Papers by Tamer Alkhouli
CONFETTI: Conversational Function-Calling Evaluation Through Turn-Level Interactions (2025.acl-long)
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Tamer Alkhouli, Katerina Margatina, James Gung, Raphael Shu, Claudia Zaghi, Monica Sunkara, Yi Zhang
| Challenge: | Current benchmarks lack comprehensive assessment of large language models in complex conversational scenarios. |
| Approach: | They introduce a conversational benchmark to evaluate function-calling capabilities of large language models. |
| Outcome: | The proposed benchmark evaluates the function-calling capabilities and response quality of large language models. |
Supplement Generation Training for Enhancing Agentic Task Performance (2026.findings-acl)
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Young Min Cho, Daniele Bonadiman, Divya Bhargavi, Tamer Alkhouli, Salvatore Romeo, Dongwei Jiang, Khushbu Pahwa, Yubin Ge, Etsuko Ishii, Monica Sunkara, Yi Zhang
| Challenge: | Training large foundation models for agentic tasks is impractical due to high computational costs, long iteration cycles, and rapid obsolescence as new models are released. |
| Approach: | They propose a method that trains a small LLM to generate supplemental text that helps the larger LLM solve the task more effectively. |
| Outcome: | The proposed approach decouples task-specific optimization from large foundation models . it achieves consistent and significant performance gains across diverse tasks and models - all without gradient access to the actor model. |
RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition (P18-4)
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| Challenge: | Using RETURNN, we train and decode attention models for translation and speech recognition. |
| Approach: | They propose a layer-wise pretraining scheme for recurrent attention models and show its significant effect on deep recurrence encoder networks. |
| Outcome: | The proposed training and decoding scheme improves 1% on expected training and improves on WMT 2017 and Switchboard. |
Eliciting Better Multilingual Structured Reasoning from LLMs through Code (2024.acl-long)
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| Challenge: | xSTREET exposes a gap in base LLM performance between English and non-English reasoning tasks. |
| Approach: | They propose a multilingual structured reasoning and explanation dataset that covers four tasks across six languages and extends the English STREET benchmark to 5 additional diverse languages. |
| Outcome: | The proposed models show improved multilingual performance on scientific commonsense reasoning subtasks and no regression on non-reasoning tasks. |
Neural Hidden Markov Model for Machine Translation (P18-2)
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| Challenge: | Attention-based neural machine translation models selectively focus on specific source positions to produce a translation. |
| Approach: | They propose to replace the attention component with a neural hidden Markov model that selectively focuss on specific source positions to produce a translation. |
| Outcome: | The proposed model performs better than the state-of-the-art attention-based models on the GermanEnglish and ChineseEnglish translation tasks. |