5 papers
Inference-Free Multimodal Learned Sparse Retrieval for Production-Scale Visual Document Search
Gyu-Hwung Cho, Youngjune Lee, Kiyoon Jeong +5
As large-scale visual-document corpora such as arXiv papers and enterprise PDFs continue to grow, visual-document retrieval has gained increasing attention; yet it still lacks a de…
On Listwise Reranking for Corpus Feedback
Soyoung Yoon, Jongho Kim, Daeyong Kwon +2
Reranker improves retrieval performance by capturing document interactions. At one extreme, graph-aware adaptive retrieval (GAR) represents an information-rich regime, requiring a…
AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Reranking
Soyoung Yoon, Gyuwan Kim, Gyu-Hwung Cho +1
Listwise reranking with large language models (LLMs) enhances top-ranked results in retrieval-based applications. Due to the limit in context size and high inference cost of long c…
RoToR: Towards More Reliable Responses for Order-Invariant Inputs
Soyoung Yoon, Dongha Ahn, Youngwon Lee +3
Mitigating positional bias of language models (LMs) for listwise inputs is a well-known and important problem (e.g., lost-in-the-middle). While zero-shot order-invariant LMs have b…
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…