3 citations · 4 across the 4 of their papers we have counts for
5 papers
Don't Ask the LLM: Visual Token Pruning with Spectral Structure and Semantic Relevance
Intaek Shin, Geuntaek Lim, Sungjune Park +1
Large vision-language models (LVLMs) represent images and videos as long visual-token sequences, substantially increasing inference costs. These costs motivate visual-token pruning…
BankerToolBench: Evaluating AI Agents in End-to-End Investment Banking Workflows
Elaine Lau, Markus Dücker, Ronak Chaudhary +24
Existing AI benchmarks lack the fidelity to assess economically meaningful progress on professional workflows. To evaluate frontier AI agents in a high-value, labor-intensive profe…
Real-Time Trustworthiness Scoring for LLM Structured Outputs and Data Extraction
Hui Wen Goh, Jonas Mueller
Structured Outputs from current LLMs exhibit sporadic errors, hindering enterprise AI deployment. We present CONSTRUCT, a real-time uncertainty estimator that scores the trustworth…
ActiveLab: Active Learning with Re-Labeling by Multiple Annotators
Hui Wen Goh, Jonas Mueller
In real-world data labeling applications, annotators often provide imperfect labels. It is thus common to employ multiple annotators to label data with some overlap between their e…
CROWDLAB: Supervised learning to infer consensus labels and quality scores for data with multiple annotators
Hui Wen Goh, Ulyana Tkachenko, Jonas Mueller
Real-world data for classification is often labeled by multiple annotators. For analyzing such data, we introduce CROWDLAB, a straightforward approach to utilize any trained classi…