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cs.CL2026
Rebalancing Token Importance in Language Models with TF-IDF Weighted Cross-Entropy Loss
Zhijian Li, Stefan Larson, Kevin Leach
Large language models are typically trained under uniform token weighting, which allows frequent and low-information tokens to dominate learning and can increase the tendency to me…
cs.CL2026
Revising RVL-CDIP: Quantifying Errors and Test-Train Overlap
Stefan Larson, Attila Nagy, Sam Desai +8
RVL-CDIP is a popular dataset for benchmarking document classifiers. However, the dataset contains ample amounts of label errors as well as non-trivial amounts of test-train overla…