44 citations · 125 across the 34 of their papers we have counts for
4 papers · 1 filter
Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead
Vidhisha Balachandran, Jingya Chen, Lingjiao Chen +8
Inference-time scaling can enhance the reasoning capabilities of large language models (LLMs) on complex problems that benefit from step-by-step problem solving. Although lengtheni…
Unearthing Skill-Level Insights for Understanding Trade-Offs of Foundation Models
Mazda Moayeri, Vidhisha Balachandran, Varun Chandrasekaran +6
With models getting stronger, evaluations have grown more complex, testing multiple skills in one benchmark and even in the same instance at once. However, skill-wise performance i…
BenchAgents: Multi-Agent Systems for Structured Benchmark Creation
Natasha Butt, Varun Chandrasekaran, Neel Joshi +2
Evaluation insights are limited by the availability of high-quality benchmarks. As models evolve, there is a need to create benchmarks that can measure progress on new and complex…
Eureka: Evaluating and Understanding Large Foundation Models
Vidhisha Balachandran, Jingya Chen, Neel Joshi +6
Rigorous and reproducible evaluation is critical for assessing the state of the art and for guiding scientific advances in Artificial Intelligence. Evaluation is challenging in pra…