Knowledge editing aims to update specific facts in language models to ensure reliable and up-to-date behavior. A key challenge is whether edited knowledge generalizes beyond the format in which the edit is applied, yet existing evaluations primarily assess generalization using paraphrases. In this work, we identify a critical blind spot: generalization across heterogeneous task formats, including multiple-choice and verification-based question answering. We show that paraphrase-based evaluations overestimate robustness, as models that perform well on paraphrases exhibit substantial performance drops across task formats. While model predictions are consistent across formats prior to editing, editing introduces format-dependent inconsistencies. We further observe that heterogeneous task formats exhibit distinct decision landscapes, where single-format objectives lead to format-specific local optima with divergent edit directions, resulting in misaligned outputs across formats. To address this issue, we demonstrate that aligning edit directions across task formats improves cross-format generalization and consistency. These findings highlight the need to move beyond paraphrase-based evaluation and to develop editing methods that maintain consistent factual behavior across diverse task formulations.

The development of practical (multimodal) large language model assistants for Korean weather forecasters is hindered by the absence of a multidimensional, expert-level evaluation framework grounded in authoritative sources. To address this, we introduce K-MetBench, a diagnostic benchmark grounded in national qualification exams. It exposes critical gaps across four dimensions: expert visual reasoning of charts, logical validity via expert-verified rationales, Korean-specific geo-cultural comprehension, and fine-grained domain analysis. Our evaluation of 55 models reveals a profound modality gap in interpreting specialized diagrams and a reasoning gap where models hallucinate logic despite correct predictions. Crucially, Korean models outperform significantly larger global models in local contexts, demonstrating that parameter scaling alone cannot resolve cultural dependencies. K-MetBench serves as a roadmap for developing reliable, culturally aware expert AI agents.

Large language models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks. However, they remain vulnerable to semantic inconsistency, where minor formatting variations result in divergent predictions for semantically equivalent inputs. Our comprehensive evaluation reveals that this brittleness persists even in state-of-the-art models such as GPT-4o, posing a serious challenge to their reliability. Through a mechanistic analysis, we find that semantic-equivalent input changes induce instability in internal representations, ultimately leading to divergent predictions. This reflects a deeper structural issue, where form and meaning are intertwined in the embedding space. We further demonstrate that existing mitigation strategies, including direct fine-tuning on format variations, do not fully address semantic inconsistency, underscoring the difficulty of the problem. Our findings highlight the need for deeper mechanistic understanding to develop targeted methods that improve robustness.

Large language models (LLMs) often make factually incorrect responses despite their success in various applications. In this paper, we hypothesize that relying heavily on simple co-occurrence statistics of the pre-training corpora is one of the main factors that cause factual errors. Our results reveal that LLMs are vulnerable to the co-occurrence bias, defined as preferring frequently co-occurred words over the correct answer. Consequently, LLMs struggle to recall facts whose subject and object rarely co-occur in the pre-training dataset although they are seen during finetuning. We show that co-occurrence bias remains despite scaling up model sizes or finetuning. Therefore, we suggest finetuning on a debiased dataset to mitigate the bias by filtering out biased samples whose subject-object co-occurrence count is high. Although debiased finetuning allows LLMs to memorize rare facts in the training set, it is not effective in recalling rare facts unseen during finetuning. Further research in mitigation will help build reliable language models by preventing potential errors. The code is available at https://github.com/CheongWoong/impact_of_cooccurrence.