provider stringclasses 54 values | name stringclasses 186 values | size stringclasses 120 values | variant stringclasses 110 values | version stringclasses 110 values | sector stringclasses 4 values | openness stringclasses 2 values | region stringclasses 5 values | country stringclasses 13 values | source_id stringclasses 434 values | is_first_party bool 2 classes | category int64 1 7 | year int64 2.02k 2.03k | metadata stringclasses 433 values | score float64 0 3 | is_model_release bool 2 classes |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Cohere | command-r | null | plus | null | Industry | open | North America | Canada | third-party-papers_62 | true | 2 | 2,025 | {'title': 'Global MMLU : Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation', 'url': 'https://arxiv.org/pdf/2412.03304', 'release_date': '2025-02-19'} | 3 | false |
Anthropic | claude-4 | null | sonnet | null | Industry | closed | North America | United States | third-party-papers_62 | false | 2 | 2,025 | {'title': 'Global MMLU : Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation', 'url': 'https://arxiv.org/pdf/2412.03304', 'release_date': '2025-02-19'} | 3 | false |
Cohere | aya-expanse | 32B | null | null | Industry | open | North America | Canada | third-party-papers_62 | true | 2 | 2,025 | {'title': 'Global MMLU : Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation', 'url': 'https://arxiv.org/pdf/2412.03304', 'release_date': '2025-02-19'} | 3 | false |
Meta | llama-3.1 | 70B | null | null | Industry | open | North America | United States | third-party-papers_62 | true | 2 | 2,025 | {'title': 'Global MMLU : Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation', 'url': 'https://arxiv.org/pdf/2412.03304', 'release_date': '2025-02-19'} | 3 | false |
Alibaba | qwen-2.5 | 32B | null | null | Industry | open | East Asia | China | third-party-papers_62 | false | 2 | 2,025 | {'title': 'Global MMLU : Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation', 'url': 'https://arxiv.org/pdf/2412.03304', 'release_date': '2025-02-19'} | 3 | false |
Anthropic | claude-3.5 | null | Sonnet | 20241022 | Industry | closed | North America | United States | third-party-papers_63 | false | 2 | 2,025 | {'title': 'Randomness, Not Representation: The Unreliability of Evaluating Cultural Alignment in LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3715275.3732147'} | 3 | false |
OpenAI | gpt-4o | null | null | null | Industry | closed | North America | United States | third-party-papers_63 | false | 2 | 2,025 | {'title': 'Randomness, Not Representation: The Unreliability of Evaluating Cultural Alignment in LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3715275.3732147'} | 3 | false |
Meta | llama-3.1 | 405B | null | null | Industry | open | North America | United States | third-party-papers_63 | false | 2 | 2,025 | {'title': 'Randomness, Not Representation: The Unreliability of Evaluating Cultural Alignment in LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3715275.3732147'} | 3 | false |
Google | gemini-2.0 | null | Flash | null | Industry | closed | North America | United States | third-party-papers_63 | false | 2 | 2,025 | {'title': 'Randomness, Not Representation: The Unreliability of Evaluating Cultural Alignment in LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3715275.3732147'} | 3 | false |
Mistral | mistral-large | null | null | 2411 | Industry | open | Europe | France | third-party-papers_63 | false | 2 | 2,025 | {'title': 'Randomness, Not Representation: The Unreliability of Evaluating Cultural Alignment in LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3715275.3732147'} | 3 | false |
Meta | llama-3.1 | 405B | null | null | Industry | open | North America | United States | third-party-papers_63 | false | 1 | 2,025 | {'title': 'Randomness, Not Representation: The Unreliability of Evaluating Cultural Alignment in LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3715275.3732147'} | 3 | false |
