Papers by Baishakhi Ray
ReCode: Robustness Evaluation of Code Generation Models (2023.acl-long)
Copied to clipboard
Shiqi Wang, Zheng Li, Haifeng Qian, Chenghao Yang, Zijian Wang, Mingyue Shang, Varun Kumar, Samson Tan, Baishakhi Ray, Parminder Bhatia, Ramesh Nallapati, Murali Krishna Ramanathan, Dan Roth, Bing Xiang
| Challenge: | Existing work on robustness in text or code tasks has focused on classification, while robustness for code generation tasks is an uncharted area. |
| Approach: | They propose a robustness evaluation benchmark for code generation models that customizes over 30 transformations specifically for code on docstrings, function and variable names, code syntax, and code format. |
| Outcome: | The proposed model performs better on human annotators and on SOTA models with human annnotators. |
A Static Evaluation of Code Completion by Large Language Models (2023.acl-industry)
Copied to clipboard
Hantian Ding, Varun Kumar, Yuchen Tian, Zijian Wang, Rob Kwiatkowski, Xiaopeng Li, Murali Krishna Ramanathan, Baishakhi Ray, Parminder Bhatia, Sudipta Sengupta
| Challenge: | Large language models trained on code have shown great potential to increase productivity of software developers. |
| Approach: | They propose a static evaluation framework to quantify static errors in Python code completions by leveraging Abstract Syntax Trees. |
| Outcome: | The proposed framework is more efficient and applicable to code in the wild. |
LibEvolutionEval: A Benchmark and Study for Version-Specific Code Generation (2025.naacl-long)
Copied to clipboard
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. |
Benchmarking Large Language Models Under Data Contamination: A Survey from Static to Dynamic Evaluation (2025.emnlp-main)
Copied to clipboard
Simin Chen, Yiming Chen, Zexin Li, Yifan Jiang, Zhongwei Wan, Yixin He, Dezhi Ran, Tianle Gu, Haizhou Li, Tao Xie, Baishakhi Ray
| Challenge: | In the era of evaluating large language models, data contamination is an increasingly prominent concern . static benchmarking has been used for evaluation, but there are limitations of *dynamic* benchmarks . |
| Approach: | They propose a series of optimal design principles for *dynamic* benchmarking and analyze the limitations of existing *static* benchmarks. |
| Outcome: | The proposed benchmarks highlight a critical gap in the evaluation of LLMs. |
Unified Pre-training for Program Understanding and Generation (2021.naacl-main)
Copied to clipboard
| Challenge: | PLUG is a programming language that is used for programming and language understanding and generation tasks. |
| Approach: | They propose a sequence-to-sequence model that performs a broad spectrum of program and language understanding and generation tasks. |
| Outcome: | The proposed model outperforms or rivals state-of-the-art models on code summarization, code generation, and code translation tasks in seven programming languages. |
CodeFort: Robust Training for Code Generation Models (2024.findings-emnlp)
Copied to clipboard
Yuhao Zhang, Shiqi Wang, Haifeng Qian, Zijian Wang, Mingyue Shang, Linbo Liu, Sanjay Krishna Gouda, Baishakhi Ray, Murali Krishna Ramanathan, Xiaofei Ma, Anoop Deoras
| Challenge: | Existing research efforts to improve code generation models are inadequate . code generation model performance is degraded under small perturbations . |
| Approach: | They propose a framework to improve the robustness of code generation models by generalizing code perturbations to enrich training data and enabling various robust training strategies. |
| Outcome: | The proposed framework increases pass rates and robustness drop rate against code-syntax perturbations. |
Towards Learning (Dis)-Similarity of Source Code from Program Contrasts (2022.acl-long)
Copied to clipboard
| Challenge: | Existing models that focus on identifying functional (dis)similarity of source code get confused when trying to identify functional (Dis)-similarities. |
| Approach: | They propose to pre-train a Transformer model with such automatically generated program contrasts to better identify similar code in the wild and differentiate vulnerable programs from benign ones. |
| Outcome: | The proposed model outperforms existing models in vulnerability and code clone detection tasks even with much less data. |
ContraCLM: Contrastive Learning For Causal Language Model (2023.acl-long)
Copied to clipboard
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. |
Building Language Models for Text with Named Entities (P18-1)
Copied to clipboard
| Challenge: | Existing language models fail to predict the entity names due to their wide variations. |
| Approach: | They propose a language model which can learn the entity names by leveraging their entity type information. |
| Outcome: | The proposed model achieves 52.2% better perplexity in recipe generation and 22.06% on code generation than state-of-the-art language models. |
A Transformer-based Approach for Source Code Summarization (2020.acl-main)
Copied to clipboard
| Challenge: | Generating a readable summary that describes the functionality of a program is known as source code summarization. |
| Approach: | They propose a Transformer model that uses a self-attention mechanism to capture long-range dependencies by encoding source code tokens relative to the code token position. |
| Outcome: | The proposed model outperforms the state-of-the-art methods by a significant margin. |
Reasoning in Token Economies: Budget-Aware Evaluation of LLM Reasoning Strategies (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing evaluations that focus on performance metrics miss a key factor: increased effectiveness due to additional compute. |
| Approach: | They propose to incorporate the compute budget into evaluations to provide a more informative comparison that takes into account both performance metrics and computational cost. |
| Outcome: | The proposed framework outperforms reasoning strategies when they use comparable compute resources. |
Retrieval Augmented Code Generation and Summarization (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Software developers often recall parts of source code or code summaries that they had written in the past while implementing software or documenting them. |
| Approach: | They propose a retrieval augmented framework that retrieves relevant code or summaries from a database and provides them as a supplement to code generation or summarization models. |
| Outcome: | The proposed framework can search for relevant code or summaries from retrieval databases and can work with unimodal (only code or natural language description) or bimodal instances (code-description pairs). |
PropTest: Automatic Property Testing for Improved Visual Programming (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Visual Programming is an alternative to end-to-end black-box visual reasoning models. |
| Approach: | They propose a visual programming strategy that leverages Large Language Models to generate the logic of a program in the form of its source code. |
| Outcome: | The proposed method improves ViperGPT on visual question answering and referring expression comprehension with an LLM. |
Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages (2023.eacl-main)
Copied to clipboard
| 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. |