Tanalp Ağustoslu
Computational Linguistics MSc Student at the Center for Information and Language Processing of LMU
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Projects and Publications
LMU at PerAnsSumm 2025: LlaMA-in-the-loop at Perspective-Aware Healthcare Answer Summarization Task 2.2 Factuality
Tanalp Agustoslu.In CL4Health, ACL 2025 Workshop.
📝 Abstract: In this paper, we describe our submission for the shared task on Perspective-aware Healthcare Answer Summarization. Our system consists of two quantized models of the LlaMA family, applied across fine-tuning and few-shot settings. Additionally, we adopt the SumCoT prompting technique to improve the factual correctness of the generated summaries. We show that SumCoT yields more factually accurate summaries, even though this improvement comes at the expense of lower performance on lexical overlap and semantic similarity metrics such as ROUGE and BERTScore. Our work highlights an important trade-off when evaluating summarization models.
Sign Language Sense Disambiguation
Jana Grimm, Miriam Winkler, Oliver Kraus, Tanalp Agustoslu.LIMO 2024 @ KONVENS 2024. arXiv Preprint.
📝 Abstract: This project explores methods to enhance sign language translation of German sign language, specifically focusing on disambiguation of homonyms. Sign language is ambiguous and understudied which is the basis for our experiments. We approach the improvement by training transformer-based models on various bodypart representations to shift the focus on said bodypart. To determine the impact of, e.g., the hand or mouth representations, we experiment with different combinations. The results show that focusing on the mouth increases the performance in small dataset settings while shifting the focus on the hands retrieves better results in larger dataset settings. Our results contribute to better accessibility for non-hearing persons by improving the systems powering digital assistants, enabling a more accurate interaction. The code for this project can be found on GitHub.
LMU-BioNLP at SemEval-2024 Task 2: Large Diverse Ensembles for Robust Clinical NLI
Zihang Sun, Danqi Yan, Anyi Wang, Tanalp Agustoslu, Qi Feng, Chengzhi Hu, Longfei Zuo, Shijia Zhou, Hermine Kleiner, Pingjun Hong, Suteera Seeha, Sebastian Loftus, Anna Barwig, Oliver Kraus, Jona Volohonsky, Yang Sun, Leopold Martin, Lena Altinger, Jing Wang, Leon Weber.In SemEval, ACL 2024 Workshop.
📝 Abstract: In this paper, we describe our submission for the NLI4CT 2024 shared task on robust Natural Language Inference over clinical trial reports. Our system is an ensemble of nine diverse models which we aggregate via majority voting. The models use a large spectrum of different approaches ranging from a straightforward Convolutional Neural Network over fine-tuned Large Language Models to few-shot-prompted language models using chain-of-thought reasoning.Surprisingly, we find that some individual ensemble members are not only more accurate than the final ensemble model but also more robust.
Lispy - A Lisp dialect
Lispy is a functional programming language with an interpreted nature and a homoiconic design, drawing inspiration from the Lisp programming language developed in 1958. The interpreter is written in C, utilizing the MPC library for parsing operations. Lispy serves as an Interactive Prompt, enabling users to evaluate expressions in real-time or to evaluate external files written in the Lispy language.
