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Found 5 papers, 0 papers with code
CURATRON: Complete Robust Preference Data for Robust Alignment of Large Language Models
This paper addresses the challenges of aligning large language models (LLMs) with human values via preference learning (PL), with a focus on the issues of incomplete and corrupted data in preference datasets.
User Friendly and Adaptable Discriminative AI: Using the Lessons from the Success of LLMs and Image Generation Models
in a fashion similar to generative AI tools, where they can easily provide immediate feedback as well as adapt the deployed models as desired.
Dynamic Tiling: A Model-Agnostic, Adaptive, Scalable, and Inference-Data-Centric Approach for Efficient and Accurate Small Object Detection
We introduce Dynamic Tiling, a model-agnostic, adaptive, and scalable approach for small object detection, anchored in our inference-data-centric philosophy.
Generative AI for Business Strategy: Using Foundation Models to Create Business Strategy Tools
Generative models (foundation models) such as LLMs (large language models) are having a large impact on multiple fields.
Estimating the Personality of White-Box Language Models
Our work seeks to address this gap by exploring the personality traits of several large-scale language models designed for open-ended text generation and the datasets used for training them.
