Thunder-KoNUBench: A Corpus-Aligned Benchmark for Korean Negation Understanding

arXiv:2601.04693 · cs.CL · Submitted 2026-01-08 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "Thunder-KoNUBench: A Corpus-Aligned Benchmark for Korean Negation Understanding".

Jane: Negation understanding in Korean remains an under-explored area, and this research introduces Thunder-KoNUBench, a new sentence-level benchmark designed to systematically evaluate how large language models handle negation phenomena in Korean.

Tom: First, who's behind it and why it matters.

Title and authors: Tom: Now we’re going to look closer at what the paper actually summarizes, which really lays out the core contribution of Thunder-KoNUBench. Essentially, they start by conducting a corpus-based analysis of Korean negation to map out its various characteristics and distribution across different sentence types.

Jane: That initial analysis is key because it shows that negation isn't uniform; it’s distributed differently depending on whether you look at adnominal, adverbial, or main clauses.

Lu: They identify major negation types within the corpus like "안 계열," "못 계열," and "말다," which helps researchers pinpoint exactly what linguistic phenomena are most prevalent in Korean usage.

Meng: From a practical standpoint, knowing these specific types gives engineers a better idea of the edge cases they need to train the AI on when dealing with Korean text.

Lalam: And then they move on to defining standard negation as a recursive operation applied to the logical structure among main clauses, which is fundamentally different from local negation.

Tom: That definition of standard negation as a recursive operation that reverses the truth value all the way down to atomic propositions is what gives this benchmark its theoretical weight.

Jane: And local negation, which only provides a partial negation of the overall meaning, is then classified based on Korean sentence structures into categories like noun clauses or subordinate clauses.

Lu: This systematic separation between the global logical operation and the local application provides a very clear taxonomy for understanding Korean negation, which is very helpful for researchers.

Meng: I wonder how this classification helps us move toward building systems that can handle complex reasoning, rather than just surface-level pattern matching.

Lalam: And then they construct Thunder-KoNUBench, which is a multiple-choice benchmark with four thousand seven hundred eighty-four instances where each sentence is paired with options for standard negation, local negation, contradiction, and paraphrase.

Tom: That structure of the benchmark itself—using those four distinct options—is what makes it a comprehensive test of a model's ability to distinguish between these different types of linguistic operations.

Jane: So, in short, the paper summarizes that they created a corpus-aligned benchmark to systematically evaluate how LLMs handle the empirical distribution of Korean negation phenomena.

Lu: This summary really highlights that the work bridges deep linguistic analysis with practical evaluation metrics for AI systems working in Korean.

Meng: It’s about grounding the abstract concept of logical negation in real-world Korean text, which is what we need to build reliable applications on top of.

The paper's summary: Tom: Next, let’s talk about the specific improvements the authors suggested for this benchmark and what those mean for future work. They didn't just stop at creating a test set; they pointed out how to make it better.

Jane: The key improvement suggested is moving toward supervised finetuning on Thunder-KoNUBench, and they found that this actually enhances the models’ contextual understanding in Korean.

Lu: They also highlighted a critical finding that cloze-style supervision is more effective than symbol-style supervision when learning sentence-level negation.

Meng: That distinction between the two supervision styles is really important for our engineering pipeline because it tells us which training format yields better results for this specific task.

Lalam: And they also suggested using Low-Rank Adaptation during fine-tuning to ensure parameter efficiency without causing catastrophic forgetting during the process.

Tom: So, that’s a three-pronged improvement: better supervision style, a specific training technique like LoRA, and the use of this benchmark itself to improve comprehension.

Jane: From an application standpoint, if we can achieve better contextual understanding through this method, it means our AI systems will be much better equipped to handle complex Korean sentences in real-world scenarios.

Lu: The implication is that models won't just memorize negation markers; they’ll develop a more robust way to reason about the logical relationships between clauses.

Meng: I see this as a pathway to build systems that are less brittle when encountering unexpected or complex Korean phrasing, which is exactly what we need for deployment.

Lalam: The paper also concludes by stating that Thunder-KoNUBench is publicly available to support transparent and reproducible research, which is a huge step for the community.

Tom: So, essentially, the improvements focus on creating a feedback loop where we use this benchmark to train models more effectively in Korean.

Jane: It’s about providing the right kind of supervision so that the AI learns the underlying logic rather than just superficial linguistic patterns.

The paper's improvements: Tom: Alright team, we’ve covered a lot on Thunder-KoNUBench, and I think it’s time to wrap up by summarizing the main points and thinking about what this means for us going forward. We established that LLMs definitely hit performance degradation when required to reason with negation in Korean.

