Papers by Nickil Maveli
Co-training an Unsupervised Constituency Parser with Weak Supervision (2022.findings-acl)
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| Challenge: | Existing methods for unsupervised parsing that use bootstrapping classifiers to identify if a node dominates a span are lacking. |
| Approach: | They propose a method for unsupervised parsing that relies on bootstrapping classifiers to identify if a node dominates a specific span. |
| Outcome: | The proposed method achieves 63.1 F1 on the English test set and new state-of-the-art on treebanks for Chinese and Japanese. |
Can LLMs Compress (and Decompress)? Evaluating Code Understanding and Execution via Invertibility (2026.findings-acl)
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| Challenge: | a recent development of code-LLMs has demonstrated remarkable performance across various software engineering applications. |
| Approach: | They propose a round-trip code execution reasoning task to test round- trip consistency . they use zero-shot prompting, supervised fine-tuning on execution traces and self-reflection mechanisms to evaluate models . |
| Outcome: | The proposed benchmarks show that LLMs struggle with round-trip consistency . the benchmarks lack the internal coherence required for trustworthy code reasoning . |
What can Large Language Models Capture about Code Functional Equivalence? (2025.findings-naacl)
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| Challenge: | SeqCoBench is a benchmark to assess how Code-LLMs can capture code semantics. |
| Approach: | They propose a benchmark to assess how Code-LLMs capture code semantics . they use seqCoBench to evaluate whether they can discern semantically equivalent or different pairs of programs . |
| Outcome: | The proposed benchmarks show that they can capture code semantics better than classical match-based retrieval scores. |