Papers with next-token prediction paradigm

2 papers
The Mystery of the Pathological Path-star Task for Language Models (2024.emnlp-main)

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Challenge: Language models have become increasingly capable of solving a variety of complex tasks.
Approach: They propose a path-star task where multiple arms radiate from a single starting node and each node is unique.
Outcome: The proposed task is learnable using teacher-forcing in alternative settings and improves results across a variety of model types.
LLM-ForcedAligner: A Non-Autoregressive and Accurate LLM-Based Forced Aligner for Multilingual and Long-Form Speech (2026.acl-long)

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Challenge: Existing methods for forcing alignment are language-specific and prone to temporal shifts.
Approach: They propose a slot-filling paradigm that uses time indices to predict slot positions.
Outcome: The proposed method reduces accumulated temporal shifts by 69% compared with prior methods.

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