Papers by Ishaan Gupta

2 papers
LLMs are Brittle to Simple Code Transformations: Introducing CETBench – A Benchmark for Code-Equivalence Checking (2026.findings-acl)

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Challenge: a new benchmarking tool for code equivalence checks the performance of LLMs.
Approach: They propose a code-equivalence with transformations benchmark built from a repository of programs that may solve the same or different tasks.
Outcome: The proposed approach boosts performance on the transformed pairs of programs.
Which Modality should I use - Text, Motif, or Image? : Understanding Graphs with Large Language Models (2024.findings-naacl)

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Challenge: Current research typically employs limited setups with small real-world graphs.
Approach: They propose a new approach to encoding a graph with diverse modalities, such as text, image, and motif, coupled with prompts to approximate a diagram’s global connectivity.
Outcome: The proposed approach improves performance of LLMs in graph structure analysis by focusing on homophily, motif presence, and graph difficulty.

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