2 citations · 9 across the 19 of their papers we have counts for
4 papers · 1 filter
Beyond Naïve Prompting: Strategies for Improved Context-aided Forecasting with LLMs
Arjun Ashok, Andrew Robert Williams, Vincent Zhihao Zheng +5
Real-world forecasting requires models to integrate not only historical data but also relevant contextual information provided in textual form. While large language models (LLMs) s…
Learning to Defer for Causal Discovery with Imperfect Experts
Oscar Clivio, Divyat Mahajan, Perouz Taslakian +4
Integrating expert knowledge, e.g. from large language models, into causal discovery algorithms can be challenging when the knowledge is not guaranteed to be correct. Expert recomm…
PairBench: Are Vision-Language Models Reliable at Comparing What They See?
Aarash Feizi, Sai Rajeswar, Adriana Romero-Soriano +4
Understanding how effectively large vision language models (VLMs) compare visual inputs is crucial across numerous applications, yet this fundamental capability remains insufficien…
Hierarchical Retrieval at Scale: Bridging Transparency and Efficiency
Shubham Gupta, Zichao Li, Tianyi Chen +4
Information retrieval is a core component of many intelligent systems as it enables conditioning of outputs on new and large-scale datasets. While effective, the standard practice…