1 citations · 1 across the 5 of their papers we have counts for
6 papers
Do LLM Tutors Actually Teach? A Process-Centered Synthesis of the Tutoring Loop
Sayantan Adak, Rima Hazra, Koyel Mukherjee +2
Large language models can produce fluent tu- toring dialogue, but whether they actually help students learn remains unclear. We present a process-centered synthesis of LLM tutoring…
Online Learning with LLM Experts from Limited Feedback
Wang Wei, Soumyabrata Pal, Koyel Mukherjee +4
We study adaptive routing of prompts to large language model (LLM) experts to maximize response quality in an online setting with limited feedback. We formulate it as a bandit prob…
An Answer is just the Start: Related Insight Generation for Open-Ended Document-Grounded QA
Saransh Sharma, Pritika Ramu, Aparna Garimella +1
Answering open-ended questions remains challenging for AI systems because it requires synthesis, judgment, and exploration beyond factual retrieval, and users often refine answers…
From Selection to Generation: A Survey of LLM-based Active Learning
Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie +31
Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points for labeling and training. In recent…
PromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic Languages with Example Selection from Related Example Banks
Soumya Suvra Ghosal, Soumyabrata Pal, Koyel Mukherjee +1
Large Language Models (LLMs) have recently demonstrated impressive few-shot learning capabilities through in-context learning (ICL). However, ICL performance is highly dependent on…
FiRST: Finetuning Router-Selective Transformers for Input-Adaptive Latency Reduction
Akriti Jain, Saransh Sharma, Koyel Mukherjee +1
Auto-regressive Large Language Models (LLMs) demonstrate remarkable performance across different domains such as vision and language processing. However, due to sequential processi…