Mistral | mistral-large | null | null | 2411 | Industry | open | Europe | France | third-party-papers_63 | false | 1 | 2,025 | {'title': 'Randomness, Not Representation: The Unreliability of Evaluating Cultural Alignment in LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3715275.3732147'} | 3 | false |
Google | gemini-2.0 | null | Flash | null | Industry | closed | North America | United States | third-party-papers_63 | false | 1 | 2,025 | {'title': 'Randomness, Not Representation: The Unreliability of Evaluating Cultural Alignment in LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3715275.3732147'} | 3 | false |
OpenAI | gpt-4o | null | null | null | Industry | closed | North America | United States | third-party-papers_63 | false | 1 | 2,025 | {'title': 'Randomness, Not Representation: The Unreliability of Evaluating Cultural Alignment in LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3715275.3732147'} | 3 | false |
Anthropic | claude-3.5 | null | Sonnet | 20241022 | Industry | closed | North America | United States | third-party-papers_63 | false | 1 | 2,025 | {'title': 'Randomness, Not Representation: The Unreliability of Evaluating Cultural Alignment in LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3715275.3732147'} | 3 | false |
Meta | llama-3.3 | 70B | null | null | Industry | open | North America | United States | third-party-papers_64 | false | 1 | 2,025 | {'title': 'Social Bias Benchmark for Generation: A Comparison of Generation and QA-Based Evaluations', 'url': 'https://aclanthology.org/2025.findings-acl.585.pdf'} | 3 | false |
NAVER | hyperclova-x | null | dash | 1 | Industry | open | East Asia | South Korea | third-party-papers_64 | false | 1 | 2,025 | {'title': 'Social Bias Benchmark for Generation: A Comparison of Generation and QA-Based Evaluations', 'url': 'https://aclanthology.org/2025.findings-acl.585.pdf'} | 3 | false |
NAVER | hyperclova-x | null | null | 3 | Industry | open | East Asia | South Korea | third-party-papers_64 | false | 1 | 2,025 | {'title': 'Social Bias Benchmark for Generation: A Comparison of Generation and QA-Based Evaluations', 'url': 'https://aclanthology.org/2025.findings-acl.585.pdf'} | 3 | false |
OpenAI | gpt-3.5 | null | Turbo | 125 | Industry | closed | North America | United States | third-party-papers_64 | false | 1 | 2,025 | {'title': 'Social Bias Benchmark for Generation: A Comparison of Generation and QA-Based Evaluations', 'url': 'https://aclanthology.org/2025.findings-acl.585.pdf'} | 3 | false |
Alibaba | qwen-2.5 | 72B | Instruct, Turbo | null | Industry | open | East Asia | China | third-party-papers_64 | false | 1 | 2,025 | {'title': 'Social Bias Benchmark for Generation: A Comparison of Generation and QA-Based Evaluations', 'url': 'https://aclanthology.org/2025.findings-acl.585.pdf'} | 3 | false |
Google | gemini-2.0 | null | Flash | 1 | Industry | closed | North America | United States | third-party-papers_64 | false | 1 | 2,025 | {'title': 'Social Bias Benchmark for Generation: A Comparison of Generation and QA-Based Evaluations', 'url': 'https://aclanthology.org/2025.findings-acl.585.pdf'} | 3 | false |
OpenAI | gpt-4 | null | Turbo | 2024-04-09 | Industry | closed | North America | United States | third-party-papers_64 | false | 1 | 2,025 | {'title': 'Social Bias Benchmark for Generation: A Comparison of Generation and QA-Based Evaluations', 'url': 'https://aclanthology.org/2025.findings-acl.585.pdf'} | 3 | false |
Anthropic | claude-3 | null | Haiku | 20240307 | Industry | closed | North America | United States | third-party-papers_64 | false | 1 | 2,025 | {'title': 'Social Bias Benchmark for Generation: A Comparison of Generation and QA-Based Evaluations', 'url': 'https://aclanthology.org/2025.findings-acl.585.pdf'} | 3 | false |
