arXiv:2601.04693v2 Announce Type: replace Abstract: Although negation is known to challenge large language models (LLMs), benchmarks for evaluating negation understanding-especially in Korean-are scarce. We conduct a corpus-based analysis of Korean negation and show that LLM performance degrades under negation. We then introduce Thunder-KoNUBench, a sentence-level negation understanding benchmark that reflects the empirical distribution of Korean negation phenomena. Evaluating 47 LLMs on Thunder-KoNUBench, we analyze the effects of model size and instruction tuning, and perform error analysis
The proliferation of LLMs creates an urgent need for robust evaluation benchmarks, particularly for nuanced linguistic phenomena like negation and in languages beyond English, driving the development of specialized tools like Thunder-KoNUBench.
This benchmark highlights a critical weakness in current LLMs regarding negation understanding, specifically in Korean, indicating a need for improved architectural design or training methodologies to prevent misinterpretation in real-world applications.
The availability of Thunder-KoNUBench provides a standardized tool to rigorously test and compare the negation understanding capabilities of different LLMs, fostering targeted advancements in multilingual AI.
- · Korean NLP researchers
- · Developers of Korean LLMs
- · Users of Korean AI applications
- · LLMs with poor negation understanding
- · AI applications reliant on precise linguistic parsing
Further research and development will focus on improving LLM performance on negation in Korean and other complex linguistic structures.
Enhanced Korean LLMs could lead to more reliable AI in critical applications like legal text analysis, medical diagnostics, or customer service.
Improved linguistic nuance in AI could reduce biases or errors associated with misinterpreting subtle aspects of human communication.
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Read at arXiv cs.CL