ERD: A Framework for Improving LLM Reasoning for Cognitive Distortion Classification

Core Concepts

Improving cognitive distortion classification using ERD framework with LLMs.

Abstract

Abstract:

Large Language Models (LLMs) aid in psychotherapy accessibility.
ERD framework enhances cognitive distortion classification.
Introduction:

LLMs dominate machine learning, especially in medical applications.
CoT reasoning improves empathetic responses in conversational AI chatbots.
Challenges:

DoT method tends to overdiagnose cognitive distortions.
Performance limitations in multi-class setup hinder practical usage.
ERD Framework:

Extraction, Reasoning, and Debate steps improve classification performance.
Experimental results show significant enhancements in F1 scores and specificity.
Experiments:

ERD outperforms baselines with improved F1 scores and specificity.
Controlling judge behavior through different prompts influences performance.
Conclusion:

ERD framework effectively identifies cognitive distortions with LLMs.

Stats

Our experimental results on a public dataset show that ERD improves the multi-class F1 score as well as binary specificity score.
Table 1 shows that the multi-class F1 score increases more than 10% when the ground-truth distorted part is extracted before running DoT.
Adding Extraction module improves the distortion classification score by more than 9%, and adding Debate module not only improves the distortion classification score by around 7%, but also improves the distortion assessment specificity by more than 25%.

Quotes

Key Insights Distilled From

ERD

by Sehee Lim,Ye... at arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14255.pdf

Deeper Inquiries

How can the ERD framework be adapted for other applications beyond cognitive distortion classification?

The ERD framework, which stands for Extraction-Reasoning-Debate, can be adapted for various applications beyond cognitive distortion classification by modifying its components to suit different tasks.

Extraction: The extraction step in ERD can be tailored to identify specific elements relevant to the target application. For instance, in sentiment analysis, this step could focus on extracting emotional cues or key phrases indicative of sentiment from text inputs.

Reasoning: The reasoning module can be customized based on the requirements of the new application. For example, in medical diagnosis tasks, the reasoning process may involve analyzing symptoms and medical history to suggest potential illnesses.

Debate: The debate stage can incorporate multiple perspectives or models specialized for different aspects of the task at hand. In legal document analysis, this could involve contrasting interpretations of legal language by different agents within the debate process.

By adapting these components and integrating domain-specific knowledge into each stage of the ERD framework, it can effectively address a wide range of applications such as sentiment analysis, medical diagnosis support systems, legal document review tools, and more.

What are potential drawbacks or limitations of relying heavily on LLMs for psychotherapy support?

While Large Language Models (LLMs) offer significant advancements in psychotherapy support systems like cognitive behavioral therapy (CBT), there are several drawbacks and limitations to consider:

Lack of Emotional Intelligence: LLMs may struggle with understanding nuanced emotions and providing empathetic responses crucial in therapeutic settings.

Ethical Concerns: Privacy issues arise when sensitive patient data is processed by LLMs without robust privacy measures.

Overreliance on Technology: Excessive dependence on LLMs may reduce human interaction essential for building trust between patients and therapists.

Bias Amplification: If not properly trained or monitored, LLMs might perpetuate biases present in training data affecting treatment outcomes.

Limited Contextual Understanding: LLMs may misinterpret context leading to inaccurate assessments or advice during therapy sessions.

Addressing these limitations requires careful consideration when implementing LLM-based solutions in psychotherapy settings.

How might incorporating human feedback into the debate process impact the effectiveness of the ERD framework?

Incorporating human feedback into the debate process within the ERD framework could enhance its effectiveness through several mechanisms:

Validation Mechanism: Human feedback acts as a validation mechanism ensuring that decisions made during debates align with expert judgment improving overall accuracy.

2 .Bias Correction: Humans provide corrective input if they notice biases or errors made by AI agents during debates helping refine decision-making processes.
3 .Contextual Understanding: Human feedback offers insights into contextual nuances that AI agents might overlook enhancing comprehension and decision-making capabilities.
4 .Continuous Learning: Feedback loops enable continuous learning allowing AI agents to adapt based on real-world scenarios improving performance over time.
5 .Trust Building: Incorporating human feedback fosters trust among users who feel reassured knowing that their interactions are overseen by both AI and human experts promoting acceptance and engagement with AI-driven systems.
Overall , integrating human feedback ensures a balanced approach combining machine intelligence with human expertise resulting in more reliable outcomes across various applications including psychotherapy support provided by frameworks like ERD..