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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.01168 |
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| _version_ | 1866913456334045184 |
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| author | Zhou, Bo Geißler, Daniel Lukowicz, Paul |
| author_facet | Zhou, Bo Geißler, Daniel Lukowicz, Paul |
| contents | Large Language Models (LLMs) have made significant advances in natural language processing, but their underlying mechanisms are often misunderstood. Despite exhibiting coherent answers and apparent reasoning behaviors, LLMs rely on statistical patterns in word embeddings rather than true cognitive processes. This leads to vulnerabilities such as "hallucination" and misinformation. The paper argues that current LLM architectures are inherently untrustworthy due to their reliance on correlations of sequential patterns of word embedding vectors. However, ongoing research into combining generative transformer-based models with fact bases and logic programming languages may lead to the development of trustworthy LLMs capable of generating statements based on given truth and explaining their self-reasoning process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01168 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Misinforming LLMs: vulnerabilities, challenges and opportunities Zhou, Bo Geißler, Daniel Lukowicz, Paul Computation and Language Artificial Intelligence Large Language Models (LLMs) have made significant advances in natural language processing, but their underlying mechanisms are often misunderstood. Despite exhibiting coherent answers and apparent reasoning behaviors, LLMs rely on statistical patterns in word embeddings rather than true cognitive processes. This leads to vulnerabilities such as "hallucination" and misinformation. The paper argues that current LLM architectures are inherently untrustworthy due to their reliance on correlations of sequential patterns of word embedding vectors. However, ongoing research into combining generative transformer-based models with fact bases and logic programming languages may lead to the development of trustworthy LLMs capable of generating statements based on given truth and explaining their self-reasoning process. |
| title | Misinforming LLMs: vulnerabilities, challenges and opportunities |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2408.01168 |