Papers by Amin Ahmad
mAggretriever: A Simple yet Effective Approach to Zero-Shot Multilingual Dense Retrieval (2023.emnlp-main)
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| Challenge: | MLIR requires human annotations in multiple languages, making training labor-intensive. |
| Approach: | They propose a multilingual information retrieval model that leverages pre-trained multilingual transformers for dense retrieval. |
| Outcome: | Empirical results show that mAggretriever outperforms state-of-the-art models fine-tuned on English training data. |
Multilingual Universal Sentence Encoder for Semantic Retrieval (2020.acl-demos)
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Yinfei Yang, Daniel Cer, Amin Ahmad, Mandy Guo, Jax Law, Noah Constant, Gustavo Hernandez Abrego, Steve Yuan, Chris Tar, Yun-hsuan Sung, Brian Strope, Ray Kurzweil
| Challenge: | Using a multi-task trained dual-encoder, our models embed text from 16 languages into a shared semantic space. |
| Approach: | They propose retrieval focused multilingual sentence embedding models on TensorFlow Hub. |
| Outcome: | The models achieve state-of-the-art on monolingual and cross-lingual retrieval (SR) and retrieval question answering (ReQA) competitive performance is obtained on related tasks of translation pair bitext retrieval and retrieving question answering. |
ReQA: An Evaluation for End-to-End Answer Retrieval Models (D19-58)
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| Challenge: | Popular QA benchmarks like SQuAD have driven progress on identifying answer spans within a specific passage . retrieving relevant answers from a huge corpus of documents is still a challenging problem . |
| Approach: | They propose a benchmark for evaluating large-scale sentence-level answer retrieval models . they establish baselines using both neural encoding models and classical retrieval techniques . |
| Outcome: | The proposed model outperforms human models on identifying answer spans within a specific passage . the proposed model is scalable and can bypass the typical document retrieval step . |
On the Impact of Cross-Domain Data on German Language Models (2023.findings-emnlp)
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Amin Dada, Aokun Chen, Cheng Peng, Kaleb Smith, Ahmad Idrissi-Yaghir, Constantin Seibold, Jianning Li, Lars Heiliger, Christoph Friedrich, Daniel Truhn, Jan Egger, Jiang Bian, Jens Kleesiek, Yonghui Wu
| Challenge: | Traditionally, large language models have been trained on general web crawls or domain-specific data. |
| Approach: | They present a German dataset and a dataset aimed at containing high-quality data to examine the importance of data diversity over quality. |
| Outcome: | The proposed model outperforms models trained on quality data on multiple downstream tasks. |
Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding (2024.lrec-main)
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Ahmad Idrissi-Yaghir, Amin Dada, Henning Schäfer, Kamyar Arzideh, Giulia Baldini, Jan Trienes, Max Hasin, Jeanette Bewersdorff, Cynthia S. Schmidt, Marie Bauer, Kaleb E. Smith, Jiang Bian, Yonghui Wu, Jörg Schlötterer, Torsten Zesch, Peter A. Horn, Christin Seifert, Felix Nensa, Jens Kleesiek, Christoph M. Friedrich
| Challenge: | Pre-trained language models can struggle in specialized domains such as medicine . existing generalpurpose pre-tried models can be used and refined through further pre-training on domainspecific unlabeled data. |
| Approach: | They pre-trained German medical language models on 2.4B tokens from translated public data and 3B token of German clinical data. |
| Outcome: | The proposed models outperform clinical models on various downstream tasks in germany . the authors show that continuous pre-training can match or exceed clinical models trained from scratch . |
FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMs (2025.naacl-short)
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Forrest Sheng Bao, Miaoran Li, Renyi Qu, Ge Luo, Erana Wan, Yujia Tang, Weisi Fan, Manveer Singh Tamber, Suleman Kazi, Vivek Sourabh, Mike Qi, Ruixuan Tu, Chenyu Xu, Matthew Gonzales, Ofer Mendelevitch, Amin Ahmad
| Challenge: | Existing evaluations of hallucinations in large language models suffer from a lack of diversity and recency in the LLM and LLM families considered. |
| Approach: | They propose a summarization hallucination benchmark that challenges models to disagree on hallucines . they use models to generate answers or summaries from textual input . |
| Outcome: | The proposed model combines the best of 10 modern LLMs with ground truth annotations. |
MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation Systems (2025.naacl-long)
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| Challenge: | Traditional retrieval-augmented generation benchmarks use heuristics as the ground truth for evaluation, but require an expensive large language model (LLM) as a judge for a reliable evaluation. |
| Approach: | They propose to use large language models as a judge for retrieval-augmented generation benchmarks . they use heuristic metrics as input and a large language model as heuriistic input . |
| Outcome: | The proposed method couples heuristic features with large language models as judge for evaluation. |