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Found 2 papers, 2 papers with code
Towards Measuring Representational Similarity of Large Language Models
Understanding the similarity of the numerous released large language models (LLMs) has many uses, e. g., simplifying model selection, detecting illegal model reuse, and advancing our understanding of what makes LLMs perform well.
Technical Report: Impact of Position Bias on Language Models in Token Classification
Therefore, we conduct an in-depth evaluation of the impact of position bias on the performance of LMs when fine-tuned on token classification benchmarks.
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