50 citations · 117 across the 17 of their papers we have counts for
7 papers · 1 filter
BIG-Bench Extra Hard
Mehran Kazemi, Bahare Fatemi, Hritik Bansal +17
Large language models (LLMs) are increasingly deployed in everyday applications, demanding robust general reasoning capabilities and diverse reasoning skillset. However, current LL…
Entailed Between the Lines: Incorporating Implication into NLI
Shreya Havaldar, Hamidreza Alvari, John Palowitch +3
Much of human communication depends on implication, conveying meaning beyond literal words to express a wider range of thoughts, intentions, and feelings. For models to better unde…
Towards Realistic Synthetic User-Generated Content: A Scaffolding Approach to Generating Online Discussions
Krisztian Balog, John Palowitch, Barbara Ikica +3
The emergence of synthetic data represents a pivotal shift in modern machine learning, offering a solution to satisfy the need for large volumes of data in domains where real data…
SocialQuotes: Learning Contextual Roles of Social Media Quotes on the Web
John Palowitch, Hamidreza Alvari, Mehran Kazemi +2
Web authors frequently embed social media to support and enrich their content, creating the potential to derive web-based, cross-platform social media representations that can enab…
Into the Unknown: Generating Geospatial Descriptions for New Environments
Tzuf Paz-Argaman, John Palowitch, Sayali Kulkarni +2
Similar to vision-and-language navigation (VLN) tasks that focus on bridging the gap between vision and language for embodied navigation, the new Rendezvous (RVS) task requires rea…
Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning
Bahare Fatemi, Mehran Kazemi, Anton Tsitsulin +6
Large language models (LLMs) have showcased remarkable reasoning capabilities, yet they remain susceptible to errors, particularly in temporal reasoning tasks involving complex tem…
Where Do We Go from Here? Multi-scale Allocentric Relational Inference from Natural Spatial Descriptions
Tzuf Paz-Argaman, Sayali Kulkarni, John Palowitch +2
When communicating routes in natural language, the concept of acquired spatial knowledge is crucial for geographic information retrieval (GIR) and in spatial cognitive research. Ho…