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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.10992 |
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| _version_ | 1866909110801268736 |
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| author | Lyre, Holger |
| author_facet | Lyre, Holger |
| contents | Do LLMs understand the meaning of the texts they generate? Do they possess a semantic grounding? And how could we understand whether and what they understand? I start the paper with the observation that we have recently witnessed a generative turn in AI, since generative models, including LLMs, are key for self-supervised learning. To assess the question of semantic grounding, I distinguish and discuss five methodological ways. The most promising way is to apply core assumptions of theories of meaning in philosophy of mind and language to LLMs. Grounding proves to be a gradual affair with a three-dimensional distinction between functional, social and causal grounding. LLMs show basic evidence in all three dimensions. A strong argument is that LLMs develop world models. Hence, LLMs are neither stochastic parrots nor semantic zombies, but already understand the language they generate, at least in an elementary sense. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10992 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | "Understanding AI": Semantic Grounding in Large Language Models Lyre, Holger Computation and Language Artificial Intelligence Do LLMs understand the meaning of the texts they generate? Do they possess a semantic grounding? And how could we understand whether and what they understand? I start the paper with the observation that we have recently witnessed a generative turn in AI, since generative models, including LLMs, are key for self-supervised learning. To assess the question of semantic grounding, I distinguish and discuss five methodological ways. The most promising way is to apply core assumptions of theories of meaning in philosophy of mind and language to LLMs. Grounding proves to be a gradual affair with a three-dimensional distinction between functional, social and causal grounding. LLMs show basic evidence in all three dimensions. A strong argument is that LLMs develop world models. Hence, LLMs are neither stochastic parrots nor semantic zombies, but already understand the language they generate, at least in an elementary sense. |
| title | "Understanding AI": Semantic Grounding in Large Language Models |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2402.10992 |