9 papers
Benchmarking Hybrid Deep Research Across Database Querying and Web Search
Ruofan Wu, Peiran Xu, Xiaolong Li +9
While autonomous agents have made significant strides in "deep research" by iteratively navigating the open web to synthesize information, real-world problem-solving is rarely conf…
ArcticSwarm: Deferring Early Consensus in Long-Horizon Multi-Agent Research
Soyoung Yoon, Boyi Liu, Yite Wang +6
Multi-agent systems have shown strong performance in domains with reliable verifiers such as coding, where multi-parallel candidate generation selected by a verifier is effective.…
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…
SAFE: An LLM-as-Verifier Framework for Evidence-Grounded Multi-Hop Reasoning
Daeyong Kwon, Soyoung Yoon, Seung-won Hwang
Multi-hop QA benchmarks often reward Large Language Models (LLMs) for spurious correctness, where models reach correct answers through invalid intermediate reasoning. We propose SA…
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…
ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval
Soyoung Yoon, Eunbi Choi, Jiyeon Kim +3
We propose ListT5, a novel reranking approach based on Fusion-in-Decoder (FiD) that handles multiple candidate passages at both train and inference time. We also introduce an effic…