Emre Can AcikgozI am a PhD fellow in Computer Science at UIUC advised by Prof. Dilek Hakkani-Tur and Prof. Gokhan Tur. My research interests lie in Conversational AI and Generative Agents. I obtained my MSc student in Computer Science at Koc University. I worked around Large Language Models and Multimodal Learning under the supervision of Deniz Yuret and Aykut Erdem. Prior to my MSc, I recieved my BSc in Electrical and Electronics Engineering (AI focus) from Koc University, where I worked under the supervision of Deniz Yuret on supervised and unsupervised morphological analysis. Email / GitHub / HuggingFace / Google Scholar / LinkedIn |
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ResearchMy research is in Conversational AI, Generative Agents, and Large Language Models. |
2024 |
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Bridging the Bosphorus: Advancing Turkish Large Language Models through Strategies for Low-Resource Language Adaptation and Benchmarking
Emre Can Acikgoz, Mete Erdoğan, Deniz Yuret arXiv, 2024 arxiv / website / code / poster This study evaluates the effectiveness of training strategies for large language models in low-resource languages like Turkish, focusing on model adaptation, development, and fine-tuning to enhance reasoning skills and address challenges such as data scarcity and catastrophic forgetting. |
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Hippocrates: An Open-Source Framework for Advancing Large Language Models in Healthcare
Emre Can Acikgoz, Osman Batur İnce, Rayene Bech, Arda Anıl Boz, Ilker Kesen, Aykut Erdem, Erkut Erdem arXiv, 2024 arxiv / website / poster We present Hippocrates, an open-source LLM framework specifically developed for the medical domain. Also, we introduce Hippo, a family of 7B models tailored for the medical domain, fine-tuned from Mistral and LLaMA2 through continual pre-training, instruction tuning, and reinforcement learning from human and AI feedback. |
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ViLMA: A Zero-Shot Benchmark for Linguistic and Temporal Grounding in Video-Language Models
Ilker Kesen, Andrea Pedrotti, Mustafa Dogan, Michele Cafagna, Emre Can Acikgoz, Letitia Parcalabescu, Iacer Calixto, Anette Frank, Albert Gatt, Aykut Erdem, Erkut Erdem ICLR, 2024 arxiv / website / code ViLMA (Video Language Model Assessment) presents a comprehensive benchmark for Video-Language Models, starting with a fundamental comprehension test and followed by a more advanced evaluation for temporal reasoning skills. |
2022 |
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Transformers on Multilingual Clause-Level Morphology
Emre Can Acikgoz, Tilek Chubakov, Müge Kural, Gözde Gül Şahin, Deniz Yuret EMNLP Workshop, 2022 arxiv / code / slides This paper describes the winning approaches in MRL: The 1st Shared Task on Multilingual Clause-level Morphology. Our submission, which excelled in all three parts of the shared task — inflection, reinflection, and analysis — won the first prize in each category. |
Tutor
Comp547: Deep Unsupervised Learning (Spring'24) |
Talks
Huawei NLP/ML Community Seminer Series: Morphological Analysis with Large Language Models (2022, Virtual) |
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