Papers by Nickvash Kani
STEM-POM: Evaluating Language Models Math-Symbol Reasoning in Document Parsing (2025.findings-acl)
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| Challenge: | Advances in large language models have spurred research into enhancing their reasoning capabilities, particularly in math-rich STEM documents. |
| Approach: | They propose a benchmark dataset to evaluate LLMs’ reasoning abilities on math symbols within contextual scientific text. |
| Outcome: | The proposed dataset demonstrates that state-of-the-art LLMs achieve an average accuracy of 20-60% under in-context learning and 50-60% with fine-tuning, highlighting a substantial gap in their ability to classify mathematical symbols. |
E-Gen: Leveraging E-Graphs to Improve Continuous Representations of Symbolic Expressions (2025.naacl-long)
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| Challenge: | Existing methods for embedding mathematical expressions are limited by the size and diversity of training data. |
| Approach: | They propose an e-graph-based dataset generation scheme that synthesizes large and diverse datasets. |
| Outcome: | The proposed method outperforms state-of-the-art large language models on several tasks. |
MaRF: Leveraging Representation-Level Fusion of Formula Semantics for Mathematical Information Retrieval (2026.findings-acl)
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| Challenge: | Mathematical information retrieval (MIR) relies on combining textual content with mathematical expressions. |
| Approach: | They propose a dual-encoder representation-level fusion framework for MIR that integrates formula semantics into context-aware dense retrieval. |
| Outcome: | The proposed framework outperforms baselines on the ARQMath-3 benchmark. |