Papers by Wasi Uddin Ahmad
AVATAR: A Parallel Corpus for Java-Python Program Translation (2023.findings-acl)
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| Challenge: | Program translation is a time-consuming and costly process that requires expertise in both the source and target languages. |
| Approach: | They present a collection of 9,515 programming problems and their solutions written in Java and Python. |
| Outcome: | The proposed model lacks in generating functionally accurate code. |
Retrieval Enhanced Data Augmentation for Question Answering on Privacy Policies (2023.eacl-main)
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| Challenge: | Existing labeled datasets are heavily imbalanced, limiting the QA performance in this domain. |
| Approach: | They propose a question answering task that captures relevant text segments from unlabeled policy documents and expands the positive examples in the training set. |
| Outcome: | The proposed framework elevates the baseline by a large margin (10% F1) and achieves a new state-of-the-art F1 score of 50%. |
Scaling Test-Time Compute to Achieve IOI Gold Medal with Open-Weight Models (2026.acl-long)
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Mehrzad Samadi, Aleksander Ficek, Sean Narenthiran, Siddhartha Jain, Wasi Uddin Ahmad, Somshubra Majumdar, Vahid Noroozi, Boris Ginsburg
| Challenge: | Competitive programming has become a rigorous benchmark for evaluating the reasoning and problem-solving capabilities of large language models (LLMs). |
| Approach: | They propose a scalable and reproducible test-time compute framework that achieves IOI gold-level performance using open-weight models. |
| Outcome: | The proposed framework achieves IOI gold-level performance using open-weight models . it scales consistently with available compute, narrowing the gap between open and closed systems. |
LibEvolutionEval: A Benchmark and Study for Version-Specific Code Generation (2025.naacl-long)
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Sachit Kuhar, Wasi Uddin Ahmad, Zijian Wang, Nihal Jain, Haifeng Qian, Baishakhi Ray, Murali Krishna Ramanathan, Xiaofei Ma, Anoop Deoras
| Challenge: | Recent code completion models focus on local file contexts, but do not fully capture the complexities of real-world software development. |
| Approach: | They propose a version-specific code-completion task across eight libraries as they evolve over the years and an in-depth analysis of two widely used public libraries: PyTorch and Matplotlib. |
| Outcome: | The proposed model improves performance with public libraries, compared with existing models. |
Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions for Large Language Models (2025.acl-industry)
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Somshubra Majumdar, Vahid Noroozi, Mehrzad Samadi, Sean Narenthiran, Aleksander Ficek, Wasi Uddin Ahmad, Jocelyn Huang, Jagadeesh Balam, Boris Ginsburg
| Challenge: | Large Language Models (LLMs) require high quality instruction data for effective alignment, especially in code generation tasks where expert curated datasets are expensive to produce. |
| Approach: | They propose a scalable algorithm for synthesizing large-scale, high quality coding instructions using evolutionary principles. |
| Outcome: | The proposed approach generates 7.5 million coding instructions with a small seed population and is highly parallelizable and effective even with weaker generator models. |
PLUE: Language Understanding Evaluation Benchmark for Privacy Policies in English (2023.acl-short)
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| Challenge: | Existing efforts to understand privacy policies are limited by processing the language in a way exclusive to a single task focusing on certain privacy practices. |
| Approach: | They propose a privacy policy language understanding evaluation benchmark to evaluate the understanding of privacy policies across multiple tasks. |
| Outcome: | The proposed framework improves the understanding of privacy policies across multiple tasks. |
CrossSum: Beyond English-Centric Cross-Lingual Summarization for 1,500+ Language Pairs (2023.acl-long)
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| Challenge: | a large-scale cross-lingual summarization dataset is available for free . a cross-linguistic summarizing model can be trained in any target language . |
| Approach: | They propose a multistage data sampling algorithm to train a cross-lingual summarization model capable of summarizing an article in any target language. |
| Outcome: | The proposed model outperforms baseline models on ROUGE and LaSE. |
ContraCLM: Contrastive Learning For Causal Language Model (2023.acl-long)
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Nihal Jain, Dejiao Zhang, Wasi Uddin Ahmad, Zijian Wang, Feng Nan, Xiaopeng Li, Ming Tan, Ramesh Nallapati, Baishakhi Ray, Parminder Bhatia, Xiaofei Ma, Bing Xiang
| Challenge: | Existing studies show that causal language models lack expressiveness due to poor discrimination ability. |
| Approach: | They propose a contrastive learning framework that enhances discrimination of representations and bridges the gap with encoder-only models. |
| Outcome: | The proposed framework improves discrimination and source code generation capabilities on a variety of downstream tasks. |
BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla (2023.findings-eacl)
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| Challenge: | 'BanglaNLG' is a comprehensive benchmark for evaluating natural language generation models in Bangla, a widely spoken yet low-resource language. |
| Approach: | They propose to aggregate six conditional text generation tasks under the BanglaNLG benchmark and introduce a new dataset on dialogue generation in the process. |
| Outcome: | The proposed model outperforms several multilingual models by 9% absolute gain and 32% relative gain on all of these tasks. |
Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages (2023.eacl-main)
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| Challenge: | Recent developments of multilingual pre-trained sequence-to-sequence models for programming languages have been effective for a broad spectrum of downstream software engineering tasks. |
| Approach: | They propose to combine a source-to-target model with a target-tosource model trained in parallel. |
| Outcome: | The proposed approach performs competitively with state-of-the-art methods. |