1k citations · 1k across the 8 of their papers we have counts for
8 papers
BlueFin: Benchmarking LLM Agents on Financial Spreadsheets
Srivatsa Kundurthy, Clara Na, Colton Moraine +6
We present BlueFin, a benchmark that tasks large language model (LLM) agents with synthesis, manipulation, and comprehension tasks over spreadsheet workbooks in the professional fi…
Prescriptive Scaling Laws for Data Constrained Training
Justin Lovelace, Christian Belardi, Srivatsa Kundurthy +2
Training compute is increasingly outpacing the availability of high-quality data. This shifts the central challenge from optimal compute allocation to extracting maximum value from…
SpreadsheetArena: Decomposing Preference in LLM Generation of Spreadsheet Workbooks
Srivatsa Kundurthy, Clara Na, Michael Handley +5
We consider the task of end-to-end spreadsheet generation, where language models produce spreadsheet artifacts to satisfy users' explicit and implicit constraints, specified in nat…
Stop-Think-AutoRegress: Language Modeling with Latent Diffusion Planning
Justin Lovelace, Christian Belardi, Sofian Zalouk +3
The Stop-Think-AutoRegress Language Diffusion Model (STAR-LDM) integrates latent diffusion planning with autoregressive generation. Unlike conventional autoregressive language mode…
Imitation Learning for Multi-turn LM Agents via On-policy Expert Corrections
Niklas Lauffer, Xiang Deng, Srivatsa Kundurthy +2
A popular paradigm for training LM agents relies on imitation learning, fine-tuning on expert trajectories. However, we show that the off-policy nature of imitation learning for mu…
SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?
Xiang Deng, Jeff Da, Edwin Pan +19
We introduce SWE-Bench Pro, a substantially more challenging benchmark that builds upon the best practices of SWE-BENCH [25], but is explicitly designed to capture realistic, compl…
LAION-5B: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu +13
Groundbreaking language-vision architectures like CLIP and DALL-E proved the utility of training on large amounts of noisy image-text data, without relying on expensive accurate la…
LANTERN-RD: Enabling Deep Learning for Mitigation of the Invasive Spotted Lanternfly
Srivatsa Kundurthy
The Spotted Lanternfly (SLF) is an invasive planthopper that threatens the local biodiversity and agricultural economy of regions such as the Northeastern United States and Japan.…