2 papers
cs.HC2026
Lost in Simulation: LLM-Simulated Users are Unreliable Proxies for Human Users in Agentic Evaluations
Preethi Seshadri, Samuel Cahyawijaya, Ayomide Odumakinde +2
Agentic benchmarks increasingly rely on LLM-simulated users to scalably evaluate agent performance, yet the robustness, validity, and fairness of this approach remain unexamined. T…
cs.CL2024
Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual Progress
Ayomide Odumakinde, Daniel D'souza, Pat Verga +2
The use of synthetic data has played a critical role in recent state-of-art breakthroughs. However, overly relying on a single oracle teacher model to generate data has been shown…