Not What You Meant: Can LLMs Follow a Specified Negation Semantics?

arXiv:2609.27517 · cs.AI · Submitted 2026-09-23 · Read on arXiv

cs.AI

Submitted: 2026-09-23

Updated: 2026-09-23

Code: https://github.com/14H034160212/NAFBench

Terminology

• Trace — 42× in this paper · explained in 2 episodes
• Accuracy — 41× in this paper · explained in 1 episode
• Benchmark — 13× in this paper · explained in 2 episodes
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Grounding — 4× in this paper · explained in 3 episodes
• Abstention — 3× in this paper · explained in 1 episode
• Verification — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 1× in this paper
• International Conference on Machine Learning — 1× in this paper
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• LoRA Fine-Tuning — 1× in this paper · explained in 3 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Conflict-Aware Fusion: Mitigating Logic Inertia in Large Language Models via Structured Cognitive Priors
• RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization
• AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions
• PrologMCP: A Standardized Prolog Tool Interface for LLM Agents
• A negation detection assessment of GPTs: analysis with the xNot360 dataset

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