9 citations · 9 across the 5 of their papers we have counts for
5 papers · 1 filter
Towards User Simulators That Stay On Task And Still Sound Natural
Mustafa Omer Gul, Philippe Laban, Tanya Goyal +1
Users increasingly rely on AI systems for extended, multi-turn tasks, yet evidence indicates that LLM-based system performance degrades over prolonged interaction. To evaluate and…
Learning User Representations to Improve Simulation Fidelity
Shuhaib Mehri, Philippe Laban, Michel Galley +3
User simulators enable interactive training and evaluation of AI assistants at scale. Yet current simulators struggle to reproduce the diverse behaviors of real users, leading to a…
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…
Flipping the Dialogue: Training and Evaluating User Language Models
Tarek Naous, Philippe Laban, Wei Xu +1
Conversations with LMs involve two participants: a human user leading the conversation, and an LM assistant responding to the user's request. To satisfy this specific role, LMs are…
LLMs Get Lost In Multi-Turn Conversation
Philippe Laban, Hiroaki Hayashi, Yingbo Zhou +1
Large Language Models (LLMs) are conversational interfaces. As such, LLMs have the potential to assist their users not only when they can fully specify the task at hand, but also t…