Papers Preference Mapping
“Preference Mapping” 태그가 달린 논문 11편 · 필터 해제
PAL: Pluralistic Alignment Framework for Learning from Heterogeneous Preferences
Large foundation models pretrained on raw web-scale data are not readily deployable without additional step of extensive alignment to human preferences. Such alignment is typically done by collecting large amounts of pai…
Preference MappingPersonalized Language Modeling from Personalized Human Feedback
Personalized large language models (LLMs) are designed to tailor responses to individual user preferences. While Reinforcement Learning from Human Feedback (RLHF) is a commonly used framework for aligning LLMs with human…
Instruction FollowingLanguage ModelingLanguage ModellingPreference Mapping+1Distributional Domain-Invariant Preference Matching for Cross-Domain Recommendation
Learning accurate cross-domain preference mappings in the absence of overlapped users/items has presented a persistent challenge in Non-overlapping Cross-domain Recommendation (NOCDR). Despite the efforts made in previou…
Preference MappingHuman Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis
Recent text-to-image generative models can generate high-fidelity images from text inputs, but the quality of these generated images cannot be accurately evaluated by existing evaluation metrics. To address this issue, w…
Image GenerationPreference MappingDirect Preference Optimization: Your Language Model is Secretly a Reward Model
While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised nature of their trai…
Language ModelingLanguage ModellingmodelPreference Mapping+2Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation
The ability to collect a large dataset of human preferences from text-to-image users is usually limited to companies, making such datasets inaccessible to the public. To address this issue, we create a web app that enabl…
Image GenerationPreference MappingText to Image GenerationText-to-Image GenerationImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation
We present a comprehensive solution to learn and improve text-to-image models from human preference feedback. To begin with, we build ImageReward -- the first general-purpose text-to-image human preference reward model -…
Image GenerationPreference MappingText to Image GenerationText-to-Image GenerationLAION-5B: An open large-scale dataset for training next generation image-text models
Groundbreaking language-vision architectures like CLIP and DALL-E proved the utility of training on large amounts of noisy image-text data, without relying on expensive accurate labels used in standard vision unimodal su…
Image GenerationPreference Mappingzero-shot-classificationZero-Shot LearningLearning Transferable Visual Models From Natural Language Supervision
State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is n…
Action RecognitionBenchmarkingFew-Shot Image Classificationgeo-localization+21Curvature as an Organizing Principle of Mid-level Visual Representation: A Semantic-preference Mapping Approach
A central challenge in visual neuroscience is understanding the mid-level representations of the ventral stream. We used a novel, data-driven approach (semantic-preference mapping) combined with an image-statistics appro…
ObjectPreference MappingPAI-BPR: Personalized Outfit Recommendation Scheme with Attribute-wise Interpretability
Fashion is an important part of human experience. Events such as interviews, meetings, marriages, etc. are often based on clothing styles. The rise in the fashion industry and its effect on social influencing have made o…
AttributePreference Mapping