Anthropic | claude-3.5 | null | Sonnet | 20241022 | Industry | closed | North America | United States | third-party-papers_64 | false | 1 | 2,025 | {'title': 'Social Bias Benchmark for Generation: A Comparison of Generation and QA-Based Evaluations', 'url': 'https://aclanthology.org/2025.findings-acl.585.pdf'} | 3 | false |
OpenAI | gpt-4o | null | Turbo | 2024-11-20 | Industry | closed | North America | United States | third-party-papers_64 | false | 1 | 2,025 | {'title': 'Social Bias Benchmark for Generation: A Comparison of Generation and QA-Based Evaluations', 'url': 'https://aclanthology.org/2025.findings-acl.585.pdf'} | 3 | false |
OpenAI | gpt-4o | null | Turbo | 2024-05-13 | Industry | closed | North America | United States | third-party-papers_64 | false | 1 | 2,025 | {'title': 'Social Bias Benchmark for Generation: A Comparison of Generation and QA-Based Evaluations', 'url': 'https://aclanthology.org/2025.findings-acl.585.pdf'} | 3 | false |
OpenAI | gpt-3.5 | null | Turbo | null | Industry | closed | North America | United States | third-party-papers_65 | false | 5 | 2,024 | {'title': 'CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model Generation', 'url': 'https://aclanthology.org/2024.emnlp-main.844/'} | 3 | false |
OpenAI | gpt-4 | null | Turbo | null | Industry | closed | North America | United States | third-party-papers_65 | false | 5 | 2,024 | {'title': 'CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model Generation', 'url': 'https://aclanthology.org/2024.emnlp-main.844/'} | 3 | false |
Meta | llama-3 | 8B | null | null | Industry | open | North America | United States | third-party-papers_65 | false | 5 | 2,024 | {'title': 'CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model Generation', 'url': 'https://aclanthology.org/2024.emnlp-main.844/'} | 3 | false |
Meta | llama-2 | 13B | null | null | Industry | open | North America | United States | third-party-papers_65 | false | 5 | 2,024 | {'title': 'CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model Generation', 'url': 'https://aclanthology.org/2024.emnlp-main.844/'} | 3 | false |
Mistral | mixtral | 8x7B | null | null | Industry | open | Europe | France | third-party-papers_65 | false | 5 | 2,024 | {'title': 'CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model Generation', 'url': 'https://aclanthology.org/2024.emnlp-main.844/'} | 3 | false |
Meta | llama-2 | 70B | null | null | Industry | open | North America | United States | third-party-papers_65 | false | 5 | 2,024 | {'title': 'CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model Generation', 'url': 'https://aclanthology.org/2024.emnlp-main.844/'} | 3 | false |
Mistral | mistral | 7B | null | null | Industry | open | Europe | France | third-party-papers_65 | false | 5 | 2,024 | {'title': 'CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model Generation', 'url': 'https://aclanthology.org/2024.emnlp-main.844/'} | 3 | false |
Meta | llama-3 | 70B | null | null | Industry | open | North America | United States | third-party-papers_65 | false | 5 | 2,024 | {'title': 'CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model Generation', 'url': 'https://aclanthology.org/2024.emnlp-main.844/'} | 3 | false |
Meta | llama-2 | 7B | null | null | Industry | open | North America | United States | third-party-papers_65 | false | 5 | 2,024 | {'title': 'CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model Generation', 'url': 'https://aclanthology.org/2024.emnlp-main.844/'} | 3 | false |
Meta | llama-3.1 | 70B | Instruct | null | Industry | open | North America | United States | third-party-papers_66 | false | 3 | 2,025 | {'title': 'Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks', 'url': 'https://aclanthology.org/2025.acl-long.317.pdf', 'release_date': '2025-06-09'} | 3 | false |
Anthropic | claude-3.5 | null | Sonnet | null | Industry | closed | North America | United States | third-party-papers_66 | false | 3 | 2,025 | {'title': 'Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks', 'url': 'https://aclanthology.org/2025.acl-long.317.pdf', 'release_date': '2025-06-09'} | 3 | false |
