5 citations · 5 across the 12 of their papers we have counts for
12 papers
TreeThink: A Modular Tree Search Library for Mathematical Reasoning with LLMs
Burak S. Akbudak, Zeynel A. Uluşan, Can S. Erer +1
Tree search algorithms enable systematic exploration of the proof space in neural theorem proving. Existing LLM tree search libraries primarily target natural language reasoning an…
Just Ask for a Table: A Thirty-Token User Prompt Defeats Sponsored Recommendations in Twelve LLMs
Andreas Maier, Jeta Sopa, Gozde Gul Sahin +2
Wu et al. (2026) showed that most frontier large language models (LLMs) recommend a sponsored, roughly twice-as-expensive flight when their system prompt contains a soft sponsorshi…
FormalRewardBench: A Benchmark for Formal Theorem Proving Reward Models
Zeynel A. Uluşan, Burak S. Akbudak, Can S. Erer +1
Recent neural theorem provers use reinforcement learning with verifiable rewards (RLVR), where proof assistants provide binary correctness signals. While verifiable rewards are che…
Visual Reasoning through Tool-supervised Reinforcement Learning
Qihua Dong, Gozde Sahin, Pei Wang +4
In this paper, we investigate the problem of how to effectively master tool-use to solve complex visual reasoning tasks for Multimodal Large Language Models. To achieve that, we pr…
Cetvel: A Unified Benchmark for Evaluating Language Understanding, Generation and Cultural Capacity of LLMs for Turkish
Yakup Abrek Er, Ilker Kesen, Gözde Gül Şahin +1
We introduce Cetvel, a comprehensive benchmark designed to evaluate large language models (LLMs) in Turkish. Existing Turkish benchmarks often lack either task diversity or cultura…
GECTurk WEB: An Explainable Online Platform for Turkish Grammatical Error Detection and Correction
Ali Gebeşçe, Gözde Gül Şahin
Sophisticated grammatical error detection/correction tools are available for a small set of languages such as English and Chinese. However, it is not straightforward -- if not impo…
Instructional Text Across Disciplines: A Survey of Representations, Downstream Tasks, and Open Challenges Toward Capable AI Agents
Abdulfattah Safa, Tamta Kapanadze, Arda Uzunoğlu +1
Recent advances in large language models have demonstrated promising capabilities in following simple instructions through instruction tuning. However, real-world tasks often invol…
No Optimal Language Set Exists for Multilingual Instruction Tuning: Insights from a Linguistically-Informed Study
Gürkan Soykan, Gözde Gül Şahin
Multilingual instruction tuning (MIT) is challenged by the curse of multilinguality, data scarcity, and high computational cost. A natural hypothesis is that carefully selecting a…
A Zero-Shot Open-Vocabulary Pipeline for Dialogue Understanding
Abdulfattah Safa, Gözde Gül Şahin
Dialogue State Tracking (DST) is crucial for understanding user needs and executing appropriate system actions in task-oriented dialogues. Majority of existing DST methods are desi…
PARADISE: Evaluating Implicit Planning Skills of Language Models with Procedural Warnings and Tips Dataset
Arda Uzunoglu, Abdalfatah Rashid Safa, Gözde Gül Şahin
Recently, there has been growing interest within the community regarding whether large language models are capable of planning or executing plans. However, most prior studies use L…
Quantifying Divergence for Human-AI Collaboration and Cognitive Trust
Müge Kural, Ali Gebeşçe, Tilek Chubakov +1
Predicting the collaboration likelihood and measuring cognitive trust to AI systems is more important than ever. To do that, previous research mostly focus solely on the model feat…
Data Augmentation via Dependency Tree Morphing for Low-Resource Languages
Gözde Gül Şahin, Mark Steedman
Neural NLP systems achieve high scores in the presence of sizable training dataset. Lack of such datasets leads to poor system performances in the case low-resource languages. We p…