7 citations · 17 across the 11 of their papers we have counts for
12 papers
ROSUM-MCTS: Monte Carlo Tree Search-Inspired HDL Code Summarization with Structural Rewards
Prashanth Vijayaraghavan, Charles Mackin, Luyao Shi +6
Large language models (LLMs) have shown promise in code summarization, yet their effectiveness for Hardware Description Languages (HDLs) like VHDL and Verilog remains underexplored…
StepPRM-RTL: Stepwise Process-Reward Guided LLM Fine-Tuning for Enhanced RTL Synthesis
Prashanth Vijayaraghavan, Apoorva Nitsure, Luyao Shi +2
Automatic generation of RTL code for digital hardware designs remains challenging due to long-horizon reasoning, multi-step dependencies, and strict correctness constraints in Veri…
SYMDIREC: A Neuro-Symbolic Divide-Retrieve-Conquer Framework for Enhanced RTL Synthesis and Summarization
Prashanth Vijayaraghavan, Apoorva Nitsure, Luyao Shi +5
Register-Transfer Level (RTL) synthesis and summarization are central to hardware design automation but remain challenging for Large Language Models (LLMs) due to rigid HDL syntax,…
Chain-of-Descriptions: Improving Code LLMs for VHDL Code Generation and Summarization
Prashanth Vijayaraghavan, Apoorva Nitsure, Charles Mackin +9
Large Language Models (LLMs) have become widely used across diverse NLP tasks and domains, demonstrating their adaptability and effectiveness. In the realm of Electronic Design Aut…
Revisiting Group Relative Policy Optimization: Insights into On-Policy and Off-Policy Training
Youssef Mroueh, Nicolas Dupuis, Brian Belgodere +6
We revisit Group Relative Policy Optimization (GRPO) in both on-policy and off-policy optimization regimes. Our motivation comes from recent work on off-policy Proximal Policy Opti…
Multivariate Stochastic Dominance via Optimal Transport and Applications to Models Benchmarking
Gabriel Rioux, Apoorva Nitsure, Mattia Rigotti +2
Stochastic dominance is an important concept in probability theory, econometrics and social choice theory for robustly modeling agents' preferences between random outcomes. While m…
Distributional Preference Alignment of LLMs via Optimal Transport
Igor Melnyk, Youssef Mroueh, Brian Belgodere +6
Current LLM alignment techniques use pairwise human preferences at a sample level, and as such, they do not imply an alignment on the distributional level. We propose in this paper…
VHDL-Eval: A Framework for Evaluating Large Language Models in VHDL Code Generation
Prashanth Vijayaraghavan, Luyao Shi, Stefano Ambrogio +4
With the unprecedented advancements in Large Language Models (LLMs), their application domains have expanded to include code generation tasks across various programming languages.…
Risk Aware Benchmarking of Large Language Models
Apoorva Nitsure, Youssef Mroueh, Mattia Rigotti +6
We propose a distributional framework for benchmarking socio-technical risks of foundation models with quantified statistical significance. Our approach hinges on a new statistical…
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs
Brian Belgodere, Pierre Dognin, Adam Ivankay +11
Real-world data often exhibits bias, imbalance, and privacy risks. Synthetic datasets have emerged to address these issues. This paradigm relies on generative AI models to generate…
A Scalable Space-efficient In-database Interpretability Framework for Embedding-based Semantic SQL Queries
Prabhakar Kudva, Rajesh Bordawekar, Apoorva Nitsure
AI-Powered database (AI-DB) is a novel relational database system that uses a self-supervised neural network, database embedding, to enable semantic SQL queries on relational table…
Unlocking New York City Crime Insights using Relational Database Embeddings
Apoorva Nitsure, Rajesh Bordawekar, Jose Neves
This version withdrawn by arXiv administrators because the author did not have the right to agree to our license at the time of submission.