Papers by Rajarshi Haldar
CL Scholar: The ACL Anthology Knowledge Graph Miner (N18-5)
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Mayank Singh, Pradeep Dogga, Sohan Patro, Dhiraj Barnwal, Ritam Dutt, Rajarshi Haldar, Pawan Goyal, Animesh Mukherjee
| Challenge: | ACL Anthology is a repository for papers related to computational linguistics and natural language processing. |
| Approach: | They propose to automate periodic crawling, indexing and processing of new articles . they propose to use CL Scholar to support more than 1200 natural language queries . |
| Outcome: | The proposed system can answer three different types of natural language queries. |
A Multi-Perspective Architecture for Semantic Code Search (2020.acl-main)
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| Challenge: | Existing models do not model interactions between code and description until the final step when their global similarity is calculated. |
| Approach: | They propose a multi-perspective cross-lingual neural framework for code–text matching that captures both global and local similarities. |
| Outcome: | The proposed model performs better on the CoNaLa dataset than previous approaches that map code and text to a single joint embedding space. |
Analyzing the Performance of Large Language Models on Code Summarization (2024.lrec-main)
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| Challenge: | Large language models perform very well on tasks that involve both natural language and source code. |
| Approach: | They show that large language models perform very well on tasks that involve both natural language and source code. |
| Outcome: | The proposed models perform very well on tasks that involve both natural language and source code. |
Rating Roulette: Self-Inconsistency in LLM-As-A-Judge Frameworks (2025.findings-emnlp)
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| Challenge: | Using large language models (LLMs) for evaluating natural language generation has gained traction . lm judges have low intra-rater reliability in their assigned scores, making it difficult to measure how good their judgments actually are. |
| Approach: | They show that large language models align more closely with human preferences than n-grams . they quantify this variance and compare them to other NLG tasks and benchmarks based on the results . |
| Outcome: | The proposed models align more closely with human preferences than n-gram or embedding-based metrics. |