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…
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…
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…
Analyzing the Effectiveness of Listwise Reranking with Positional Invariance on Temporal Generalizability
Soyoung Yoon, Jongyoon Kim, Seung-won Hwang
This working note outlines our participation in the retrieval task at CLEF 2024. We highlight the considerable gap between studying retrieval performance on static knowledge docume…
Split-and-Bridge: Adaptable Class Incremental Learning within a Single Neural Network
Jong-Yeong Kim, Dong-Wan Choi
Continual learning has been a major problem in the deep learning community, where the main challenge is how to effectively learn a series of newly arriving tasks without forgetting…