# ConvCounsel: A Conversational Dataset for Student Counseling

arXiv: https://arxiv.org/abs/2411.00604 · HTML: https://mlsanity.com/paper/2411.00604/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 1 Nov 2024.

## Builds on (most cited first, 3 of 3)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Towards Emotional Support Dialog Systems](https://mlsanity.com/paper/2106.01144/) (2021)

## Built on by (most cited first, 4 of 4)

- [MHQA: A Diverse, Knowledge Intensive Mental Health Question Answering Challenge for Language Models](https://mlsanity.com/paper/2502.15418/) (2025)
- [Dialogue is Better Than Monologue: Instructing Medical LLMs via Strategical Conversations](https://mlsanity.com/paper/2501.17860/) (2025)
- [OMIND: Framework for Knowledge Grounded Finetuning and Multi-Turn Dialogue Benchmark for Mental Health LLMs](https://mlsanity.com/paper/2603.25105/) (2026)
- [Psy-Chronicle:A Structured Pipeline for Synthesizing Long-Horizon Campus Psychological Counseling Dialogues](https://mlsanity.com/paper/2605.22140/) (2026)

## Abstract (arXiv)

Student mental health is a sensitive issue that necessitates special attention. A primary concern is the student-to-counselor ratio, which surpasses the recommended standard of 250:1 in most universities. This imbalance results in extended waiting periods for in-person consultations, which cause suboptimal treatment. Significant efforts have been directed toward developing mental health dialogue systems utilizing the existing open-source mental health-related datasets. However, currently available datasets either discuss general topics or various strategies that may not be viable for direct application due to numerous ethical constraints inherent in this research domain. To address this issue, this paper introduces a specialized mental health dataset that emphasizes the active listening strategy employed in conversation for counseling, also named as ConvCounsel. This dataset comprises both speech and text data, which can facilitate the development of a reliable pipeline for mental health dialogue systems. To demonstrate the utility of the proposed dataset, this paper also presents the NYCUKA, a spoken mental health dialogue system that is designed by using the ConvCounsel dataset. The results show the merit of using this dataset.