# SocialLM: Social Signal Processing of Patient-Provider Communication using LLMs and Contextual Aggregation

arXiv: https://arxiv.org/abs/2505.04152 · HTML: https://mlsanity.com/paper/2505.04152/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 52 references that are in the map.
- First posted on arXiv 7 May 2025.

## Builds on (most cited first, 11 of 11)

- [pyannote.audio: neural building blocks for speaker diarization](https://mlsanity.com/paper/1911.01255/) (2019)
- [Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness](https://mlsanity.com/paper/2308.16175/) (2023)
- [A Survey of Uncertainty Estimation in LLMs: Theory Meets Practice](https://mlsanity.com/paper/2410.15326/) (2024)
- [Modeling Motivational Interviewing Strategies On An Online Peer-to-Peer Counseling Platform](https://mlsanity.com/paper/2211.05182/) (2022)
- [Scaffolding Empathy: Training Counselors with Simulated Patients and Utterance-level Performance Visualizations](https://mlsanity.com/paper/2502.18673/) (2025)
- [Assessing Empathy in Large Language Models with Real-World Physician-Patient Interactions](https://mlsanity.com/paper/2405.16402/) (2024)
- [Affect Recognition in Conversations Using Large Language Models](https://mlsanity.com/paper/2309.12881/) (2023)
- [CommSense: A Wearable Sensing Computational Framework for Evaluating Patient-Clinician Interactions](https://mlsanity.com/paper/2407.08143/) (2024)
- [Detecting PTSD in Clinical Interviews: A Comparative Analysis of NLP Methods and Large Language Models](https://mlsanity.com/paper/2504.01216/) (2025)
- [Predicting Trust In Autonomous Vehicles: Modeling Young Adult Psychosocial Traits, Risk-Benefit Attitudes, And Driving Factors With Machine Learning](https://mlsanity.com/paper/2409.08980/) (2024)
- [Toward Automated Detection of Biased Social Signals from the Content of Clinical Conversations](https://mlsanity.com/paper/2407.17477/) (2024)

## Built on by (most cited first, 1 of 1)

- [Depression Detection at the Point of Care: Automated Analysis of Linguistic Signals from Routine Primary Care Encounters](https://mlsanity.com/paper/2604.06193/) (2026)

## Abstract (arXiv)

Effective patient-provider communication is difficult to assess at scale. We examine whether large language models (LLMs) can track 20 social behaviors from clinical transcripts without fine-tuning. Across three model families and multiple prompting strategies, LLMs reliably detect social signals, though performance varies by patient race and visit segment. To address this variability under query-only API constraints, we introduce an agreement-weighted ensemble using group-level agreement patterns. This approach improves both accuracy and stability over the best individual model, demonstrating a practical pathway for scalable social signal tracking in clinical conversations.