Jane: And the core message is that this benchmark provides a corpus-aligned dataset that accurately reflects the distribution of these complex linguistic features, giving us something much more representative than previous tests.

Lu: The systematic analysis of negation types and the way they define standard versus local negation gives us a solid theoretical framework to guide our understanding of Korean syntax.

Meng: From an engineering perspective, the findings show that we can improve contextual understanding in Korean by using supervised finetuning on this specific dataset with cloze supervision.

Lalam: Ultimately, this work gives us a concrete way to support transparent and reproducible research in Korean NLP, which is vital for building reliable systems.

Tom: So, to summarize the implication for the broader field: we need better evaluation tools that specifically target negation understanding in Korean because current LLMs show clear weaknesses there.

Jane: And moving forward, using Thunder-KoNUBench for fine-tuning with cloze supervision seems like a very effective strategy for boosting those comprehension capabilities.

Lu: The work confirms that focusing on the recursive logical structure of standard negation is the correct theoretical path forward for modeling these linguistic operations.

Meng: For us in development, this means prioritizing training methods that give models better context and less reliance on simple surface markers.

Lalam: I think the long-term impact is making Korean language processing more robust and capable of handling nuanced discourse, which really advances AI's ability to interact with the language naturally.

Conclusion: Tom: So we've spent some time breaking down Thunder-KoNUBench, which is that new sentence-level benchmark designed to systematically test how large language models handle negation in Korean, and the main takeaway is that these models definitely struggle when it comes to reasoning with Korean negation.

Jane: Exactly. The authors showed us that this isn't just about spotting a word; it’s about understanding the deep logical structure of how negation works across different parts of a sentence, which is what makes this benchmark so important for teaching AI proper reasoning skills.

Lu: I find the way they categorized standard negation as a recursive operation really fascinating; it gives us a structural map to understand the underlying logic of Korean syntax, which is something we can use to design next-generation reasoning architectures.

Meng: From a practical standpoint, I’m really interested in how this testing informs our training pipelines; knowing that supervised finetuning on this specific data enhances contextual understanding is a very useful piece of engineering advice.

Lalam: And for me, the implications are huge because if we can train AI models to handle this kind of structural reasoning accurately, it means we could build applications that interact with Korean language on a much more reliable and nuanced level.

Tom: It really does sound like a significant step forward for Korean NLP, and I'm genuinely excited about the potential for more robust AI in this language.

Jane: I agree, and it’s inspiring to see how meticulous the authors were in constructing such a detailed corpus-aligned dataset that reflects real usage.

Lu: The future potential here is massive; imagine AI systems that don't just translate words but truly understand the logical implications of negation in complex Korean discourse, which opens up so many creative possibilities.

Meng: I hope we can see this kind of deep understanding translate into more reliable applications, because right now, surface-level cues are often enough for simple tasks.

Lalam: And if we succeed in this, it could mean our AI systems become much more capable of navigating and interacting with the culture through language in a way that feels genuinely intelligent and consistent.

Tom: So, to wrap up, Thunder-KoNUBench gives us the empirical evidence we need to push AI models past simple pattern matching and into true contextual reasoning with Korean negation.

Jane: It’s a powerful tool for anyone looking to build better Korean language comprehension tools, and I think we should all look at how this structure helps us refine our own models.

Lu: We've got so much more to explore with these kinds of structural benchmarks, and I can already see a whole new way to approach world modeling in Korean.

Meng: For me, the immediate focus is on making sure our engineering teams are ready to implement these supervised training strategies efficiently once we adopt this benchmark.

Lalam: And I believe that by improving how AI understands these fundamental logical operations, we're not just fixing a linguistic problem, but enhancing the overall way AI can be used to understand and navigate our culture.

Tom: We've got a lot of fascinating stuff to chew on today with Thunder-KoNUBench, but next time we’ll be diving into those optimization risk bounds for Kolmogorov-Arnold Networks!

Sungmok Jung, Yeonkyoung So, Joonhak Lee, Sangho Kim, Yelim Ahn, Jaejin Lee

Graduate School of Data Science, Seoul National University

cs.CL

Submitted: 2026-01-08

Updated: 2026-06-28

Comments: Accepted to Findings of ACL 2026

Journal ref: Findings of the Association for Computational Linguistics: ACL 2026

DOI: 10.18653/v1/2026.findings-acl.324

Code: https://github.com/mcrl/Thunder-KoNUBenchhttps:

License: http://creativecommons.org/licenses/by-nc-sa/4.0/

Importance score: 83/100

The gist: Negation understanding in Korean remains an under-explored area, and this research introduces Thunder-KoNUBench, a new sentence-level benchmark designed to systematically evaluate how large language

