Common Sense Reasoning
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Benchmarks
WinoGrande
ARC (Challenge)
ARC (Easy)
ReCoRD
CommonsenseQA
PARus
RWSD
RuCoS
Event2Mind test
Russian Event2Mind
SWAG
BIG-bench (Winowhy)
Event2Mind dev
CODAH
CrowdSource QA
Visual Dialog v0.9
Visual Dialog v0.9
WinoGAViL
Most implemented
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Language Models are Few-Shot Learners
RoBERTa: A Robustly Optimized BERT Pretraining Approach
LLaMA: Open and Efficient Foundation Language Models
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Papers
The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes
Large Language Models (LLMs) have achieved strong performance across natural language processing tasks, yet reliable reasoning remains an open challenge. Although modern LLMs show progress in structured inference, multi-…
Mathematical ReasoningCommon Sense ReasoningReinforcement LearningDomain GeneralizationMAGNIFIED: RL Fine-tuning of Multimodal Large Language Models for Motion Planning
Multi-modal Large Language Models (MLLMs) have demonstrated remarkable capabilities in semantic understanding and common sense reasoning, making them promising candidates for solving planning problems in autonomous drivi…
Common Sense ReasoningReinforcement LearningAutonomous VehiclesAutonomous DrivingERGeoBench:A Comprehensive Benchmark for Embodied Reasoning and Geo-localization in Multimodal Large Language Models
Multimodal large language models (MLLMs) have shown strong potential as embodied agents, yet embodied geo-localization remains underexplored due to the lack of fine-grained evaluation. We introduce ERGeoBench, a diagnost…
Common Sense ReasoningSpatial ReasoningConQuR: Corner Aligned Activation Quantization via Optimized Rotations for LLMs
Large language models (LLMs) are costly to deploy due to their large memory footprint and high inference cost. Weight-activation quantization can reduce these costs, but low-bit activation quantization remains difficult …
Common Sense ReasoningLeveraging Pretrained Language Models as Energy Functions for Glauber Dynamics Text Diffusion
We present a discrete diffusion-based language model using Glauber dynamics from statistical physics. Our main insight is that instead of trying to train a discrete state space diffusion model using Glauber dynamics with…
Common Sense ReasoningLong-Context Aware Upcycling: A New Frontier for Hybrid LLM Scaling
Hybrid sequence models that combine efficient Transformer components with linear sequence modeling blocks are a promising alternative to pure Transformers, but most are still pretrained from scratch and therefore fail to…
Common Sense Reasoning