2 citations · 2 across the 3 of their papers we have counts for
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Curate-Train-Refine: A Closed-Loop Agentic Framework for Zero Shot Classification
Gaurav Maheshwari, Kevin El Haddad
Large language models (LLMs) and high-capacity encoders have advanced zero and few-shot classification, but their inference cost and latency limit practical deployment. We propose…
ASR Benchmarking: Need for a More Representative Conversational Dataset
Gaurav Maheshwari, Dmitry Ivanov, Théo Johannet +1
Automatic Speech Recognition (ASR) systems have achieved remarkable performance on widely used benchmarks such as LibriSpeech and Fleurs. However, these benchmarks do not adequatel…
Efficacy of Synthetic Data as a Benchmark
Gaurav Maheshwari, Dmitry Ivanov, Kevin El Haddad
Large language models (LLMs) have enabled a range of applications in zero-shot and few-shot learning settings, including the generation of synthetic datasets for training and testi…