LLMdoctor: Token-Level Flow-Guided Preference Optimization for Efficient Test-Time Alignment of Large Language Models | ScienceToStartup | ScienceToStartup

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References (31)

[1]

Flow-guided Direct Preference Optimization for Knowledge Graph Reasoning with Trees

2025Tiesunlong Shen, Rui Mao et al.

[2]

Guided Speculative Inference for Efficient Test-Time Alignment of LLMs

2025Jonathan Geuter, Youssef Mroueh et al.

[3]

Collaborative Multi-LoRA Experts with Achievement-based Multi-Tasks Loss for Unified Multimodal Information Extraction

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"Efficiently aligns large language models with human preferences using a novel token-level method outperforming traditional fine-tuning."

AI Alignment•Score: 7•View PDF ↗

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LLMdoctor: Token-Level Flow-Guided Preference Optimization for Efficient Test-Time Alignment of Large Language Models

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References (31)

Founder's Pitch

"Efficiently aligns large language models with human preferences using a novel token-level method outperforming traditional fine-tuning."

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