A feedforward graph of heterogeneous frozen LLMs linked by linear projections in a shared latent space outperforms single models on ARC-Challenge, OpenBookQA, and MMLU using just 17.6M trainable parameters.
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Dead Weights, Live Signals: Feedforward Graphs of Frozen Language Models
A feedforward graph of heterogeneous frozen LLMs linked by linear projections in a shared latent space outperforms single models on ARC-Challenge, OpenBookQA, and MMLU using just 17.6M trainable parameters.