LLMs Struggle with Abstract Meaning Comprehension More Than Expected
arXiv cs.AI / 4/15/2026
💬 OpinionIdeas & Deep AnalysisModels & Research
Key Points
- The paper argues that abstract word understanding remains difficult for language models because abstract meanings are non-concrete and rely on high-level semantics.
- Using the SemEval-2021 Task 4 (ReCAM) cloze-style evaluation, it finds that many LLMs (including GPT-4o) under zero-shot, one-shot, and few-shot prompting perform worse on abstract meaning questions than fine-tuned models like BERT and RoBERTa.
- It introduces a bidirectional attention classifier that more dynamically attends to both the passage and candidate options, aiming to mimic human cognitive strategies for abstraction.
- The proposed method improves accuracy by 4.06% on Task 1 and 3.41% on Task 2, suggesting architectural changes and attention design can partially mitigate abstract comprehension gaps.
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