Toward Adversarial Training on Contextualized Language Representation
https://github.com/gkevinyen5418/LoRA-RITE
공식 코드LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization
https://github.com/google-deepmind/concordia
공식 코드https://github.com/google-research-datasets/timedial
공식 코드https://github.com/google-research/ALBERT
공식 코드ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/google-research/bert
공식 코드BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/google-research/flan
공식 코드https://github.com/google-research/google-research
공식 코드https://github.com/google-research/language
공식 코드Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models
https://github.com/google-research/multilingual-t5
공식 코드mT5: A massively multilingual pre-trained text-to-text transformer
https://github.com/google/BIG-bench
공식 코드Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
https://github.com/graphpku/coi
공식 코드https://github.com/guyyariv/vlmig
공식 코드Improving Visual Commonsense in Language Models via Multiple Image Generation
https://github.com/hakys-a/meow
공식 코드https://github.com/happywwy/elabor
공식 코드Elaboration-Generating Commonsense Question Answering at Scale
https://github.com/haryoa/copal-id
공식 코드COPAL-ID: Indonesian Language Reasoning with Local Culture and Nuances
https://github.com/hemingkx/imagenetvc
공식 코드ImageNetVC: Zero- and Few-Shot Visual Commonsense Evaluation on 1000 ImageNet Categories
https://github.com/hmz-15/Interactive-Scene-Reconstruction
공식 코드Reconstructing Interactive 3D Scenes by Panoptic Mapping and CAD Model Alignments
https://github.com/hri-eu/common_sense_for_robots
공식 코드Exploring Large Language Models as a Source of Common-Sense Knowledge for Robots
https://github.com/huawei-noah/kd-nlp
공식 코드https://github.com/hustvl/alphadrive
공식 코드https://github.com/hyintell/llmsymbolic
공식 코드https://github.com/ianporada/modeling_event_plausibility
공식 코드Modeling Event Plausibility with Consistent Conceptual Abstraction
https://github.com/ibm/diffg-rl
공식 코드DiffG-RL: Leveraging Difference between State and Common Sense
https://github.com/icitylab/hspr
공식 코드Hierarchical Spatial Proximity Reasoning for Vision-and-Language Navigation
https://github.com/iesl/protoqa-data
공식 코드ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning
https://github.com/ilyagusev/headlinecause
공식 코드HeadlineCause: A Dataset of News Headlines for Detecting Causalities
https://github.com/imsquared/prime-the-search
공식 코드https://github.com/internlm/lmdeploy
공식 코드AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.