uema2lab_segment — Retrieval Only Task

Is this a manual (human intervention) or automatic run?: automatic
Does this run leverage neural networks?: yes
Does this run leverage proprietary models in any step of the retrieval pipeline?: yes
Does this run leverage open-weight LLMs (> 5B parameters) in any step of the retrieval pipeline?: no
Does this run leverage smaller open-weight language models in any step of the retrieval pipeline?: no
Was this run padded with results from a baseline run?: N/A
What would you categorize this run as?: Ensemble/Fusion of First Stages
Please provide a short description of this run: In this system, each narrative query was first decomposed into multiple sub-queries. For each sub-query, both sparse (BM25) and dense (vector-based) retrieval were performed on a document-level index. The retrieval results were then fused using Reciprocal Rank Fusion (RRF) to produce a ranked list of 100 documents per sub-query. These sub-query-level ranked lists were further merged using RRF at the narrative-query level to produce a final ranked list of documents. Next, each document in the ranked list was segmented into smaller textual segments. For each segment, we computed the embedding-based similarity to the original narrative query, and selected the most relevant segment per document. The final ranked list of segments was submitted as the run.
Please give this run a priority for inclusion in manual assessments.: 1 (top)