Key concepts

Standard Negation: This is the main form of negation defined as a recursive operation applied to the logical structure among main clauses. It involves negating the logical relationship between sentences and applying this rule repeatedly until only basic propositions remain, ensuring deep semantic understanding.
Local Negation: Local negation refers to partial negation that does not change the overall meaning of a sentence. This type of negation is categorized based on Korean sentence structure, such as noun clauses or subordinate clauses, and is distinct from standard negation.
Thunder-KoNUBench: This is a multiple-choice benchmark with 4,784 sentences where each original sentence is paired with four options: standard negation, local negation, contradiction, and paraphrase. It was constructed to systematically evaluate LLMs' ability to distinguish between these types of Korean negation.
Performance Degradation: The study observed that larger models sometimes show a temporary decline in performance when reasoning about Korean negation. This suggests that the current complexity of language representation might not yet match the level of linguistic supervision required for accurate negation tasks.

Terminology

Benchmark — 45× in this paper · explained in 2 episodes
Fine-Tuning — 27× in this paper · explained in 5 episodes
Instruction Tuning — 10× in this paper · explained in 2 episodes
Contextual Understanding — 7× in this paper · explained in 2 episodes
Accuracy — 6× in this paper · explained in 1 episode
Adaptation — 5× in this paper · explained in 1 episode
Core — 4× in this paper · explained in 2 episodes
Low-Resource Languages — 4× in this paper · explained in 4 episodes
Actor — 2× in this paper · explained in 3 episodes
Annotator Agreement — 2× in this paper · explained in 1 episode
Catastrophic Forgetting — 2× in this paper · explained in 5 episodes
Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
Robustness — 2× in this paper · explained in 5 episodes
Semantic Understanding — 2× in this paper · explained in 2 episodes
Verification — 2× in this paper · explained in 2 episodes
Downstream Tasks — 1× in this paper · explained in 2 episodes
Foundation Model — 1× in this paper · explained in 3 episodes
Grounding — 1× in this paper · explained in 3 episodes
Institute of Computer Technology — 1× in this paper
Meta-learning — 1× in this paper · explained in 2 episodes
Multiple-Choice Question Answering — 1× in this paper
Parameter Efficiency — 1× in this paper · explained in 3 episodes
Structural Reasoning — 1× in this paper · explained in 2 episodes
Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
Transferability — 1× in this paper · explained in 4 episodes
Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes
alpha — 1× in this paper · explained in 1 episode

Summary

Negation understanding in Korean remains an under-explored area, and this research introduces Thunder-KoNUBench, a new sentence-level benchmark designed to systematically evaluate how large language models handle negation phenomena in Korean. This work is significant because it demonstrates that LLMs encounter performance degradation when required to reason with negation in Korean, and it provides a corpus-aligned dataset that reflects the empirical distribution of these complex linguistic features.

Corpus Analysis and Negation Characteristics

The study begins with a corpusbased analysis of Korean negation to examine its characteristics and distribution. The authors analyze the statistical properties of negation using a large-scale corpus from the OpenAI Dataset Project, identifying major negation types such as 안 계열 (an type), 못 계열 (mot type), and 말다 (malda). They segment sentences and manually verify negative sentences, finding that approximately 10.7% of the Korean corpus consists of negative sentences. The analysis further details the distribution of syntactic negation in Korean, showing how negation appears across various clause types, including Adnominal Clause, Adverbial Clause, and Main Clause.

Definition and Typology of Negation in Korean

The paper systematically defines standard negation as a recursive operation applied to the logical structure among main clauses, distinct from local negation. Standard negation is defined by negating the logical relationship between main clauses and recursively applying it to each clause until only atomic propositions remain. Local negation refers to partial negation that does not reverse the overall sentence meaning, which is classified based on Korean sentence structures into categories such as Noun clauses, Adnominal clauses, Subordinate clauses, and Coordinated sentences with local negation.

Construction of Thunder-KoNUBench

Thunder-KoNUBench is a multiple-choice benchmark consisting of 4,784 instances. Each original sentence is paired with four options: standard negation, local negation, contradiction, and paraphrase. The construction process involves three main stages:

Pre-processing of original sentences by crawling Korean Wikipedia and merging sentence pairs using the GPT-4.1 mini model to avoid overly simplistic source sentences.
Generation of choices where standard negation and local negation are created manually by the authors to ensure accuracy, while contradiction and paraphrase options are initially generated using the OpenAI API and refined through a thorough review process.
A rigorous review process involving independent task allocation, cross-checking among authors, and consensus-building meetings to ensure linguistic correctness and semantic validity.

