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    "prose": "Abhinav Bohra, a senior applied scientist at Amazon, stated that his biggest career regret is waiting years to build a professional reputation outside the company. [^1]\n\nResults show that while LLMs exhibit human-like rating patterns, their absolute rating agreement is low and varies substantially by model size and evaluation construct. [^2]\n\nThe paper investigates the utility of Large Language Models (LLMs) in selecting an effective explanation method for a given recommender system application. [^3]\n\nAbhinav Bohra advised tech workers to build a public record of their work outside their company to avoid starting from scratch if they leave due to layoffs or a difficult job market. [^4]\n\nAbhinav Bohra developed rules for himself to share work externally, stating that if a problem exists in published research, it is a conversation, but nonpublic data, internal metrics, and unreleased products are off the table. [^5]\n\nResearchers generated 18 distinct explanation prototypes to evaluate the performance of different explanation methods. [^6]",
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