28 papers
Frontier AI performance across the business disciplines: a case-grounded benchmark of knowledge work and analytical reasoning
Ajay Patel, Kartik Hosanagar, Ramayya Krishnan +3
Large language models (LLMs) are improving rapidly as reflected in benchmark scores, yet these AI benchmarks largely test capabilities such as factual recall, narrow question answe…
FineInstructions: Scaling Synthetic Instructions to Pre-Training Scale
Ajay Patel, Colin Raffel, Chris Callison-Burch
Due to limited supervised training data, large language models (LLMs) are typically pre-trained via a self-supervised "predict the next word" objective on a vast amount of unstruct…
Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why
Delip Rao, Chris Callison-Burch
Whether a rubric-based LLM judge can replace human annotation is decided by its measured agreement with human labels. Yet the same verdicts can support wildly varying agreement num…
NSF-SciFy: Mining the NSF Awards Database for Scientific Claims
Delip Rao, Weiqiu You, Eric Wong +1
We introduce NSF-SciFy, a comprehensive dataset of scientific claims and investigation proposals extracted from National Science Foundation award abstracts. While previous scientif…
When Verification Fails: How Compositionally Infeasible Claims Escape Rejection
Muxin Liu, Delip Rao, Grace Kim +1
Scientific claim verification, the task of determining whether claims are entailed by scientific evidence, is fundamental to establishing discoveries in evidence while preventing m…
Detecting and Correcting Reference Hallucinations in Commercial LLMs and Deep Research Agents
Delip Rao, Eric Wong, Chris Callison-Burch
Large language models and deep research agents supply citation URLs to support their claims, yet the reliability of these citations has not been systematically measured. We address…