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Rama Kumar Pasumarthi

4 accepted papers

2025

Chatbot Arena Estimate: towards a generalized performance benchmark for LLM capabilities

NAACL 2025industry

In industrial LLM development, evaluating large language models (LLMs) is critical for tasks like benchmarking internal models and detecting regressions during fine-tuning, but existing benchmark aggregation methods, such as Elo-based systems, can be resource-intensive, public facing, and time-consu…

2023

RD-Suite: A Benchmark for Ranking Distillation

NeurIPS 2023poster

The distillation of ranking models has become an important topic in both academia and industry. In recent years, several advanced methods have been proposed to tackle this problem, often leveraging ranking information from teacher rankers that is absent in traditional classification settings. To dat…

Cited by 7SourcePDFScholar
2021

Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees?

ICLR 2021spotlight

Despite the success of neural models on many major machine learning problems, their effectiveness on traditional Learning-to-Rank (LTR) problems is still not widely acknowledged. We first validate this concern by showing that most recent neural LTR models are, by a large margin, inferior to the best…

Cited by 131SourcePDFScholar
2021

RankDistil: Knowledge Distillation for Ranking

AISTATS 2021poster

Knowledge distillation is an approach to improve the performance of a student model by using the knowledge of a complex teacher. Despite its success in several deep learning applications, the study of distillation is mostly confined to classification settings. In particular, the use of distillation…

Cited by 38SourcePDFScholar