Abstract We argue that there are certain properties of language that our current large language models (LLMs) don't learn. We present an empirical investigation of visual-auditory properties of language through a series of tasks, termed H-Test. This benchmark highlights a fundamental gap between human linguistic comprehension, which naturally integrates sensory experiences, and the sensory-deprived processing capabilities of LLMs. In support of our hypothesis, 1. deliberate reasoning (Chain-of-Thought), 2. few-shot examples, or 3. stronger LLM from the same model family (LLaMA 2 13B -> LLaMA 2 70B) do not trivially bring improvements in H-Test performance. Therefore, we make a particular connection to the philosophical case of Mary, who learns about the world in a sensory-deprived environment. Our experiments show that some of the strongest proprietary LLMs stay near random chance baseline accuracy of 50%, highlighting the limitations of knowledge acquired in the absence of sensory experience. Key Findings on H-Test 1. Insignificant Intra-Family Improvements. * A stronger model in the same model family often does not bring meaningful improvement in H-Test performance. 2. Number of Examples Has Minimal Impact. * The number of examples given neither increases nor decreases performance significantly, strongly hinting that the LLM is simply not learning from H-Test few-shot examples. 3. Deliberate Reasoning (CoT) Often Decreases Performance. * If LLMs benefit from such logical, step-by-step semantic reasoning on the H-Test, this can also imply that H-Test is fundamentally solvable by developing stronger language-only models. But CoT decreases performances in general. 4. Training with more orthography-specific language data does not improve H-Test. * We produced 1000 training instances per task in H-Test and fine-tuned gpt-3.5-turbo-0613 ten different times accordingly. After training for three epochs on each task, we evaluated them on H-Test at k = 50 an