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Talking About Large Language Models
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Thanks to rapid progress in artificial intelligence, we have entered an era when technology and philosophy intersect in interesting ways. Sitting squarely at the centre of this intersection are large language models (LLMs). The more adept LLMs become at mimicking human language, the more vulnerable we become to anthropomorphism, to seeing the systems in which they are embedded as more human-like than they really are. This trend is amplified by the natural tendency to use philosophically loaded terms, such as "knows", "believes", and "thinks", when describing these systems. To mitigate this trend, this paper advocates the practice of repeatedly stepping back to remind ourselves of how LLMs, and the systems of which they form a part, actually work. The hope is that increased scientific precision will encourage more philosophical nuance in the discourse around artificial intelligence, both within the field and in the public sphere.
Forward citations
Cited by 4 Pith papers
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CC-Tuning: A Cross-Lingual Connection Mechanism for Improving Joint Multilingual Supervised Fine-Tuning
CC-Tuning fuses English feed-forward activations into non-English inputs during multilingual supervised fine-tuning, using a trainable Decision Maker and a least-squares Transform Matrix to simulate the connection at ...
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P-CoT: A Pedagogically-motivated Participatory Chain-of-Thought Prompting for Phonological Reasoning in LLMs
P-CoT prompting improves many LLM results on PhonologyBench tasks, but it does not consistently beat baselines across all models and tasks as the paper claims.
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Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions
Current XAI methods for DNNs and LLMs rest on paradoxes and false assumptions that demand a paradigm shift to verification protocols, scientific foundations, context-aware design, and faithful model analysis rather th...
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UniToMBench: Integrating Perspective-Taking to Improve Theory of Mind in LLMs
A new benchmark, UniToMBench, is proposed for evaluating Theory of Mind in LLMs, but its evaluation results are mixed and do not substantiate the claimed improvements.
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