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Related Questions
- What factors most significantly impact the quality and accuracy of large language model (LLM) learned knowledge?
- How does the volume of training data affect the broadness and depth of global knowledge representation in LLMs?
- What are some challenges AI researchers face in developing diverse and accurate global knowledge data for LLM training scenarios?
- Will incorporating domain-specific data reduce the LLM's cross-domain generalizability potential?
- Discuss the trade-off between learning global knowledge versus knowledge accuracy in LLM design – how does the accuracy benefit impact overall LLM outputs?
- Some argue that pretraining LM models on broad domain-spanning datasets creates stronger ability to reason abstractly — do you agree with and support this hypothesis with references?
- From a prompt engineering standpoint can we leverage existing web scrapers to harvest better in-domain data from public source web pages — should large models use existing open crawled web pages, such large-scale data will they only add noise, improve a model, or worsened its overall output — analyze this in context.
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