High-quality training data is critical to the performance of large language models (LLMs). Recent work has explored using LLMs to rate and select data based on a small set of human-designed criteria (rules), but these approaches often rely heavily on heuristics, lack principled metrics for rule evaluation, and generalize poorly to new tasks. We propose a novel rule-based data selection framework that introduces a metric based on the orthogonality of rule score vectors to evaluate and select complementary rules. Our automated pipeline first uses LLMs to generate diverse rules covering multiple aspects of data quality, then rates samples according to these rules and applies the determinantal point process (DPP) to select the most independent rules. These rules are then used to score the full dataset, and high-scoring samples are selected for downstream tasks such as LLM fine-tuning. We evaluate our framework in two experiment setups: (1) alignment with ground-truth ratings and (2) performance of LLMs fine-tuned on the selected data. Experiments across IMDB, Medical, Math, and Code domains demonstrate that our DPP-based rule selection consistently improves both rating accuracy and downstream model performance over strong baselines.

Radar-based, contactless in-home vital sign monitoring can significantly improve the healthcare of the rapidly growing aging population by enabling early detection of clinical events and continuous tracking of physiological states, while maintaining privacy, comfort, and user compliance. We introduce a unique experimental environment, collected for over a year – consisting of populated test homes, equipped with arrays of continuous-wave radar sensors placed on ceilings and walls, with occupants engaged in everyday activities within the homes. We applied and compared multiple analytical methods to extract heart rate from the radar data, using combinations of normalized cross-correlation (NXC), machine learning with individual sensors, and sensor fusion. Our results show that sensor fusion leads to the lowest mean absolute errors, reduced by over 30% from single-sensor machine learning models and over 80% from NXC. We additionally observed that a dense sensor array design enhances signal quality and reduces noise by more than 15%, providing more accurate heart rate monitoring even during dynamic activities, such as jumping and walking. These results highlight the effectiveness of sensor fusion and dense array designs in real-world settings. Our novel experimental platform and analytical results represent a meaningful step toward scalable application of radar technology in real residential settings for non-contact health monitoring.

Reinforcement Learning from Human Feedback (RLHF) is widely used to align models with human preferences, particularly to enhance the safety of responses generated by LLMs. This method traditionally relies on choosing preferred responses from response pairs. However, due to variations in human opinions and the difficulty of making an overall comparison of two responses, there is a growing shift towards a fine-grained annotation approach, assessing responses based on multiple specific metrics or rules. Selecting and applying these rules efficiently while accommodating the diversity of preference data remains a significant challenge. In this paper, we introduce a dynamic approach that adaptively selects the most critical rules for each pair of responses. We develop a mathematical framework that leverages the maximum discrepancy between each paired responses and theoretically show that this strategy optimizes the mutual information between the rule-based labeling and the hidden ground-truth preferences. We then train an 8B reward model using the adaptively labeled preference dataset and evaluate its performance on RewardBench. As of May 25, 2025, our model achieved the highest safety performance on the leaderboard, outperforming various larger models.

Large language models (LLMs) are increasingly essential in processing natural languages, yet their application is frequently compromised by biases and inaccuracies originating in their training data.In this study, we introduce \textbf{Cross-Care}, the first benchmark framework dedicated to assessing biases and real world knowledge in LLMs, specifically focusing on the representation of disease prevalence across diverse demographic groups.We systematically evaluate how demographic biases embedded in pre-training corpora like $ThePile$ influence the outputs of LLMs.We expose and quantify discrepancies by juxtaposing these biases against actual disease prevalences in various U.S. demographic groups.Our results highlight substantial misalignment between LLM representation of disease prevalence and real disease prevalence rates across demographic subgroups, indicating a pronounced risk of bias propagation and a lack of real-world grounding for medical applications of LLMs.Furthermore, we observe that various alignment methods minimally resolve inconsistencies in the models' representation of disease prevalence across different languages.For further exploration and analysis, we make all data and a data visualization tool available at: \url{www.crosscare.net}.

Hongyin Luo, Shang-Wen Li, Mingye Gao, Seunghak Yu, James Glass. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.