How Order-Sensitive Are LLMs? OrderProbe for Deterministic Structural Reconstruction

arXiv:2601.08626 · cs.CL · Submitted 2026-01-13 · Read on arXiv

cs.CL

Submitted: 2026-01-13

Updated: 2026-10-05

Comments: EMNLP 2026 Findings

Code: https://github.com/Zhaolu-K/OrderProbe

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Robustness — 46× in this paper · explained in 5 episodes
• Benchmark — 25× in this paper · explained in 2 episodes
• Accuracy — 24× in this paper · explained in 1 episode
• Consistency — 20× in this paper · explained in 3 episodes
• Hallucination — 11× in this paper · explained in 3 episodes
• Alignment — 8× in this paper · explained in 1 episode
• Fidelity — 8× in this paper · explained in 2 episodes
• Thinking mode — 8× in this paper · explained in 2 episodes
• Aggregation — 5× in this paper · explained in 1 episode
• Redundancy — 4× in this paper · explained in 2 episodes
• Semantic Understanding — 4× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Grounding — 3× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Structural Integrity — 3× in this paper · explained in 3 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• In-Context Learning — 2× in this paper · explained in 4 episodes
• Natural Language Inference — 2× in this paper
• Natural Language Inference (NLI) — 2× in this paper · explained in 2 episodes
• Semantic Similarity — 2× in this paper · explained in 2 episodes
• Structural Constraints — 2× in this paper · explained in 2 episodes
• Structural Reasoning — 2× in this paper · explained in 2 episodes
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Annotator Agreement — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Evaluation Dimensions — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Low-Resource Languages — 1× in this paper · explained in 4 episodes
• Maximum Degradation Amount — 1× in this paper
• Positional Bias — 1× in this paper · explained in 2 episodes
• Reasoning Traces — 1× in this paper · explained in 3 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Semantic Gap — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• DeepSeek-V3 Technical Report
• Chengyu-Bench: Benchmarking Large Language Models for Chinese Idiom Understanding and Use
• The Llama 3 Herd of Models
• GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
• RoTBench: A Multi-Level Benchmark for Evaluating the Robustness of Large Language Models in Tool Learning
• GPT-4 Technical Report
• Qwen2.5 Technical Report

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