[2406.11939] From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline
![[2406.11939] From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline](https://i0.wp.com/arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png?w=1200&resize=1200,700&ssl=1 "[2406.11939] From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline"){kind=logo}
[Submitted on 17 Jun 2024 (v1), last revised 14 Oct 2024 (this version, v2)] View a PDF of the paper ...
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Reducing Labeling Costs in Sentiment Analysis via Semi-Supervised Learning
arXiv:2410.11355v1 Announce Kind: cross Summary: Labeling datasets is a noteworthy problem in machine studying, each by way of price and ...
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[2406.11109] Investigating Annotator Bias in Large Language Models for Hate Speech Detection
[Submitted on 17 Jun 2024 (v1), last revised 12 Oct 2024 (this version, v3)] View a PDF of the paper ...
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A Benchmark for Grounded Spatial Reasoning Evaluation via Multimodal LLMs
[Submitted on 19 Jun 2024 (v1), last revised 10 Oct 2024 (this version, v2)] View a PDF of the paper ...
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LLMs Robustness with Incorrect Multiple-Choice Options
[Submitted on 27 Aug 2024 (v1), last revised 10 Oct 2024 (this version, v2)] View a PDF of the paper ...
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[2402.15048] Unlocking the Power of Large Language Models for Entity Alignment
[Submitted on 23 Feb 2024 (v1), last revised 9 Oct 2024 (this version, v2)] View a PDF of the paper ...
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[2405.18348] Can Automatic Metrics Assess High-Quality Translations?
[Submitted on 28 May 2024 (v1), last revised 10 Oct 2024 (this version, v2)] View a PDF of the paper ...
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Query-centric Data Synthesis Approach for Long-context Scaling of Large Language Model
[Submitted on 30 May 2024 (v1), last revised 9 Oct 2024 (this version, v5)] View a PDF of the paper ...
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[2401.15884] Corrective Retrieval Augmented Generation
[Submitted on 29 Jan 2024 (v1), last revised 7 Oct 2024 (this version, v3)] View a PDF of the paper ...
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PrefixQuant: Static Quantization Beats Dynamic through Prefixed Outliers in LLMs
arXiv:2410.05265v1 Announce Kind: cross Summary: Quantization is important for deploying Massive Language Fashions (LLMs) by enhancing reminiscence effectivity and inference ...
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