ConsistencyChecker: Tree-based Evaluation of LLM Generalization Capabilities

2025-06-14

AI Summary

To address the challenge of evaluating semantic and functional consistency in multi-step human–LLM interactions, this paper proposes a tree-based unsupervised evaluation framework. It models textual state evolution as a sequence of reversible transformations—such as machine translation or AI-assisted programming—and constructs a directed tree where edges correspond to forward and inverse operations. Consistency is quantified in a zero-shot, data-leakage-free manner via dynamically generated LLM-based reference trajectories and cross-depth similarity aggregation. The core contribution is the first tree-structured metric grounded in invertible operations, eliminating reliance on supervised paired data. Evaluated across eight LLMs spanning diverse architectures and scales, the framework demonstrates strong discriminative capability: its consistency scores correlate highly (r > 0.7) with WMT 2024 automatic rankings, validating its generalizability and reliability.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

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

Abstract

Evaluating consistency in large language models (LLMs) is crucial for ensuring reliability, particularly in complex, multi-step interactions between humans and LLMs. Traditional self-consistency methods often miss subtle semantic changes in natural language and functional shifts in code or equations, which can accumulate over multiple transformations. To address this, we propose ConsistencyChecker, a tree-based evaluation framework designed to measure consistency through sequences of reversible transformations, including machine translation tasks and AI-assisted programming tasks. In our framework, nodes represent distinct text states, while edges correspond to pairs of inverse operations. Dynamic and LLM-generated benchmarks ensure a fair assessment of the model's generalization ability and eliminate benchmark leakage. Consistency is quantified based on similarity across different depths of the transformation tree. Experiments on eight models from various families and sizes show that ConsistencyChecker can distinguish the performance of different models. Notably, our consistency scores-computed entirely without using WMT paired data-correlate strongly (r>0.7) with WMT 2024 auto-ranking, demonstrating the validity of our benchmark-free approach. Our implementation is available at: https://github.com/ulab-uiuc/consistencychecker.

Problem

• Evaluating LLM consistency in multi-step interactions
• Detecting semantic and functional shifts in transformations
• Assessing generalization without benchmark leakage

Innovation

• Tree-based framework for LLM consistency evaluation
• Reversible transformations measure semantic and functional shifts
• Dynamic benchmarks prevent data leakage