6 papers
Mask-to-Correct: Leveraging Retriever Diversity for Masking-guided Faithful Fact Correction
Payel Santra, Lavisha Sharma, Madhusudan Ghosh +1
The rapid spread of misinformation on social media highlights the need for robust, automated fact correction frameworks. However, existing works rely on supervised learning from ma…
An Evaluation of Context Length Extrapolation in Long Code via Positional Embeddings and Efficient Attention
Madhusudan Ghosh, Rishabh Gupta
The rapid advancement of large language models (LLMs) has led to a significant increase in automated tools in the software engineering, capable of performing various code-related t…
LLM Based Long Code Translation using Identifier Replacement
Manojit Chakraborty, Madhusudan Ghosh, Rishabh Gupta
In the domain of software development, LLMs have been utilized to automate tasks such as code translation, where source code from one programming language is translated to another…
HF-RAG: Hierarchical Fusion-based RAG with Multiple Sources and Rankers
Payel Santra, Madhusudan Ghosh, Debasis Ganguly +2
Leveraging both labeled (input-output associations) and unlabeled data (wider contextual grounding) may provide complementary benefits in retrieval augmented generation (RAG). Howe…
In-Context Learning as an Effective Estimator of Functional Correctness of LLM-Generated Code
Susmita Das, Madhusudan Ghosh, Priyanka Swami +2
When applying LLM-based code generation to software development projects that follow a feature-driven or rapid application development approach, it becomes necessary to estimate th…
Unified Graph Networks (UGN): A Deep Neural Framework for Solving Graph Problems
Rudrajit Dawn, Madhusudan Ghosh, Partha Basuchowdhuri +1
Deep neural networks have enabled researchers to create powerful generalized frameworks, such as transformers, that can be used to solve well-studied problems in various applicatio…