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20232026
most citedDemocratizing LLMs: An Exploration of Cost-Performance Trade-offs in Self-Refined Open-Source Models

9 citations · 10 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.CL2026

Measuring and Mitigating the Distributional Gap Between Real and Simulated User Behaviors

Shuhaib Mehri, Philippe Laban, Sumuk Shashidhar +4

As user simulators are increasingly used for interactive training and evaluation of AI assistants, it is essential that they represent the diverse behaviors of real users. While ex…

cs.CL2025

Question Generation for Assessing Early Literacy Reading Comprehension

Xiaocheng Yang, Sumuk Shashidhar, Dilek Hakkani-Tur

Assessment of reading comprehension through content-based interactions plays an important role in the reading acquisition process. In this paper, we propose a novel approach for ge…

cs.CL2025

AURA: A Diagnostic Framework for Tracking User Satisfaction of Interactive Planning Agents

Takyoung Kim, Janvijay Singh, Shuhaib Mehri +6

The growing capabilities of large language models (LLMs) in instruction-following and context-understanding lead to the era of agents with numerous applications. Among these, task…

cs.CL2025

YourBench: Easy Custom Evaluation Sets for Everyone

Sumuk Shashidhar, Clémentine Fourrier, Alina Lozovskia +3

Evaluating large language models (LLMs) effectively remains a critical bottleneck, as traditional static benchmarks suffer from saturation and contamination, while human evaluation…

cs.CL2024★ 1 cited

Unsupervised Human Preference Learning

Sumuk Shashidhar, Abhinav Chinta, Vaibhav Sahai +1

Large language models demonstrate impressive reasoning abilities but struggle to provide personalized content due to their lack of individual user preference information. Existing…

cs.CL2023★ 9 cited

Democratizing LLMs: An Exploration of Cost-Performance Trade-offs in Self-Refined Open-Source Models

Sumuk Shashidhar, Abhinav Chinta, Vaibhav Sahai +2

The dominance of proprietary LLMs has led to restricted access and raised information privacy concerns. High-performing open-source alternatives are crucial for information-sensiti…