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Related Questions
- What are the common techniques used to optimize negative sampling for better analogy generation in language models?
- How does negative sampling impact the accuracy of analogy generation in language models compared to other methods?
- Can you explain the concept of noise injection in negative sampling for analogy generation and its role in improving model performance?
- What are the key differences between negative sampling and other analogy generation techniques, such as word embeddings and knowledge graph-based methods?
- How can negative sampling be used to improve the robustness of language models to out-of-vocabulary words and rare events?
- Can you discuss the trade-offs between the size of the negative sampling dataset and the model's performance in analogy generation?
- What are some common challenges and limitations of using negative sampling for analogy generation in language models, and how can they be addressed?
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