7 papers
ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders
Ofer Meshi, Krisztian Balog, Sally Goldman +5
The promise of LLM-based user simulators to improve conversational AI is hindered by a critical "realism gap," leading to systems that are optimized for simulated interactions, but…
RouteNator: A Router-Based Multi-Modal Architecture for Generating Synthetic Training Data for Function Calling LLMs
Vibha Belavadi, Tushar Vatsa, Dewang Sultania +5
This paper addresses fine-tuning Large Language Models (LLMs) for function calling tasks when real user interaction data is unavailable. In digital content creation tools, where us…
Assessing biomedical knowledge robustness in large language models by query-efficient sampling attacks
R. Patrick Xian, Alex J. Lee, Satvik Lolla +4
The increasing depth of parametric domain knowledge in large language models (LLMs) is fueling their rapid deployment in real-world applications. Understanding model vulnerabilitie…
Conditional Language Policy: A General Framework for Steerable Multi-Objective Finetuning
Kaiwen Wang, Rahul Kidambi, Ryan Sullivan +17
Reward-based finetuning is crucial for aligning language policies with intended behaviors (e.g., creativity and safety). A key challenge is to develop steerable language models tha…
Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process Supervision
Zihan Wang, Yunxuan Li, Yuexin Wu +4
Process supervision, using a trained verifier to evaluate the intermediate steps generated by a reasoner, has demonstrated significant improvements in multi-step problem solving. I…
Enabling Language Models to Implicitly Learn Self-Improvement
Ziqi Wang, Le Hou, Tianjian Lu +4
Large Language Models (LLMs) have demonstrated remarkable capabilities in open-ended text generation tasks. However, the inherent open-ended nature of these tasks implies that ther…