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Found 4 papers, 2 papers with code
Small Language Models Learn Enhanced Reasoning Skills from Medical Textbooks
While recent advancements in commercial large language models (LM) have shown promising results in medical tasks, their closed-source nature poses significant privacy and security concerns, hindering their widespread use in the medical field.
Ranked #5 on
Question Answering
on MedQA
Test Time Embedding Normalization for Popularity Bias Mitigation
Popularity bias is a widespread problem in the field of recommender systems, where popular items tend to dominate recommendation results.
Exploiting the Potential of Seq2Seq Models as Robust Few-Shot Learners
In-context learning, which offers substantial advantages over fine-tuning, is predominantly observed in decoder-only models, while encoder-decoder (i. e., seq2seq) models excel in methods that rely on weight updates.
Item-based Variational Auto-encoder for Fair Music Recommendation
Our proposed system is based on an ensemble between an item-based variational auto-encoder (VAE) and a Bayesian personalized ranking matrix factorization (BPRMF).
