Improving Semantic Proximity in Information Retrieval through Cross-Lingual Alignment
Seongtae Hong · Youngjoon Jang · Jungseob Lee · Hyeonseok Moon · Heuiseok Lim
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Seongtae Hong · Youngjoon Jang · Jungseob Lee · Hyeonseok Moon · Heuiseok Lim
Jeongho Yoon · Chanhee Park · Yongchan Chun · Hyeonseok Moon · Heuiseok Lim
Current LLM-based services typically require users to submit raw text regardless of its sensitivity. While intuitive, such practice introduces substantial privacy risks, as unauthorized access may expose personal, medical, or legal information. Although prior defenses strived to mitigate these ri...
Hyeonseok Moon · Heuiseok Lim
Recent reports suggest that LLMs can handle increasingly long contexts. However, many existing benchmarks for context understanding embed substantial query-irrelevant content, which shifts evaluation toward retrieving relevant snippets rather than fully integrating all provided information. Under...
Youngjoon Jang · Seongtae Hong · Hyeonseok Moon · Heuiseok Lim
Transferring knowledge from a cross-encoder teacher via Knowledge Distillation (KD) has become a standard paradigm for training retrieval models. While existing studies have largely focused on mining hard negatives to improve discrimination, the systematic composition of training data and the res...