collaborators

15 papers

cs.CL2026

Popular Knowledge Propagates More Errors in LLM Knowledge Updating

Yuji Zhang, Weibing Wang, Cheng Qian +5

Updating a language model's knowledge through fine-tuning is essential for keeping its outputs current, yet can also induce factual forgetting and new hallucinations. Prior work sh…

cs.AI2026

ConvDeck: Conversational Paper-to-Slide Generation via Stage-Specific User Feedback

Tarik Can Ozden, Sachidanand VS, Furkan Horoz +4

Automatic academic paper-to-slide generation is inherently iterative, because creating an effective presentation requires repeated cycles of generation, critique, and revision. Rec…

cs.CL2026

Learning to Persuade Exposes How Easily LLMs Abandon Correct Beliefs

Nimet Beyza Bozdag, Emre Can Acikgoz, Gokhan Tur +1

Persuasion is a core dynamic of natural language communication, shaping how large language models (LLMs) update beliefs, resolve disagreements, and reach decisions. As LLMs increas…

cs.CL2026

PredAct-Bench: Benchmarking Tool-Augmented Dialogue under Controlled Tool Noise

Abdulrahman AlRabah, Xiaocheng Yang, Dilek Hakkani-Tür +1

Large Language Models (LLMs) are increasingly deployed in task-oriented dialogue systems that support multi-step decision-making in high-stakes domains such as education, healthcar…

cs.CL2026

Reasoning Consensus: Structural Ensembling of LLM Reasoning via Weighted DAG Aggregation

Amruta Parulekar, Jinu Lee, Dilek Hakkani-Tür +1

Large Language Models (LLMs) explore problems through chain-of-thought, but this exploration is buried in unstructured prose. On high-stakes tasks, users cannot tell which steps ar…

cs.LG2026

Measuring, Localizing, and Ablating Alignment Signatures in LLMs

Aniket Anand, Janvijay Singh, Zhewei Sun +2

Aligned language models often exhibit a recognizable AI-like style, yet its connection to post-training and internal representations remains poorly understood. In this work, we stu…