Papers by Shubham Gandhi
Major Entity Identification: A Generalizable Alternative to Coreference Resolution (2024.emnlp-main)
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| Challenge: | Prior work identified annotation differences as one of the main reasons for the limited generalization gap in coreference resolution models. |
| Approach: | They propose an alternative referential task where the target entities are assumed to be specified in the input and the task is limited to the frequent entities. |
| Outcome: | The proposed model generalizes well across domains on multiple datasets with supervised models and LLM-based few-shot prompting. |
An Empirical Study on Strong-Weak Model Collaboration for Repo-level Code Generation (2025.emnlp-main)
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated impressive capabilities across complex reasoning and generation tasks. |
| Approach: | They evaluate a broad spectrum of collaboration strategies for repository-level code generation where the weak model handles simpler tasks at lower cost and the most challenging tasks are delegated to the strong model. |
| Outcome: | The proposed model achieves equivalent performance to the strong model while reducing the cost by 40%. |
IdentifyMe: A Challenging Long-Context Mention Resolution Benchmark for LLMs (2025.naacl-short)
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| Challenge: | Recent evaluations of LLMs on coreference resolution have revealed that traditional output formats and evaluation metrics do not fully capture the models’ referential understanding. |
| Approach: | They propose a benchmark for mention resolution presented in a multiple-choice question format and a curated mixture of different mention types and corresponding entities. |
| Outcome: | The proposed model achieves 81.9% accuracy while the open model achieve 80%. |