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Found 4 papers, 1 papers with code
Pack of LLMs: Model Fusion at Test-Time via Perplexity Optimization
PackLLM performs model fusion by solving an optimization problem for determining each LLM's importance, so that perplexity over the input prompt is minimized.
SemPool: Simple, robust, and interpretable KG pooling for enhancing language models
Our method, termed SemPool, represents KG facts with pre-trained LMs, learns to aggregate their semantic information, and fuses it at different layers of the LM.
Extending Input Contexts of Language Models through Training on Segmented Sequences
Our method benefits both models trained with absolute positional embeddings, by extending their input contexts, as well as popular relative positional embedding methods showing a reduced perplexity on sequences longer than they were trained on.
Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models
Medical image classification is a critical problem for healthcare, with the potential to alleviate the workload of doctors and facilitate diagnoses of patients.
