6 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…
Replacing Training with Memory: Listwise Selection for Text-to-SQL
Yeonseok Jeong, Soyoung Yoon, Seongjun Lee +1
Modern Text-to-SQL systems often follow generate-execute-select pipelines, generating multiple candidate queries then selecting the best one. Listwise selection, by jointly compari…
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…
UnIte: Uncertainty-based Iterative Document Sampling for Domain Adaptation in Information Retrieval
Jongyoon Kim, Minseong Hwang, Seung-won Hwang
Unsupervised domain adaptation generalizes neural retrievers to an unseen domain by generating pseudo queries on target domain documents. The quality and efficiency of this adaptat…
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…
Adaptive Retrieval for Reasoning-Intensive Retrieval
Jongho Kim, Jaeyoung Kim, Seung-won Hwang +3
We study leveraging adaptive retrieval to ensure sufficient "bridge" documents are retrieved for reasoning-intensive retrieval. Bridge documents are those that contribute to the re…