3 papers
cs.CL2026
Sampling More, Getting Less: Calibration is the Diversity Bottleneck in LLMs
Amin Banayeeanzade, Qingchuan Yang, Dhruv Tarsadiya +6
Diversity is essential for language-model applications ranging from creative generation to scientific discovery, yet modern LLMs often collapse into a narrow subset of plausible ou…
cs.LG2026
f-INE: A Hypothesis Testing Framework for Estimating Influence under Training Randomness
Subhodip Panda, Dhruv Tarsadiya, Shashwat Sourav +2
Influence estimation methods promise to explain and debug machine learning by estimating the impact of individual samples on the final model. Yet, existing methods collapse under t…
cs.CL2025
Sample, Align, Synthesize: Graph-Based Response Synthesis with ConGrs
Sayan Ghosh, Shahzaib Saqib Warraich, Dhruv Tarsadiya +2
Language models can be sampled multiple times to access the distribution underlying their responses, but existing methods cannot efficiently synthesize rich epistemic signals acros…