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cs.CL2025
Your LLM Knows the Future: Uncovering Its Multi-Token Prediction Potential
Mohammad Samragh, Arnav Kundu, David Harrison +4
Autoregressive language models are constrained by their inherently sequential nature, generating one token at a time. This paradigm limits inference speed and parallelism, especial…
cs.CL2025
From Dense to Dynamic: Token-Difficulty Driven MoEfication of Pre-Trained LLMs
Kumari Nishu, Sachin Mehta, Samira Abnar +6
Training large language models (LLMs) for different inference constraints is computationally expensive, limiting control over efficiency-accuracy trade-offs. Moreover, once trained…