TorchUMM: A Unified Multimodal Model Codebase for Evaluation, Analysis, and Post-training
Paper • 2604.10784 • Published • 5
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jindongwang
authored 18
papers 11 months ago
Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks
Paper • 2309.17002 • Published • 1
DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks
Paper • 2309.17167 • Published • 1
Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
Paper • 2310.07521 • Published
Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup
Paper • 2305.16817 • Published
Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models
Paper • 2311.01441 • Published • 1
Supervised Knowledge Makes Large Language Models Better In-context Learners
Paper • 2312.15918 • Published • 9
Large Language Models Understand and Can be Enhanced by Emotional Stimuli
Paper • 2307.11760 • Published • 1
Large Language Model Evaluation via Matrix Entropy
Paper • 2401.17139 • Published • 3
Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning
Paper • 2308.02533 • Published
From Instructions to Intrinsic Human Values -- A Survey of Alignment Goals for Big Models
Paper • 2308.12014 • Published • 1
MM-Soc: Benchmarking Multimodal Large Language Models in Social Media Platforms
Paper • 2402.14154 • Published • 2
Dynamic Evaluation of Large Language Models by Meta Probing Agents
Paper • 2402.14865 • Published
KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models
Paper • 2402.15043 • Published • 1
Memory-Guided Multi-View Multi-Domain Fake News Detection
Paper • 2206.12808 • Published
Detoxifying Large Language Models via Knowledge Editing
Paper • 2403.14472 • Published • 3
Domain-Specific Risk Minimization for Out-of-Distribution Generalization
Paper • 2208.08661 • Published
GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective
Paper • 2211.08073 • Published • 1
Slight Corruption in Pre-training Data Makes Better Diffusion Models
Paper • 2405.20494 • Published