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cs.CL2025
Planner and Executor: Collaboration between Discrete Diffusion And Autoregressive Models in Reasoning
Lina Berrayana, Ahmed Heakl, Muhammad Abdullah Sohail +3
Current autoregressive language models (ARMs) achieve high accuracy but require long token sequences, making them costly. Discrete diffusion language models (DDLMs) enable parallel…
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.CL2024
Local and Global Decoding in Text Generation
Daniel Gareev, Thomas Hofmann, Ezhilmathi Krishnasamy +1
Text generation, a key component in applications such as dialogue systems, relies on decoding algorithms that sample strings from a language model distribution. Traditional methods…