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license: mit
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---
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license: mit
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---
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## ICLR 2024 International Conference on Learning Representations 2024 Accepted Paper Meta Info Dataset
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This dataset is collect from the ICLR 2024 OpenReview website (https://openreview.net/group?id=ICLR.cc/2024/Conference#tab-accept-oral) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/iclr2024). For researchers who are interested in doing analysis of ICLR 2024 accepted papers and potential trends, you can use the already cleaned up json files.
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Each row contains the meta information of a paper in the ICLR 2024 conference. To explore more AI & Robotic papers (NIPS/ICML/ICLR/IROS/ICRA/etc) and AI equations, feel free to navigate the Equation Search Engine (http://www.deepnlp.org/search/equation) as well as the AI Agent Search Engine to find the deployed AI Apps and Agents (http://www.deepnlp.org/search/agent) in your domain.
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### Meta Information of Json File
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```
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{
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"title": "Proving Test Set Contamination in Black-Box Language Models",
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"url": "https://openreview.net/forum?id=KS8mIvetg2",
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"detail_url": "https://openreview.net/forum?id=KS8mIvetg2",
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"authors": "Yonatan Oren,Nicole Meister,Niladri S. Chatterji,Faisal Ladhak,Tatsunori Hashimoto",
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"tags": "ICLR 2024,Oral",
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"abstract": "Large language models are trained on vast amounts of internet data, prompting concerns that they have memorized public benchmarks. Detecting this type of contamination is challenging because the pretraining data used by proprietary models are often not publicly accessible.\n\nWe propose a procedure for detecting test set contamination of language models with exact false positive guarantees and without access to pretraining data or model weights. Our approach leverages the fact that when there is no data contamination, all orderings of an exchangeable benchmark should be equally likely. In contrast, the tendency for language models to memorize example order means that a contaminated language model will find certain canonical orderings to be much more likely than others. Our test flags potential contamination whenever the likelihood of a canonically ordered benchmark dataset is significantly higher than the likelihood after shuffling the examples.\n\nWe demonstrate that our procedure is sensitive enough to reliably detect contamination in challenging situations, including models as small as 1.4 billion parameters, on small test sets only 1000 examples, and datasets that appear only a few times in the pretraining corpus. Finally, we evaluate LLaMA-2 to apply our test in a realistic setting and find our results to be consistent with existing contamination evaluations.",
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"pdf": "https://openreview.net/pdf/cfd79aaab7bdcd4f7c032c57fe7e607058042c80.pdf"
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}
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```
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## Related
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## AI Equation
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[List of AI Equations and Latex](http://www.deepnlp.org/equation/category/ai) <br>
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[List of Math Equations and Latex](http://www.deepnlp.org/equation/category/math) <br>
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[List of Physics Equations and Latex](http://www.deepnlp.org/equation/category/physics) <br>
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[List of Statistics Equations and Latex](http://www.deepnlp.org/equation/category/statistics) <br>
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[List of Machine Learning Equations and Latex](http://www.deepnlp.org/equation/category/machine%20learning) <br>
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## AI Agent Marketplace and Search
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[AI Agent Marketplace and Search](http://www.deepnlp.org/search/agent) <br>
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[Robot Search](http://www.deepnlp.org/search/robot) <br>
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[Equation and Academic search](http://www.deepnlp.org/search/equation) <br>
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[AI & Robot Comprehensive Search](http://www.deepnlp.org/search) <br>
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[AI & Robot Question](http://www.deepnlp.org/question) <br>
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[AI & Robot Community](http://www.deepnlp.org/community) <br>
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[AI Agent Marketplace Blog](http://www.deepnlp.org/blog/ai-agent-marketplace-and-search-portal-reviews-2025) <br>
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## AI Agent Reviews
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[AI Agent Marketplace Directory](http://www.deepnlp.org/store/ai-agent) <br>
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[Microsoft AI Agents Reviews](http://www.deepnlp.org/store/pub/pub-microsoft-ai-agent) <br>
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[Claude AI Agents Reviews](http://www.deepnlp.org/store/pub/pub-claude-ai-agent) <br>
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[OpenAI AI Agents Reviews](http://www.deepnlp.org/store/pub/pub-openai-ai-agent) <br>
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[Saleforce AI Agents Reviews](http://www.deepnlp.org/store/pub/pub-salesforce-ai-agent) <br>
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[AI Agent Builder Reviews](http://www.deepnlp.org/store/ai-agent/ai-agent-builder) <br>
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