Experimental Evaluation of LLMs

The researchers evaluate 47 large language models (18 Korean models and 29 non-Korean models) on Thunder-KoNUBench using the LM Evaluation Harness in two settings: cloze and symbol. The experiments analyze the effects of model size and instruction tuning. Key findings include:

within each model family, larger models tend to achieve better performance.

The authors observe a non-monotonic trend regarding model size, noting a noticeable slowdown or even a temporary decline in performance, especially among models with 8 to 12 billion parameters, suggesting a mismatch between emerging representational complexity and the level of linguistic supervision needed. Furthermore, instruction tuning often degrades performance in the cloze setting for non-Korean models, suggesting it may exacerbate the curse of multilinguality.

Error Analysis and Fine-Tuning Effects

Error analysis reveals that in the cloze setting, more than 90% of the errors are concentrated on local negation options, indicating a reliance on surface-level negation cues rather than true semantic understanding. The paper also investigates fine-tuning strategies:

supervised finetuning on Thunder-KoNUBench enhances the models’ contextual understanding in Korean.

The results show that clozebased format provides more effective supervision than symbol-based alternatives for improving negation understanding, as cloze-style fine-tuning yields larger improvements than symbol-based fine-tuning. The study concludes by stating that Thunder-KoNUBench is publicly available to support transparent and reproducible research.

Limitations of the Benchmark

The paper acknowledges limitations, noting that while it includes lexical negation through complementary antonyms, it does not evaluate negation in more naturalistic settings where negation appears in natural contexts. The design focuses on capturing the fundamental operation that maps a proposition P to its counterpart ¬P, rather than evaluating how models comprehend and reason over negation in context.

The gist

Thunder-KoNUBench is a sentence-level multiple-choice benchmark that reflects the empirical distribution of Korean negation phenomena, demonstrating that LLMs encounter performance degradation when required to reason with negation in Korean.

Improvements for AI systems

Here are specific, actionable improvements for AI systems derived from the findings in Thunder-KoNUBench: A Corpus-Aligned Benchmark for Korean Negation Understanding, along with what these improved systems can achieve.

)Specific Improvements and System Capabilities

The core improvements focus on training models to move beyond surface-level marker detection (like simply recognizing "안 or 못") toward true semantic understanding of negation scope, structure, and context in Korean.

[] Implement a fine-tuning pipeline using the Thunder-KoNUBench dataset for Korean LLMs (specifically targeting the cloze setting).
[] Utilize Low-Rank Adaptation (LoRA) during this fine-tuning process to ensure parameter efficiency without catastrophic forgetting, as demonstrated in Section 5.1 and 5.10.
[] Prioritize generation-based supervision for training: instead of just selecting from options (symbol setting), require the model to generate the correct standard negation sentence based on a prompt (as shown in Table 9).

)What These Improved AI Systems Can Do

The improved systems will possess significantly enhanced capabilities in Korean language comprehension, specifically regarding complex logical reasoning and nuanced discourse analysis:

[] Perform Accurate Sentence-Level Negation Detection and Transformation:

This system can reliably identify the main predicate of a Korean sentence and apply the correct standard negation (reversing the truth value) across various complex structures, including those involving multiple clauses (De Morgan's laws), conditional sentences, and embedded clauses (Noun, Adnominal, Subordinate).

[] Distinguish Between Different Types of Negation Semantically:

The system will be able to differentiate between:

Standard Negation (which reverses the truth value of the entire main clause).
Local Negation (which only partially negates a dependent clause or one main clause).
Contradiction and Paraphrase (which alter meaning without using explicit negation markers), allowing it to handle subtle semantic shifts.

[] Enhance Robustness Against Multilingual Bias:

By fine-tuning on Korean data, the system will mitigate the performance degradation observed in non-Korean models when exposed to Korean negation tasks, thus improving its generalization across different linguistic domains.

[] Achieve Superior Performance in Contextual Reasoning:

The system will demonstrate a measurable improvement (e.g., 10% gains on Thunder-KoNUBench) and better performance on related reasoning benchmarks (like KoBest BoolQ), indicating an improved ability to reason under the constraint of negation, even in simple declarative questions.

[] Develop Format-Specific Proficiency:

The system will be optimized for the generation task (cloze setting), making it proficient at constructing grammatically correct, logically sound negated sentences that adhere to specific Korean syntactic constraints (e.g., avoiding awkward combinations of short-form negation with certain predicates).

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 to better understand model behavior. We further show that fine-tuning on Thunder-KoNUBench improves negation understanding and broader contextual comprehension in Korean.