Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering V Adlakha, P BehnamGhader, XH Lu, N Meade, S Reddy arXiv preprint arXiv:2307.16877, 2023 | 38 | 2023 |
Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model P BehnamGhader, S Miret, S Reddy EMNLP 2023 Findings, 2023 | 12 | 2023 |
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders P BehnamGhader, V Adlakha, M Mosbach, D Bahdanau, N Chapados, ... arXiv preprint arXiv:2404.05961, 2024 | 5 | 2024 |
An Analysis of Social Biases Present in BERT Variants Across Multiple Languages P BehnamGhader, A Milios Workshop on Trustworthy and Socially Responsible Machine Learning, NeurIPS 2022, 2022 | 4* | 2022 |
MG-BERT: Multi-Graph Augmented BERT for Masked Language Modeling P BehnamGhader, H Zakerinia, MS Baghshah Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural …, 2021 | 2 | 2021 |