activity
20232026
most citedAnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets

4 citations · 10 across the 7 of their papers we have counts for

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Showing 2025Show all

2 papers · 1 filter

cs.CL2025

Causal Estimation of Tokenisation Bias

Pietro Lesci, Clara Meister, Thomas Hofmann +2

Modern language models are typically trained over subword sequences, but ultimately define probabilities over character-strings. Ideally, the choice of the tokeniser -- which maps…

cs.CL2025

Self-Training Large Language Models for Tool-Use Without Demonstrations

Ne Luo, Aryo Pradipta Gema, Xuanli He +3

Large language models (LLMs) remain prone to factual inaccuracies and computational errors, including hallucinations and mistakes in mathematical reasoning. Recent work augmented L…