Papers by Furkan Şahinuç
Reward Modeling for Scientific Writing Evaluation (2026.acl-long)
Copied to clipboard
| Challenge: | Existing models for scientific writing evaluation are primarily optimized for general-purpose benchmarks with fixed scoring rubrics and evaluation criteria. |
| Approach: | They propose to train scientific writing evaluation models that leverage domain knowledge . they use a two-stage evaluation framework that optimizes evaluation preferences and refines reasoning capabilities . |
| Outcome: | The proposed model generalizes effectively across tasks and to previously unseen settings. |
Large-Scale Hate Speech Detection with Cross-Domain Transfer (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing datasets for hate speech detection are limited due to the labor cost. |
| Approach: | They construct large-scale tweet datasets for hate speech detection in English and a low-resource language, Turkish, consisting of human-labeled 100k tweets per each. |
| Outcome: | The proposed datasets outperform conventional bag-of-words and neural models by at least 5% in English and 10% in Turkish for large-scale hate speech detection. |
Systematic Task Exploration with LLMs: A Study in Citation Text Generation (2024.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) provide unprecedented flexibility in defining and executing complex, creative natural language generation tasks. |
| Approach: | They propose a framework that consists of input manipulation, reference data, and output measurement to explore citation text generation. |
| Outcome: | The proposed framework explores citation text generation, a popular scholarly NLP task that lacks consensus on the task definition and evaluation metric and has not yet been tackled within the LLM paradigm. |
Efficient Performance Tracking: Leveraging Large Language Models for Automated Construction of Scientific Leaderboards (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing leaderboards are incomplete and some contain incorrect information. |
| Approach: | They propose a manually-curated Scientific Leaderboard dataset that overcomes these problems . they propose three experimental settings where TDM triples are fully defined, partially defined, or undefined . |
| Outcome: | The proposed system overcomes the shortcomings of existing leaderboard datasets . it can be used to evaluate and compare scientific methods, but it requires manual labor . |