9 citations · 9 across the 10 of their papers we have counts for
10 papers
SHADOWBENCH: Toward Reliable Automatic Evaluation of Semantic Alignment in Autoformalization
Hojae Han, Jongyoon Kim, Sanghyeok Park +8
Autoformalization translates informal mathematical theorems into code for proof assistants such as Lean. A central challenge is that current evaluation metrics can accept type-corr…
R-SQL: Ranking Reward and Resampling for Text-to-SQL
Hojae Han, Yeonseok Jeong, Seung-won Hwang +2
Modern Text-to-SQL systems generate multiple candidate SQL queries and rank them to judge a final prediction. However, existing methods face two limitations. First, they often scor…
Benchmarking Testing in Automated Theorem Proving
Jongyoon Kim, Hojae Han, Seung-won Hwang
Recent advances in large language models (LLMs) have shown promise in formal theorem proving, yet evaluating semantic correctness remains challenging. Existing evaluations rely on…
DuET: Dual Execution for Test Output Prediction with Generated Code and Pseudocode
Hojae Han, Jaejin Kim, Seung-won Hwang +2
This work addresses test output prediction, a key challenge in test case generation. To improve the reliability of predicted outputs by LLMs, prior approaches generate code first t…
Can David Beat Goliath? On Multi-Hop Reasoning with Resource-Constrained Agents
Hojae Han, Heeyun Jung, Jongyoon Kim +1
Reinforcement learning (RL) trains small language model agents to answer multi-hop questions by retrieving evidence over multiple turns, but reported gains typically rely on thousa…
ConvCodeWorld: Benchmarking Conversational Code Generation in Reproducible Feedback Environments
Hojae Han, Seung-won Hwang, Rajhans Samdani +1
Large language models (LLMs) have proven invaluable for code generation, particularly in interactive settings. However, existing code generation benchmarks fail to capture the dive…
PERC: Plan-As-Query Example Retrieval for Underrepresented Code Generation
Jaeseok Yoo, Hojae Han, Youngwon Lee +2
Code generation with large language models has shown significant promise, especially when employing retrieval-augmented generation (RAG) with few-shot examples. However, selecting…
Arctic-SnowCoder: Demystifying High-Quality Data in Code Pretraining
Yuxiang Wei, Hojae Han, Rajhans Samdani
Recent studies have been increasingly demonstrating that high-quality data is crucial for effective pretraining of language models. However, the precise definition of "high-quality…
ArchCode: Incorporating Software Requirements in Code Generation with Large Language Models
Hojae Han, Jaejin Kim, Jaeseok Yoo +2
This paper aims to extend the code generation capability of large language models (LLMs) to automatically manage comprehensive software requirements from given textual descriptions…
ReACC: A Retrieval-Augmented Code Completion Framework
Shuai Lu, Nan Duan, Hojae Han +3
Code completion, which aims to predict the following code token(s) according to the code context, can improve the productivity of software development. Recent work has proved that…