Papers by John Palowitch
Where Do We Go From Here? Multi-scale Allocentric Relational Inferencefrom Natural Spatial Descriptions (2024.eacl-long)
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| Challenge: | Current NLP navigation studies focus on egocentric local descriptions that require reasoning over the agent’s local perception. |
| Approach: | They propose to use a dataset to analyse English geospatial instructions to find locations and paths from natural language descriptions. |
| Outcome: | The proposed task and dataset includes 10,404 examples of English geospatial instructions for reaching a target location using map-knowledge. |
BIG-Bench Extra Hard (2025.acl-long)
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Mehran Kazemi, Bahare Fatemi, Hritik Bansal, John Palowitch, Chrysovalantis Anastasiou, Sanket Vaibhav Mehta, Lalit K Jain, Virginia Aglietti, Disha Jindal, Peter Chen, Nishanth Dikkala, Gladys Tyen, Xin Liu, Uri Shalit, Silvia Chiappa, Kate Olszewska, Yi Tay, Vinh Q. Tran, Quoc V Le, Orhan Firat
| Challenge: | Current benchmarks for large language model reasoning focus on math and coding abilities, leaving a gap in evaluating broader reasoning proficiencies. |
| Approach: | They propose a benchmark to evaluate general reasoning in large language models . they use BIG-Bench and its harder version BIG-Benefit Hard to assess general reasoning . |
| Outcome: | The new benchmark pushes the boundaries of LLM reasoning evaluation. |
Into the Unknown: Generating Geospatial Descriptions for New Environments (2024.findings-acl)
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| Challenge: | Similar to vision-and-language navigation tasks, the Rendezvous (RVS) task requires reasoning over allocentric spatial relationships using non-sequential navigation instructions and maps. |
| Approach: | They propose a large-scale augmentation method for generating high-quality synthetic data for new environments using readily available geospatial data. |
| Outcome: | The proposed method improves accuracy on unseen and seen environments by 45.83% on the Rendezvous (RVS) task. |
Entailed Between the Lines: Incorporating Implication into NLI (2025.acl-long)
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Shreya Havaldar, Hamidreza Alvari, John Palowitch, Mohammad Javad Hosseini, Senaka Buthpitiya, Alex Fabrikant
| Challenge: | True Emotions, social cues, insults, and a myriad of other messages are conveyed implicitly, often even more so than explicitly. |
| Approach: | They propose a dataset to help LLMs understand implied entailment . |
| Outcome: | The proposed dataset enables LLMs to understand implied entailment and can generalize this understanding across datasets and domains. |