AGENTIC-SYSTEMSMarch 20, 20261 min read24 views

TherapyGym: Evaluating and Aligning Clinical Fidelity and Safety in Therapy Chatbots

Researchers have introduced TherapyGym, a groundbreaking framework to evaluate and improve mental health chatbots based on clinical fidelity and safety. This tool promises to standardize the development of virtual therapy assistants according to evidence-based practices.

Computer Science > Computation and Language

Title:TherapyGym: Evaluating and Aligning Clinical Fidelity and Safety in Therapy Chatbots

Large language models (LLMs) are increasingly used for mental-health support; yet prevailing evaluation methods--fluency metrics, preference tests, and generic dialogue benchmarks--fail to capture the clinically critical dimensions of psychotherapy. We introduce THERAPYGYM, a framework that evaluates and improves therapy chatbots along two clinical pillars: fidelity and safety. Fidelity is measured using the Cognitive Therapy Rating Scale (CTRS), implemented as an automated pipeline that scores adherence to CBT techniques over multi-turn sessions. Safety is assessed using a multi-label annotation scheme, covering therapy-specific risks (e.g., failing to address harm or abuse). To mitigate bias and unreliability in LLM-based judges, we further release THERAPYJUDGEBENCH, a validation set of 116 dialogues with 1,270 expert ratings for auditing and calibration against licensed clinicians. THERAPYGYM also serves as a training harness: CTRS and safety-based rewards drive RL with configurable patient simulations spanning diverse symptom profiles. Models trained in THERAPYGYM improve on expert ratings, with average CTRS rising from 0.10 to 0.60 (and 0.16 to 0.59 under LLM judges). Our work enables scalable development of therapy chatbots that are faithful to evidence-based practice and safer in high-stakes use.

Source: arXiv cs.AI Recent

More in this category

agentic-systems

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference

Researchers have introduced Probabilistic Concept-Aware Steering (PCS), an inference-time intervention framework for LLMs. PCS provides fine-grained, safety-oriented semantic steering while preserving original task competence.

agentic-systems

S2T-RLHF: Hierarchical Credit Assignment for Stable Preference-Based RLHF

Researchers introduced S2T-RLHF, a hierarchical credit assignment framework that stabilizes preference-based RLHF training by decomposing response-level rewards into sentence and token levels.

agentic-systems

ProbSPARQL: Querying Knowledge Graphs with Multi-dimensional, Uncertain Numeric Data

Researchers have introduced ProbSPARQL, an upward-compatible SPARQL extension designed to query multi-dimensional and uncertain numeric measurement data within Knowledge Graphs, providing significant query performance gains for complex industrial applications.

agentic-systems

MUX: Continuous Reasoning via Multiplexed Tokens

Researchers introduced MUX, a novel latent reasoning method that compresses discrete text-based reasoning steps into continuous multiplexed tokens. By enabling lossless superposition, MUX significantly boosts LLM reasoning efficiency and speed across complex problem-solving tasks.

agentic-systems

FindStatBench: Evaluating Large Language Models on Combinatorial Code Synthesis

Researchers have introduced FindStatBench, a new execution benchmark containing 2,329 tasks designed to evaluate the combinatorial code synthesis capabilities of large language models.

NOW LET US Related – Cross-Dialect Generalization Without Retraining: Benchmarks and Evaluation of Schema-Derived Constrained Decoding for MLIR

agentic-systems

Cross-Dialect Generalization Without Retraining: Benchmarks and Evaluation of Schema-Derived Constrained Decoding for MLIR

Researchers introduced schema-derived constrained decoding for MLIR, enabling a 1.7B small language model to match or beat 15B-34B LLMs without retraining, while generating code 8x-25x faster.