2 citations · 5 across the 13 of their papers we have counts for
14 papers
Google Earth AI Geospatial Reasoning Agent: Capabilities and a Systematic Evaluation
Noam Slonim, Aviv Slobodkin, Gil Fidel +34
Google Earth AI is a family of geospatial AI models, together with an agent that orchestrates them. We present this agent, Google Earth AI's Geospatial Reasoning agent (GRA), a Gem…
Planetary Prediction Engine: Autonomous Geospatial Prediction via Intelligent Data Selection and Foundation Model Embeddings
Evelyn Ma, Rama Kumar Pasumarthi, Kishwar Shafin +26
Addressing critical global challenges, from food security and disaster risk to disease outbreaks and socio-economic vulnerability, demands high-fidelity geospatial modeling. Howeve…
Enabling Intrinsic Reasoning over Dense Geospatial Embeddings with DFR-Gemma
Xuechen Zhang, Aviv Slobodkin, Joydeep Paul +4
Representation learning for geospatial and spatio-temporal data plays a critical role in enabling general-purpose geospatial intelligence. Recent geospatial foundation models, such…
Utilizing Metadata for Better Retrieval-Augmented Generation
Raquib Bin Yousuf, Shengzhe Xu, Mandar Sharma +3
Retrieval-Augmented Generation systems depend on retrieving semantically relevant document chunks to support accurate, grounded outputs from large language models. In structured an…
Can an LLM Induce a Graph? Investigating Memory Drift and Context Length
Raquib Bin Yousuf, Aadyant Khatri, Shengzhe Xu +2
Recently proposed evaluation benchmarks aim to characterize the effective context length and the forgetting tendencies of large language models (LLMs). However, these benchmarks of…
LLM Augmentations to support Analytical Reasoning over Multiple Documents
Raquib Bin Yousuf, Nicholas Defelice, Mandar Sharma +2
Building on their demonstrated ability to perform a variety of tasks, we investigate the application of large language models (LLMs) to enhance in-depth analytical reasoning within…
Information Guided Regularization for Fine-tuning Language Models
Mandar Sharma, Nikhil Muralidhar, Shengzhe Xu +2
The pretraining-fine-tuning paradigm has been the de facto strategy for transfer learning in modern language modeling. With the understanding that task adaptation in LMs is often a…
Why LLMs Are Bad at Synthetic Table Generation (and what to do about it)
Shengzhe Xu, Cho-Ting Lee, Mandar Sharma +3
Synthetic data generation is integral to ML pipelines, e.g., to augment training data, replace sensitive information, and even to power advanced platforms like DeepSeek. While LLMs…
Laying Anchors: Semantically Priming Numerals in Language Modeling
Mandar Sharma, Rutuja Murlidhar Taware, Pravesh Koirala +2
Off-the-shelf pre-trained language models have become the de facto standard in NLP pipelines for a multitude of downstream tasks. However, the inability of these models to properly…
Learning Non-linguistic Skills without Sacrificing Linguistic Proficiency
Mandar Sharma, Nikhil Muralidhar, Naren Ramakrishnan
The field of Math-NLP has witnessed significant growth in recent years, motivated by the desire to expand LLM performance to the learning of non-linguistic notions (numerals, and s…
Overcoming Barriers to Skill Injection in Language Modeling: Case Study in Arithmetic
Mandar Sharma, Nikhil Muralidhar, Naren Ramakrishnan
Through their transfer learning abilities, highly-parameterized large pre-trained language models have dominated the NLP landscape for a multitude of downstream language tasks. Tho…
Innovations in Neural Data-to-text Generation: A Survey
Mandar Sharma, Ajay Gogineni, Naren Ramakrishnan
The neural boom that has sparked natural language processing (NLP) research through the last decade has similarly led to significant innovations in data-to-text generation (DTG). T…
TCube: Domain-Agnostic Neural Time-series Narration
Mandar Sharma, John S. Brownstein, Naren Ramakrishnan
The task of generating rich and fluent narratives that aptly describe the characteristics, trends, and anomalies of time-series data is invaluable to the sciences (geology, meteoro…
Once Upon A Time In Visualization: Understanding the Use of Textual Narratives for Causality
Arjun Choudhry, Mandar Sharma, Pramod Chundury +4
Causality visualization can help people understand temporal chains of events, such as messages sent in a distributed system, cause and effect in a historical conflict, or the inter…