79 citations · 128 across the 7 of their papers we have counts for
7 papers
Retriv at BLP-2025 Task 2: Test-Driven Feedback-Guided Framework for Bangla-to-Python Code Generation
K M Nafi Asib, Sourav Saha, Mohammed Moshiul Hoque
Large Language Models (LLMs) have advanced the automated generation of code from natural language prompts. However, low-resource languages (LRLs) like Bangla remain underrepresente…
RTTC: Reward-Guided Collaborative Test-Time Compute
J. Pablo Muñoz, Jinjie Yuan
Test-Time Compute (TTC) has emerged as a powerful paradigm for enhancing the performance of Large Language Models (LLMs) at inference, leveraging strategies such as Test-Time Train…
CodeInsight: A Curated Dataset of Practical Coding Solutions from Stack Overflow
Nathanaël Beau, Benoît Crabbé
We introduce a novel dataset tailored for code generation, aimed at aiding developers in common tasks. Our dataset provides examples that include a clarified intent, code snippets…
PromptChainer: Chaining Large Language Model Prompts through Visual Programming
Tongshuang Wu, Ellen Jiang, Aaron Donsbach +4
While LLMs can effectively help prototype single ML functionalities, many real-world applications involve complex tasks that cannot be easily handled via a single run of an LLM. Re…
Program Synthesis with Large Language Models
Jacob Austin, Augustus Odena, Maxwell Nye +8
This paper explores the limits of the current generation of large language models for program synthesis in general purpose programming languages. We evaluate a collection of such m…
ShapeAssembly: Learning to Generate Programs for 3D Shape Structure Synthesis
R. Kenny Jones, Theresa Barton, Xianghao Xu +5
Manually authoring 3D shapes is difficult and time consuming; generative models of 3D shapes offer compelling alternatives. Procedural representations are one such possibility: the…