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User feedback can significantly enhance LLM performance, but current evaluation methods often overlook its benefits, leading to misguided assessments of model improvements.
Cross-lingual exploration can unlock hidden knowledge in LLMs, improving factual recall and consistency across 17 languages.
Using a top or bottom-performing LLM as an anchor in "LLM-as-a-judge" benchmarks can dramatically skew results, making the choice of a mediocre anchor key to reliable evaluation.
