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Related Questions
- How do current LLM approaches handle multiple possible interpretations of a single reference, and what are the implications for scalability and computational efficiency?
- What are the computational resources required to process and resolve ambiguous references in large-scale LLMs, and how do these resources impact overall system performance?
- Can you explain the trade-offs between accuracy and efficiency in LLMs when dealing with ambiguous references, and how do these trade-offs impact the overall design of the system?
- How do LLMs currently handle out-of-domain or out-of-context references, and what are the challenges associated with scaling these approaches to larger datasets and more complex tasks?
- What are the limitations of current LLM approaches to handling anaphora resolution, and how do these limitations impact the overall coherence and fluency of generated text?
- Can you discuss the impact of reference ambiguity on the performance of LLMs in downstream tasks such as question answering and text summarization?
- What are some potential strategies for improving the scalability and computational efficiency of LLMs when handling ambiguous references, and how might these strategies be implemented in practice?
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