mlsanityData to 14 Oct 2026

Trust Modeling in Counseling Conversations: A Benchmark Study

arXiv 2501.03064 ↗First posted 6 Jan 2025cs.CLcited by 5

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Abstract (arXiv)

In mental health counseling, a variety of earlier studies have focused on dialogue modeling. However, most of these studies give limited to no emphasis on the quality of interaction between a patient and a therapist. The therapeutic bond between a patient and a therapist directly correlates with effective mental health counseling. It involves developing the patient's trust on the therapist over the course of counseling. To assess the therapeutic bond in counseling, we introduce trust as a therapist-assistive metric. Our definition of trust involves patients' willingness and openness to express themselves and, consequently, receive better care. We conceptualize it as a dynamic trajectory observable through textual interactions during the counseling. To facilitate trust modeling, we present MENTAL-TRUST, a novel counseling dataset comprising manual annotation of 212 counseling sessions with first-of-its-kind seven expert-verified ordinal trust levels. We project our problem statement as an ordinal classification task for trust quantification and propose a new benchmark, TrustBench, comprising a suite of classical and state-of-the-art language models on MENTAL-TRUST. We evaluate the performance across a suite of metrics and lay out an exhaustive set of findings. Our study aims to unfold how trust evolves in therapeutic interactions.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 17 of its 42 references that are in the map. First posted on arXiv 6 Jan 2025.

Builds on

17 of 42 references.

References · 17

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  5. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
  6. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
  7. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
  8. 2024Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phonecited by 1,917
  9. 2021MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcarecited by 161
  10. 2021Speaker and Time-aware Joint Contextual Learning for Dialogue-act Classification in Counselling Conversationscited by 31
  11. 2022Counseling Summarization using Mental Health Knowledge Guided Utterance Filteringcited by 18
  12. 2024Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: A Benchmark Studycited by 13
5 more
  1. 2023MEDIC: A Multimodal Empathy Dataset in Counselingcited by 12
  2. 2024Knowledge Planning in Large Language Models for Domain-Aligned Counseling Summarizationcited by 11
  3. 2024Benchmarking GPT-4 on Algorithmic Problems: A Systematic Evaluation of Prompting Strategiescited by 9
  4. 2023Critical Behavioral Traits Foster Peer Engagement in Online Mental Health Communitiescited by 7
  5. 2024CUEMPATHY: A Counseling Speech Dataset for Psychotherapy Researchcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.