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13 result(s) for “okapi bm25 site hotchips pp site does apache org site have apache org site does ball site:arxiv.org” · 756 ms
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The query asks the model to recall the topic of an earlier conversation about Tesla; the gold answer is anchored in one specific session about Pooja’s dig site
from the top result below
https://arxiv.org/html/2608.02613v1
6 Ingest Overhead: When Does Structured Memory Pay Off
https://arxiv.org/html/2608.03048v1
Once all chunks have been processed, the model generates the final answer conditioned on the resulting memory (Yu et al
https://arxiv.org/list/cs.CL/new
LG)
Half a million cuneiform clay tablets survive in museums worldwide, yet modern users can neither read nor write in the world's oldest writing system, leaving a 4,000-year cultural barrier that existing NLP tools have only partially addressed
https://arxiv.org/list/cs.AI/new
Research artifact available under the Apache-2
https://arxiv.org/list/math/new
We prove that there exists a universal constant $c>0$ such that for all $0<\varepsilon<1$, whenever $n$ is large enough, we have: \[ \big|\{(i,j):\|x_i-x_j\|\le \varepsilon\}\big| \geq c\cdot \varepsilon^{1/2}\cdot \big|\{(i,j):\|x_i-x_j\|\geq 1-\varepsilon\}\big
https://arxiv.org/html/2501.13956v1
Current approaches using RAG have focused on broad domain knowledge and largely static corpora—that is, document contents added to a corpus seldom change
https://arxiv.org/list/cs.IR/new
IR)
Large language models (LLMs) have emerged as promising listwise rerankers for recommender systems, but their reliability under equivalent candidate permutations remains unclear
https://arxiv.org/abs/2608.03883
Our systems combine sparse BM25 retrieval, evidence-aware rescoring for self-referential symptom reports, embedding-based reranking, query-prototype expansion, and LLM-based reranking
https://arxiv.org/abs/2104.08663
Benchmark for Zero-shot Evaluation of Information Retrieval Models, by Nandan Thakur and 4 other authors
View PDF HTML (experimental)
Abstract:Existing neural information retrieval (IR) models have often been studied in homogeneous and narrow settings, which has considerably limited insights into their out-of
https://arxiv.org/abs/2608.03860
The pipeline combines sentence-window chunking, BM25, BGE-M3 dense retrieval, reciprocal rank fusion, optional cross-encoder reranking, and grounded answer generation
https://arxiv.org/list/cs.IR/recent
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Information Retrieval
Authors and titles for recent submissions
Wed, 5 Aug 2026
https://arxiv.org/html/2407.16741v3
edu
Abstract
Software is one of the most powerful tools that we humans have at our disposal; it allows a skilled programmer to interact with the world in complex and profound ways
https://arxiv.org/html/2511.06451v1
4 Stopping rule and noise ball
A Reproducibility, Artifacts, and Ethics
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