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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/2506.01844v1
VLA models remain in an early stage of development and are not yet as mature or widely adopted as LLMs and VLMs
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/abs/2608.03883
Because no annotated training data were released for this first edition of the task, we relied on zero-shot experimentation, manual validation, and unsupervised or weakly guided retrieval pipelines
https://arxiv.org/list/cs.IR/new
KGD improves over strong pretrain-transfer baselines by 4-12% on eight public benchmarks and sustains its advantage over a 90-day production stream where baselines show no gains
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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Information Retrieval
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Wed, 5 Aug 2026