Papers by Aditya Pal
Reducing Hallucinations in Language Model-based SPARQL Query Generation Using Post-Generation Memory Retrieval (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) are susceptible to hallucinations and out-of-distribution errors when generating KG elements, such as Uniform Resource Identifiers (URIs). |
| Approach: | They propose a SPARQL query-generating framework that uses natural language placeholders and a non-parametric memory module to retrieve and resolve the correct KG URIs. |
| Outcome: | The proposed framework significantly enhances query correctness across various LLMs, datasets, and distribution shifts while achieving the near-complete suppression of URI hallucinations. |
CLIP-UP: A Simple and Efficient Mixture-of-Experts CLIP Training Recipe with Sparse Upcycling (2025.findings-emnlp)
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Xinze Wang, Chen Chen, Yinfei Yang, Hong-You Chen, Bowen Zhang, Aditya Pal, Xiangxin Zhu, Xianzhi Du
| Challenge: | Mixture-of-Experts (MoE) models are crucial for scaling model capacity while controlling inference costs. |
| Approach: | They propose an alternative training strategy that converts a dense CLIP model into a sparse MoE architecture. |
| Outcome: | The proposed training strategy outperforms dense models on COCO and Flickr30k benchmarks. |
GeoCoder: Solving Geometry Problems by Generating Modular Code through Vision-Language Models (2025.findings-naacl)
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| Challenge: | Various vision-language models (VLMs) have made significant progress in multimodal tasks, but they still struggle with geometry problems. |
| Approach: | They propose a vision-language model that leverages modular code-finetuning to generate and execute code using a predefined geometry function library. |
| Outcome: | The proposed model improves geometric reasoning abilities by 16% on a GeomVerse dataset compared to other methods. |
Generative Floor Plan Design with LLMs via Reinforcement Learning with Verifiable Rewards (2026.findings-acl)
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Luis Lara, Aristides Milios, ZhiHao Luo, Aditya Sharma, Ge Ya Luo, Christopher Beckham, Florian Golemo, Christopher Pal
| Challenge: | Existing generative models focus on respecting the requested connectivity between rooms, but do not support generating floor plans that respect numerical constraints. |
| Approach: | They propose a text-based approach that fine-tunes a large language model on real plans and applies reinforcement learning with verifiable rewards to improve adherence to topological and numerical constraints. |
| Outcome: | The proposed model outperforms existing methods on Realism, Compatibility, Diversity metrics. |