Papers with real-time adjustments

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.
AGD: Adversarial Game Defense Against Jailbreak Attacks in Large Language Models (2025.acl-long)

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Challenge: Existing defenses, including post-training alignment and prompt engineering, struggle with adaptability to out-of-distribution (OOD) attacks.
Approach: They propose an adversarial game-based defense method that dynamically adjusts LLMs’ internal representations to achieve a balanced trade-off between helpfulness and harmlessness.
Outcome: The proposed method improves LLMs’ safety over all baselines.

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