RAG-Pref is a training-free RAG-based alignment technique that conditions LLMs on contrastive preference samples during inference, yielding over 3.7x average improvement in agentic attack refusals when combined with offline methods across five LLMs.
Lima: Less is more for alignment
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SynthPert fine-tunes LLMs using synthetic reasoning traces to reach state-of-the-art on the PerturbQA benchmark for cellular perturbation prediction, surpassing the generating frontier model while generalizing to unseen cell types with only 2% of filtered data.
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Leveraging RAG for Training-Free Alignment of LLMs
RAG-Pref is a training-free RAG-based alignment technique that conditions LLMs on contrastive preference samples during inference, yielding over 3.7x average improvement in agentic attack refusals when combined with offline methods across five LLMs.
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SynthPert: Enhancing LLM Biological Reasoning via Synthetic Reasoning Traces for Cellular Perturbation Prediction
SynthPert fine-tunes LLMs using synthetic reasoning traces to reach state-of-the-art on the PerturbQA benchmark for cellular perturbation prediction, surpassing the generating frontier model while generalizing to unseen cell types with only 2% of filtered data.