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Related Questions
- Can LLMs learn to distinguish between homophones like 'to', 'too', and 'two' through self-supervised learning?
- How do LLMs currently handle homophone disambiguation, and what are the limitations of their current approaches?
- Can LLMs improve their homophone disambiguation abilities through exposure to large amounts of unlabeled text data?
- What role does contextual information play in LLMs' ability to disambiguate homophones, and can they learn to use it more effectively?
- Are there any specific techniques or architectures that are particularly well-suited for homophone disambiguation in LLMs?
- Can LLMs learn to recognize and correct homophone errors in user input, such as in language translation or text summarization tasks?
- How do LLMs' homophone disambiguation abilities compare to those of human language models, and what can we learn from their strengths and weaknesses?
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