Is This Your Final Answer? Cross-Contextual Consistency as a Measure of LLM Credibility

2026-08-10

AI Summary

This work addresses the challenge of discerning whether large language models’ responses stem from stable internal beliefs or superficial pattern matching. To this end, the authors propose Cross-Context Consistency (C3) as a behavioral metric for model trustworthiness, evaluating response stability across perturbed contexts that preserve topic coherence while remaining content-neutral. Validated across 26 models and six task categories—including reasoning, factuality, and code generation—the study demonstrates that responses exhibiting greater invariance under such cross-context perturbations are more likely to be correct or factually grounded. Notably, C3 remains sensitive to subtle performance differences even when conventional benchmark scores plateau, offering a complementary dimension for assessing trustworthy AI systems.

Abstract

Large language models (LLMs) are powerful black-box systems, making it difficult to discern whether their answers reflect stable internal beliefs or superficial pattern matching. We identify cross-contextual consistency as an underutilized behavioral property of LLMs: a credible answer should remain stable when the same task is placed under topic-aligned, content-neutral contextual variation. Building on this intuition, we operationalize Cross-Contextual Consistency (C3) by comparing model generations under original and perturbed prompts. Across 26 models and six benchmarks spanning reasoning, factuality, and code generation, we find that answers with smaller cross-contextual shifts are more likely to be correct or factual. We demonstrate that C3 provides a complementary axis of evaluation and can serve as a benchmark usefulness diagnostic, identifying which portions of a benchmark remain informative even when aggregated scores are widely considered "saturate".

Problem

• large language models
• credibility
• cross-contextual consistency
• behavioral property
• evaluation

Innovation

• Cross-Contextual Consistency
• LLM Credibility
• Prompt Perturbation
• Behavioral Evaluation
• Benchmark Diagnostic