The demand for conversational agents that provide mental health care is
consistently increasing. In this work, we develop a psychological counseling
agent, referred to as CoCoA, that applies Cognitive Behavioral Therapy (CBT)
techniques to identify and address cognitive distortions inherent in the
client's statements. Specifically, we construct a memory system to efficiently
manage information necessary for counseling while extracting high-level
insights about the client from their utterances. Additionally, to ensure that
the counseling agent generates appropriate responses, we introduce dynamic
prompting to flexibly apply CBT techniques and facilitate the appropriate
retrieval of information. We conducted dialogues between CoCoA and characters
from Character.ai, creating a dataset for evaluation. Then, we asked GPT to
evaluate the constructed counseling dataset, and our model demonstrated a
statistically significant difference from other models.

我们开发了一种心理咨询代理，称为 CoCoA，应用认知行为治疗技术来识别和解决客户言论中的认知失真，通过构建一个内存系统来高效管理与咨询相关的信息并从客户的话语中提取高级见解，并引入动态提示来确保咨询代理生成适当的回应。

COCOA: 基于 CBT 的对话辅导代理，专注于认知偏差和动态提示

COCOA: CBT-based Conversational Counseling Agent using Memory  Specialized in Cognitive Distortions and Dynamic Prompt

It has been demonstrated that prompt tuning is highly effective in
efficiently eliciting knowledge from language models (LMs). However, the prompt
tuning still lags behind fine-tuning, especially when the LMs are small.
P-tuning v2 (Liu et al., 2021b) makes it comparable with finetuning by adding
continuous prompts for every layer of the pre-trained model. However,
prepending fixed soft prompts for all instances, regardless of their
discrepancy, is doubtful. In particular, the inserted prompt position, length,
and the representations of prompts for diversified instances through different
tasks could all affect the prompt tuning performance. To fill this gap, we
propose dynamic prompting (DP): the position, length, and prompt representation
can all be dynamically optimized with respect to different tasks and instances.
We conduct comprehensive experiments on the SuperGlue benchmark to validate our
hypothesis and demonstrate substantial improvements. We also derive a unified
framework for supporting our dynamic prompting strategy. In particular, we use
a simple learning network and Gumble- Softmax for learning instance-dependent
guidance. Experimental results show that simple instance-level position-aware
soft prompts can improve the classification accuracy of up to 6 points on
average on five datasets, reducing its gap with fine-tuning. Besides, we also
prove its universal usefulness under full-data, few-shot, and multitask
regimes. Combining them together can even further unleash the power of DP,
narrowing the distance between finetuning.

本文提出了一种动态提示策略 (DP) 来优化 LMs 的 prompt tuning，通过任务优化、位置、长度和提示表示的动态优化，实验证明 DP 能提高分类准确度，并证明其在全数据、少样本和多任务情况下都是有用的。