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Related Questions
- What techniques can be used to assess the accuracy of summaries generated by large language models for complex scientific or engineering results?
- How can large language models be fine-tuned to better capture the nuances and technicalities of a specific scientific or engineering field?
- What types of data should be used to train large language models for generating natural language summaries of complex scientific or engineering results?
- How can large language models be used to integrate multiple sources of information to generate a comprehensive summary or report of complex scientific or engineering results?
- What challenges do large language models face when attempting to summarize complex scientific or engineering results with abstract or unconventional concepts?
- How can human evaluators and experts contribute to the training and fine-tuning of large language models for generating accurate summaries of complex scientific or engineering results?
- What is the current state-of-the-art in using large language models to generate summaries or reports of complex scientific or engineering results, and what are the areas of ongoing research?
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