2 papers
cs.CL2025
Studying the Role of Input-Neighbor Overlap in Retrieval-Augmented Language Models Training Efficiency
Ehsan Doostmohammadi, Marco Kuhlmann
Retrieval-augmented language models have demonstrated performance comparable to much larger models while requiring fewer computational resources. The effectiveness of these models…
cs.CL2024
How Reliable Are Automatic Evaluation Methods for Instruction-Tuned LLMs?
Ehsan Doostmohammadi, Oskar Holmström, Marco Kuhlmann
Work on instruction-tuned Large Language Models (LLMs) has used automatic methods based on text overlap and LLM judgments as cost-effective alternatives to human evaluation. In thi…