7 papers
LongSocialBench: Do Long-Context LLMs Understand Online Discussion Threads?
Xinyi Liu, Rinat Khaziev, Dilek Hakkani-Tür +1
Long-context LLMs can now ingest entire online discussion threads, but understanding their social discourse requires more than reading a long document: models must track parent-rep…
Beyond Individual Personas: Aligning Synthetic Dialogue to Population-Level Behavior Distributions
Xinyi Liu, Rinat Khaziev, Hooshang Nayyeri +3
Synthetic dialogue corpora are increasingly used as proxies for target dialogue data, yet persona-grounded generators optimize individual conversations rather than corpus compositi…
ATOD: An Evaluation Framework and Benchmark for Agentic Task-Oriented Dialogue Systems
Yifei Zhang, Hooshang Nayyeri, Rinat Khaziev +5
Agentic task-oriented dialogue (TOD) requires systems to track concurrent goals, dependencies, and long-horizon state. We examine goal-lifecycle recovery from fixed dialogue trajec…
Regularization Through Reasoning: Systematic Improvements in Language Model Classification via Explanation-Enhanced Fine-Tuning
Vivswan Shah, Randy Cogill, Hanwei Yue +2
Fine-tuning LLMs for classification typically maps inputs directly to labels. We ask whether attaching brief explanations to each label during fine-tuning yields better models. We…
Controlling the Risk of Corrupted Contexts for Language Models via Early-Exiting
Andrea Wynn, Metod Jazbec, Charith Peris +4
Large language models (LLMs) can be influenced by harmful or irrelevant context, which can significantly harm model performance on downstream tasks. This motivates principled desig…
Constrained Entropic Unlearning: A Primal-Dual Framework for Large Language Models
Taha Entesari, Arman Hatami, Rinat Khaziev +2
Large Language Models (LLMs) deployed in real-world settings increasingly face the need to unlearn sensitive, outdated, or proprietary information. Existing unlearning methods typi…
Recommendation or Discrimination?: Quantifying Distribution Parity in Information Retrieval Systems
Rinat Khaziev, Bryce Casavant, Pearce Washabaugh +2
Information retrieval (IR) systems often leverage query data to suggest relevant items to users. This introduces the possibility of unfairness if the query (i.e., input) and the re…