Papers by Aditya Sharma
TwiRGCN: Temporally Weighted Graph Convolution for Question Answering over Temporal Knowledge Graphs (2023.eacl-main)
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| Challenge: | Recent years have witnessed interest in Temporal Question Answering over Knowledge Graphs (TKGQA) but these methods are highly engineered and do not automatically discover relevant parts of the KG during multi-hop reasoning. |
| Approach: | They propose a scheme to modulate the messages passed through a KG edge during convolution based on the relevance of its associated period to the question. |
| Outcome: | The proposed system outperforms state-of-the-art models on a recent challenging dataset for multi-hop complex temporal QA called TimeQuestions. |
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. |
EduVidQA: Generating and Evaluating Long-form Answers to Student Questions based on Lecture Videos (2025.emnlp-main)
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| Challenge: | This paper explores using Multimodal Large Language Models (MLLMs) to respond to student questions from online lectures . MLLM is a novel question answering task of real world significance . |
| Approach: | They propose to use Multimodal Large Language Models to automatically respond to student questions from online lectures by using a dataset of 5252 question-answer pairs from 296 computer science videos. |
| Outcome: | The proposed model can fine tune and fine tune questions from 296 computer science videos and show that students' preferences are important to the task. |
Towards Understanding the Geometry of Knowledge Graph Embeddings (P18-1)
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| Challenge: | Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embeddable methods. |
| Approach: | They propose to use KG embedding methods to represent entities and relations as vectors in a high-dimensional space. |
| Outcome: | The proposed methods represent entities and relations in KGs as vectors in a high-dimensional space. |
Ranking LLM-Generated Loop Invariants for Program Verification (2023.findings-emnlp)
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Saikat Chakraborty, Shuvendu Lahiri, Sarah Fakhoury, Akash Lal, Madanlal Musuvathi, Aseem Rastogi, Aditya Senthilnathan, Rahul Sharma, Nikhil Swamy
| Challenge: | Large Language Models (LLMs) are capable of synthesizing inductive loop invariants for a class of programs in a 0-shot setting, yet require several samples to generate the correct invariant. |
| Approach: | They propose a re-ranking approach to generate inductive loop invariants using Large Language Models . they propose reranking rankers that can distinguish between correct and incorrect attempts . |
| Outcome: | The proposed method reduces the number of calls to a verifier by comparing the generated results with the original model. |
Exploring the Boundaries of GPT-4 in Radiology (2023.emnlp-main)
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Qianchu Liu, Stephanie Hyland, Shruthi Bannur, Kenza Bouzid, Daniel Castro, Maria Wetscherek, Robert Tinn, Harshita Sharma, Fernando Pérez-García, Anton Schwaighofer, Pranav Rajpurkar, Sameer Khanna, Hoifung Poon, Naoto Usuyama, Anja Thieme, Aditya Nori, Matthew Lungren, Ozan Oktay, Javier Alvarez-Valle
| Challenge: | Recent success of general-domain large language models has changed the natural language processing paradigm towards a unified foundation model across domains and applications. |
| Approach: | They evaluate the performance of GPT-4 on a variety of radiology tasks . they find it outperforms or matches current SOTA radiology models . |
| Outcome: | The proposed model outperforms or matches current SOTA radiology models on a range of tasks. |
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. |
Losing Visual Needles in Image Haystacks: Vision Language Models are Easily Distracted in Short and Long Contexts (2024.findings-emnlp)
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| Challenge: | evaluators of long-context vision language models (VLMs) have not kept up with the rapid development of open-weight long-constraint language models. |
| Approach: | They propose a dynamic benchmark generator for evaluating long-context reasoning in vision language models. |
| Outcome: | The proposed model can ignore irrelevant information when answering queries, showing that current models lack this capability. |