9 citations · 31 across the 10 of their papers we have counts for
11 papers
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…
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…
Surface-Based Retrieval Reduces Perplexity of Retrieval-Augmented Language Models
Ehsan Doostmohammadi, Tobias Norlund, Marco Kuhlmann +1
Augmenting language models with a retrieval mechanism has been shown to significantly improve their performance while keeping the number of parameters low. Retrieval-augmented mode…
On the Generalization Ability of Retrieval-Enhanced Transformers
Tobias Norlund, Ehsan Doostmohammadi, Richard Johansson +1
Recent work on the Retrieval-Enhanced Transformer (RETRO) model has shown that off-loading memory from trainable weights to a retrieval database can significantly improve language…
SINA-BERT: A pre-trained Language Model for Analysis of Medical Texts in Persian
Nasrin Taghizadeh, Ehsan Doostmohammadi, Elham Seifossadat +2
We have released Sina-BERT, a language model pre-trained on BERT (Devlin et al., 2018) to address the lack of a high-quality Persian language model in the medical domain. SINA-BERT…
Joint Persian Word Segmentation Correction and Zero-Width Non-Joiner Recognition Using BERT
Ehsan Doostmohammadi, Minoo Nassajian, Adel Rahimi
Words are properly segmented in the Persian writing system; in practice, however, these writing rules are often neglected, resulting in single words being written disjointedly and…