https://github.com/TheAthleticCoder/iREL-at-SemEval-2024-Task-9
공식 코드iREL at SemEval-2024 Task 9: Improving Conventional Prompting Methods for Brain Teasers
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/TonyLianLong/LLM-groundedDiffusion
공식 코드https://github.com/UKPLab/argument-reasoning-comprehension-task
공식 코드The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants
https://github.com/Vaibhavs10/anli-performance-prediction
공식 코드Embarrassingly Simple Performance Prediction for Abductive Natural Language Inference
https://github.com/WENGSYX/Self-Verification
공식 코드Large Language Models are Better Reasoners with Self-Verification
https://github.com/Wang-Haining/noveval
공식 코드A Content-Based Novelty Measure for Scholarly Publications: A Proof of Concept
https://github.com/Websail-NU/AQuA
공식 코드CODAH: An Adversarially Authored Question-Answer Dataset for Common Sense
https://github.com/YingXu001/Attacks_on_Graph_Node_Attributes
공식 코드https://github.com/a-brassard/copa-sse
공식 코드COPA-SSE: Semi-structured Explanations for Commonsense Reasoning
https://github.com/aburns4/MoTIF
공식 코드https://github.com/adkipnis/metabench
공식 코드$\texttt{metabench}$ -- A Sparse Benchmark to Measure General Ability in Large Language Models
https://github.com/aemami1/KnowRef
공식 코드https://github.com/aflah02/quench
공식 코드QUENCH: Measuring the gap between Indic and Non-Indic Contextual General Reasoning in LLMs
https://github.com/ai4ce/CityWalker
공식 코드CityWalker: Learning Embodied Urban Navigation from Web-Scale Videos
https://github.com/aiha-lab/tsld
공식 코드Token-Scaled Logit Distillation for Ternary Weight Generative Language Models
https://github.com/allenai/Holodeck
공식 코드Holodeck: Language Guided Generation of 3D Embodied AI Environments
https://github.com/allenai/plasma
공식 코드https://github.com/allenai/qasc
공식 코드QASC: A Dataset for Question Answering via Sentence Composition
https://github.com/allenai/rainbow
공식 코드UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark
https://github.com/allenai/unifiedqa
공식 코드UnifiedQA: Crossing Format Boundaries With a Single QA System
https://github.com/anjsimmo/zero-shot-crime-detection
공식 코드Garbage in, garbage out: Zero-shot detection of crime using Large Language Models
https://github.com/anranwu-richpo/dcqa
공식 코드https://github.com/anshitag/memit_csk
공식 코드https://github.com/aplesner/flip-reasoning-challenge
공식 코드https://github.com/apolloresearch/gpt2_noln
공식 코드https://github.com/arajv/SayNav
공식 코드SayNav: Grounding Large Language Models for Dynamic Planning to Navigation in New Environments
https://github.com/artomaranjyan/GradSkip-code
공식 코드GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity
https://github.com/asayeed/activebaby
공식 코드https://github.com/asgaardlab/LLMxBugs
공식 코드Large Language Models are Pretty Good Zero-Shot Video Game Bug Detectors
https://github.com/autogenai/easy-problems-that-llms-get-wrong
공식 코드https://github.com/avalonstrel/mitigating-the-alignment-tax-of-rlhf
공식 코드https://github.com/baktash81/SemEval_2024_BRAINTEASER
공식 코드BAMO at SemEval-2024 Task 9: BRAINTEASER: A Novel Task Defying Common Sense
https://github.com/braincorp/PVM
공식 코드Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent Network
https://github.com/byungkwanlee/meteor
공식 코드Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models
https://github.com/calclavia/story-generation
공식 코드Improving Neural Story Generation by Targeted Common Sense Grounding
https://github.com/canjie-luo/real-300k
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.