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Did you mean okapi bm25 site terms com pp site github com site was github if site lemmy world site:arxiv.org?
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https://arxiv.org/html/2608.00785v1
com
Abstract
Content discovery systems depend on relevance judgment for search quality evaluation, but human annotation faces inter-annotator disagreement and scaling costs
https://arxiv.org/html/2407.02518v2
com
Abstract
Large language models (LLMs) for code are typically trained to align with natural language instructions to closely follow their intentions and requirements
https://arxiv.org/html/2508.16279v1
Such advancements have laid a robust foundation for developing powerful LLM-based agent applications that can effectively interface with the world through a variety of tools, to perform diverse and complex tasks with increased autonomy and precision (Qin et al
https://arxiv.org/html/2407.04974v1
This attribute enhances its convenience and practicality for real-world applications
https://arxiv.org/html/2210.00131v4
Code and open-source demos available at https://github
https://arxiv.org/html/2402.03300v3
com/deepseek-ai/DeepSeek-Math
Abstract
Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature
https://arxiv.org/list/cs.SE/new
Through an analysis of 1,966 open source projects, 1,355 Github issues, 444 CVE reports, and 361 StackOverflow posts, we systematically map the feature support and real-world usage of globs across six common ecosystems that utilize globs
https://arxiv.org/pdf/2006.02318
If defined, must be one of book, catalog, feed, journal, magazine, manual, newsletter, pamphlet
https://arxiv.org/html/2407.04503v4
Tools to Evaluate LLMs in Multi-turn Settings
1 Introduction
2 Related work
Biases in LLMs outputs
Transmission chains featuring artificial agents
Iterated Learning
Evaluation of LLMs in social contexts
3 Methods
3.1 Experimental
https://arxiv.org/html/2607.28294v1
This work was presented at Aarhus University (2025), James MacKinnon 75th birthday conference; we thank participants for their feedback
https://arxiv.org/html/2608.03504v1
com/wp-content/uploads/2025/04/IAB_PwC-Internet-Ad-Revenue-Report-Full-Year-2024
https://arxiv.org/html/2403.16413v3
For testing a simple null hypothesis against a simple alternative one, Neyman-Pearson’s fundamental lemma yields an optimal power property of the likelihood ratio test even for the case of parameter-dependent support
https://arxiv.org/html/2608.02942v1
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https://arxiv.org/html/2607.29442v2
1footnoteFootnotefootnotesFootnotes1footnote 1We are grateful to Carsten Chong for fruitful discussions on rough noise. We benefitted from comments by and discussions with Kim Christensen, Mikko Pakkanen, Bent Jesper Christensen, and Mikkel
https://arxiv.org/html/2607.24970v2
Although IA is Pareto-efficient and DA is not, conditional on truth-telling, we do not know how much better IA’s placement is in quantitative terms
https://arxiv.org/html/2508.08980v3
A relation admits such a representation if and only if it is strongly acyclic and its transitive closure is separable, where separability means embeddability in a preorder possessing a countable separating stratification—a countable family of pairwise disjoint subsets, none containing a strictly ranked pair, that
https://arxiv.org/html/2608.02851v1
The design relies on two affine Volterra transformations involving both Fredholm- and Volterra-type integral terms, which results in a four-PIDE kernel equations
https://arxiv.org/html/2607.26405v1
After World War II, the stable US economy with its dollar attached to the gold standard made it an attractive peg codified through the 1944 Bretton Woods Agreement
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/html/2608.02800v1
com in China, Walmart Marketplace and Target Plus in the United States, Otto in Germany, Flipkart in India, and Rakuten in Japan
https://arxiv.org/html/2508.20108v2
Extensive experiments on real-world datasets demonstrate that ReVol enhances the performance of the state-of-the-art backbone models in most cases, achieving an average improvement of more than 0
https://arxiv.org/html/2608.03573v1
Multi-Task Learning Paradigms for LLMs
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https://arxiv.org/abs/2104.08663
Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs
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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https://arxiv.org/html/2608.02943v1
After projection through the recursion, the quantile terms admit a residual-rank-and-spacing representation, yielding feasible inference without innovation-density estimation or quantile smoothing
https://arxiv.org/list/cs.CG/new
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The Johnson--Lindenstrauss lemma asserts that every set of $n$ points in $d$-dimensional Euclidean space embeds into $O(\varepsilon^{-2}\log n)$-dimensional Euclidean space with distortion at most $1+\varepsilon$
https://arxiv.org/html/2508.20101v1
The model combines classical GARCH(pp, qq) dynamics with spatially correlated innovations and spatially varying parameters, estimated using local likelihood methods
https://arxiv.org/list/cs.IR/new
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https://arxiv.org/list/q-fin.MF/2021-04
A lacuna in the proof of the old Lemma 3