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Found 10 papers, 4 papers with code
HelpSteer2-Preference: Complementing Ratings with Preferences
Reward models are critical for aligning models to follow instructions, and are typically trained following one of two popular paradigms: Bradley-Terry style or Regression style.
RNR: Teaching Large Language Models to Follow Roles and Rules
However, existing models trained on open-source IFT datasets only have the ability to follow instructions from users, and often fail to follow complex role and rules specified by developers, a. k. a.
Robust Reinforcement Learning from Corrupted Human Feedback
To tackle this challenge, we propose a robust RLHF approach -- $R^3M$, which models the potentially corrupted preference label as sparse outliers.
Adaptive Preference Scaling for Reinforcement Learning with Human Feedback
By incorporating an adaptive scaling parameter into the loss for each pair, our method increases the flexibility of the reward function.
Data Diversity Matters for Robust Instruction Tuning
QDIT provides a simple method to simultaneously control dataset diversity and quality, allowing us to conduct an in-depth study on the effect of diversity and quality on instruction tuning performance.
Deep Reinforcement Learning from Hierarchical Preference Design
Researchers typically utilize feedback signals from the environment to handcraft a reward function, but this process is not always effective due to the varying scale and intricate dependencies of the feedback signals.
Machine Learning Force Fields with Data Cost Aware Training
To address this issue, we propose a multi-stage computational framework -- ASTEROID, which lowers the data cost of MLFFs by leveraging a combination of cheap inaccurate data and expensive accurate data.
AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning
Therefore, many fine-tuning methods are proposed to learn incremental updates of pre-trained weights in a parameter efficient way, e. g., low-rank increments.
PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance
Large Transformer-based models have exhibited superior performance in various natural language processing and computer vision tasks.
Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data
Recently, the Centers for Disease Control and Prevention (CDC) has worked with other federal agencies to identify counties with increasing coronavirus disease 2019 (COVID-19) incidence (hotspots) and offers support to local health departments to limit the spread of the disease.
