3 papers
cs.CL2025
Improving Instruct Models for Free: A Study on Partial Adaptation
Ozan Ä°rsoy, Pengxiang Cheng, Jennifer L. Chen +3
Instruct models, obtained from various instruction tuning or post-training steps, are commonly deemed superior and more usable than their base counterpart. While the model gains in…
cs.CL2025
Evaluating the Retrieval Robustness of Large Language Models
Shuyang Cao, Karthik Radhakrishnan, David Rosenberg +4
Retrieval-augmented generation (RAG) generally enhances large language models' (LLMs) ability to solve knowledge-intensive tasks. But RAG may also lead to performance degradation d…
cs.IR2025
An Alternative to FLOPS Regularization to Effectively Productionize SPLADE-Doc
Aldo Porco, Dhruv Mehra, Igor Malioutov +5
Learned Sparse Retrieval (LSR) models encode text as weighted term vectors, which need to be sparse to leverage inverted index structures during retrieval. SPLADE, the most popular…