Large Language Model
The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification
Additionally, a learnable component of the language prompts is trained using the available few-shot labeled data.
From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Models to Pre-trained Machine Reader
Discriminative methods were used to execute such tasks and achieved state-of-the-art performance.
What Makes and Breaks Safety Fine tuning A Mechanistic Study
Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., "design") versus the specific concepts the task is asked to be performed upon (e.g., a "cycle" vs. a "bomb").
When Y our AIs Deceive You: Challenges of Partial Observability in Reinforcement Learning from Human Feedback Leon Lang University of Amsterdam Davis Foote
Modeling the human as Boltzmann-rational w.r.t. a belief over trajectories, we prove conditions under which RLHF is
CALVIN: Improved Contextual Video Captioning via Instruction Tuning
The recent emergence of powerful Vision-Language models (VLMs) has significantly improved image captioning.
Optimizing Prompts for Text-to-Image Generation Y aru Hao
Well-designed prompts can guide text-to-image models to generate amazing images.
CausalChaos for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes
Understanding the intricate motivations behind human actions is paramount in developing sophisticated systems capable of nuanced behavior analysis.