The Compositional Architecture of Regret in Large Language Models

2025-06-18

AI Summary

This study systematically investigates, for the first time, explicit “regret” expressions—where large language models (LLMs) explicitly acknowledge their own erroneous outputs. Addressing three key challenges—lack of annotated regret datasets, absence of principled criteria for identifying optimal representation layers, and insufficient metrics for neuron functional analysis—the work: (1) constructs the first high-quality, multi-scenario regret expression dataset; (2) proposes the Supervised Compression-Decoupling Index (S-CDI) to identify the optimal regret-representation layer, revealing a cross-layer “M-shaped” information decoupling pattern; and (3) introduces the Regret-Dominant Score (RDS) and Group Influence Coefficient (GIC), enabling three-way functional categorization of neurons and uncovering a compositional neural architecture for regret. Experiments demonstrate significant improvements in probe-based classification accuracy and establish an interpretable foundation for modeling LLM self-reflection mechanisms.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Regret in Large Language Models refers to their explicit regret expression when presented with evidence contradicting their previously generated misinformation. Studying the regret mechanism is crucial for enhancing model reliability and helps in revealing how cognition is coded in neural networks. To understand this mechanism, we need to first identify regret expressions in model outputs, then analyze their internal representation. This analysis requires examining the model's hidden states, where information processing occurs at the neuron level. However, this faces three key challenges: (1) the absence of specialized datasets capturing regret expressions, (2) the lack of metrics to find the optimal regret representation layer, and (3) the lack of metrics for identifying and analyzing regret neurons. Addressing these limitations, we propose: (1) a workflow for constructing a comprehensive regret dataset through strategically designed prompting scenarios, (2) the Supervised Compression-Decoupling Index (S-CDI) metric to identify optimal regret representation layers, and (3) the Regret Dominance Score (RDS) metric to identify regret neurons and the Group Impact Coefficient (GIC) to analyze activation patterns. Our experimental results successfully identified the optimal regret representation layer using the S-CDI metric, which significantly enhanced performance in probe classification experiments. Additionally, we discovered an M-shaped decoupling pattern across model layers, revealing how information processing alternates between coupling and decoupling phases. Through the RDS metric, we categorized neurons into three distinct functional groups: regret neurons, non-regret neurons, and dual neurons.

Problem

• Identify regret expressions in LLM outputs for reliability enhancement
• Develop metrics to analyze regret representation layers and neurons
• Understand cognitive coding in neural networks via regret mechanisms

Innovation

• Constructing regret dataset via strategic prompting scenarios
• Identifying regret layers with Supervised Compression-Decoupling Index
• Categorizing neurons using Regret Dominance Score metric