Papers with real-time correction

2 papers
Hit the Sweet Spot! Span-Level Ensemble for Large Language Models (2025.coling-main)

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Challenge: a recent study focused on sample-level and token-level ensembles, which hinder dynamic correction and enhancement of outputs during the generation process.
Approach: They propose a span-level ensemble method that balances real-time adjustments and accurate ensemble decisions.
Outcome: The proposed method improves performance across language generation tasks significantly.
Preconditioned Test-Time Adaptation for Out-of-Distribution Debiasing in Narrative Generation (2026.acl-long)

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Challenge: Debiased large language models excel at handling known or low-bias prompts, but fail on unfamiliar and high-biased prompts.
Approach: They propose a debiasing framework that detects high-bias prompts and triggers context-aware LoRA updates only when a bias-risk score exceeds a threshold.
Outcome: The proposed framework reduces toxicity/bias score with significantly lower latency than standard optimization methods.

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