Search Results for author:
Found 38 papers, 17 papers with code
Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language Models
Conventional language model (LM) safety alignment relies on a reactive, disjoint procedure: attackers exploit a static model, followed by defensive fine-tuning to patch exposed vulnerabilities.
RADAR: Benchmarking Language Models on Imperfect Tabular Data
Language models (LMs) are increasingly being deployed to perform autonomous data analyses.
LSM-2: Learning from Incomplete Wearable Sensor Data
Foundation models, a cornerstone of recent advancements in machine learning, have predominantly thrived on complete and well-structured data.
Substance over Style: Evaluating Proactive Conversational Coaching Agents
While NLP research has made strides in conversational tasks, many approaches focus on single-turn responses with well-defined objectives or evaluation criteria.
Inferring Event Descriptions from Time Series with Language Models
Events are often described with natural language, so we conduct the first study of whether Large Language Models (LLMs) can infer natural language events from time series.
Human Decision-making is Susceptible to AI-driven Manipulation
Through a randomized controlled trial with 233 participants, we examined human susceptibility to such manipulation in financial (e. g., purchases) and emotional (e. g., conflict resolution) decision-making contexts.
Scaling Wearable Foundation Models
Wearable sensors have become ubiquitous thanks to a variety of health tracking features.
BLADE: Benchmarking Language Model Agents for Data-Driven Science
To address these challenges, we present BLADE, a benchmark to automatically evaluate agents' multifaceted approaches to open-ended research questions.
Are Language Models Actually Useful for Time Series Forecasting?
Large language models (LLMs) are being applied to time series forecasting.
What Are the Odds? Language Models Are Capable of Probabilistic Reasoning
Language models (LM) are capable of remarkably complex linguistic tasks; however, numerical reasoning is an area in which they frequently struggle.
Transforming Wearable Data into Health Insights using Large Language Model Agents
Despite the proliferation of wearable health trackers and the importance of sleep and exercise to health, deriving actionable personalized insights from wearable data remains a challenge because doing so requires non-trivial open-ended analysis of these data.
Towards a Personal Health Large Language Model
Here we present Personal Health Large Language Model (PH-LLM), fine-tuned from Gemini for understanding and reasoning over numerical time-series personal health data.
Language Models Still Struggle to Zero-shot Reason about Time Series
But it remains unknown whether non-trivial forecasting implies that language models can reason about time series.
Correcting misinformation on social media with a large language model
High-quality and timely correction of misinformation that identifies and explains its (in)accuracies effectively reduces false beliefs.
LabelAId: Just-in-time AI Interventions for Improving Human Labeling Quality and Domain Knowledge in Crowdsourcing Systems
Crowdsourcing platforms have transformed distributed problem-solving, yet quality control remains a persistent challenge.
IMBUE: Improving Interpersonal Effectiveness through Simulation and Just-in-time Feedback with Human-Language Model Interaction
With IMBUE's additional just-in-time feedback, participants demonstrate 17% improvement in skill mastery, along with greater enhancements in self-efficacy (27% more) and reduction of negative emotions (16% more) compared to simulation-only.
A Roadmap to Pluralistic Alignment
We identify and formalize three possible ways to define and operationalize pluralism in AI systems: 1) Overton pluralistic models that present a spectrum of reasonable responses; 2) Steerably pluralistic models that can steer to reflect certain perspectives; and 3) Distributionally pluralistic models that are well-calibrated to a given population in distribution.
A Computational Framework for Behavioral Assessment of LLM Therapists
The emergence of large language models (LLMs) like ChatGPT has increased interest in their use as therapists to address mental health challenges and the widespread lack of access to care.
Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuring
In an IRB-approved randomized field study on a large mental health website with 15, 531 participants, we design and evaluate a system that uses language models to support people through various steps of cognitive restructuring.
Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction
In this paper, we conduct a human-centered study of how language models may assist people in reframing negative thoughts.
Scaling Expert Language Models with Unsupervised Domain Discovery
Large language models are typically trained densely: all parameters are updated with respect to all inputs.
GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization
We envision our multi-year datasets can support the ML community in developing generalizable longitudinal behavior modeling algorithms.
Gendered Mental Health Stigma in Masked Language Models
Mental health stigma prevents many individuals from receiving the appropriate care, and social psychology studies have shown that mental health tends to be overlooked in men.
Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models
New ELMs are learned by branching from (mixtures of) ELMs in the current set, further training the parameters on data for the new domain, and then merging the resulting model back into the set for future use.
Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets
Here, we introduce a neural architecture for multivariate time series classification designed to address these unique domain challenges.
Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support
Advances in artificial intelligence (AI) are enabling systems that augment and collaborate with humans to perform simple, mechanistic tasks like scheduling meetings and grammar-checking text.
Transformer-Based Behavioral Representation Learning Enables Transfer Learning for Mobile Sensing in Small Datasets
This architecture combines benefits from CNN and Trans-former architectures to (1) enable better prediction performance by learning directly from raw minute-level sensor data without the need for handcrafted features by up to 0. 33 ROC AUC, and (2) use pretraining to outperform simpler neural models and boosted decision trees with data from as few a dozen participants.
Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach
Learning such transformations is challenging and requires a deep understanding of empathy while maintaining conversation quality through text fluency and specificity to the conversational context.
Adjusting for Confounders with Text: Challenges and an Empirical Evaluation Framework for Causal Inference
Across 648 experiments and two datasets, we evaluate every commonly used causal inference method and identify their strengths and weaknesses to inform social media researchers seeking to use such methods, and guide future improvements.
A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support
We develop a novel unifying theoretically-grounded framework for characterizing the communication of empathy in text-based conversations.
CORAL: COde RepresentAtion Learning with Weakly-Supervised Transformers for Analyzing Data Analysis
Large scale analysis of source code, and in particular scientific source code, holds the promise of better understanding the data science process, identifying analytical best practices, and providing insights to the builders of scientific toolkits.
Population-Scale Study of Human Needs During the COVID-19 Pandemic: Analysis and Implications
Most work to date on mitigating the COVID-19 pandemic is focused urgently on biomedicine and epidemiology.
Boba: Authoring and Visualizing Multiverse Analyses
Multiverse analysis is an approach to data analysis in which all "reasonable" analytic decisions are evaluated in parallel and interpreted collectively, in order to foster robustness and transparency.
Human-Computer Interaction
The Effect of Moderation on Online Mental Health Conversations
We found that participation in group mental health discussions led to improvements in psychological perspective, and that these improvements were larger in moderated conversations.
Learning Individualized Cardiovascular Responses from Large-scale Wearable Sensors Data
We consider the problem of modeling cardiovascular responses to physical activity and sleep changes captured by wearable sensors in free living conditions.
Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health
Mental illness is one of the most pressing public health issues of our time.
Detection Bank: An Object Detection Based Video Representation for Multimedia Event Recognition
While low-level image features have proven to be effective representations for visual recognition tasks such as object recognition and scene classification, they are inadequate to capture complex semantic meaning required to solve high-level visual tasks such as multimedia event detection and recognition.
How to Ask for a Favor: A Case Study on the Success of Altruistic Requests
We present a case study of altruistic requests in an online community where all requests ask for the very same contribution and do not offer anything tangible in return, allowing us to disentangle what is requested from textual and social factors.