OpenAI | gpt-4o | null | null | null | Industry | closed | North America | United States | third-party-papers_66 | false | 3 | 2,025 | {'title': 'Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks', 'url': 'https://aclanthology.org/2025.acl-long.317.pdf', 'release_date': '2025-06-09'} | 3 | false |
Mistral | mistral | 7B | Instruct | v0.3 | Industry | open | Europe | France | third-party-papers_66 | false | 3 | 2,025 | {'title': 'Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks', 'url': 'https://aclanthology.org/2025.acl-long.317.pdf', 'release_date': '2025-06-09'} | 3 | false |
Microsoft | phi-3 | null | small, instruct | null | Industry | open | North America | United States | third-party-papers_66 | true | 3 | 2,025 | {'title': 'Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks', 'url': 'https://aclanthology.org/2025.acl-long.317.pdf', 'release_date': '2025-06-09'} | 3 | false |
Meta | llama-3 | 70B | Instruct | null | Industry | open | North America | United States | third-party-papers_66 | false | 3 | 2,025 | {'title': 'Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks', 'url': 'https://aclanthology.org/2025.acl-long.317.pdf', 'release_date': '2025-06-09'} | 3 | false |
Microsoft | phi-3 | null | medium, instruct | null | Industry | open | North America | United States | third-party-papers_66 | true | 3 | 2,025 | {'title': 'Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks', 'url': 'https://aclanthology.org/2025.acl-long.317.pdf', 'release_date': '2025-06-09'} | 3 | false |
Mistral | mixtral | 8x7B | Instruct | v0.1 | Industry | open | Europe | France | third-party-papers_66 | false | 3 | 2,025 | {'title': 'Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks', 'url': 'https://aclanthology.org/2025.acl-long.317.pdf', 'release_date': '2025-06-09'} | 3 | false |
OpenAI | o1 | null | null | null | Industry | closed | North America | United States | third-party-papers_66 | false | 3 | 2,025 | {'title': 'Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks', 'url': 'https://aclanthology.org/2025.acl-long.317.pdf', 'release_date': '2025-06-09'} | 3 | false |
Meta | llama-3 | 8B | Instruct | null | Industry | open | North America | United States | third-party-papers_66 | false | 3 | 2,025 | {'title': 'Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks', 'url': 'https://aclanthology.org/2025.acl-long.317.pdf', 'release_date': '2025-06-09'} | 3 | false |
OpenAI | gpt-3.5 | null | Turbo | null | Industry | closed | North America | United States | third-party-papers_66 | false | 3 | 2,025 | {'title': 'Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks', 'url': 'https://aclanthology.org/2025.acl-long.317.pdf', 'release_date': '2025-06-09'} | 3 | false |
OpenAI | gpt-4 | null | null | null | Industry | closed | North America | United States | third-party-papers_66 | false | 3 | 2,025 | {'title': 'Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks', 'url': 'https://aclanthology.org/2025.acl-long.317.pdf', 'release_date': '2025-06-09'} | 3 | false |
Microsoft | phi-3 | null | mini, instruct | null | Industry | open | North America | United States | third-party-papers_66 | true | 3 | 2,025 | {'title': 'Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks', 'url': 'https://aclanthology.org/2025.acl-long.317.pdf', 'release_date': '2025-06-09'} | 3 | false |
Google | gemini-1.0 | null | pro | null | Industry | closed | North America | United States | third-party-papers_67 | false | 2 | 2,024 | {'title': 'Large language models are geographically biased', 'url': 'https://dl.acm.org/doi/10.5555/3692070.3693479'} | 3 | false |
Meta | llama-2 | 70B | null | null | Industry | open | North America | United States | third-party-papers_67 | false | 2 | 2,024 | {'title': 'Large language models are geographically biased', 'url': 'https://dl.acm.org/doi/10.5555/3692070.3693479'} | 3 | false |
OpenAI | gpt-3.5 | null | turbo | null | Industry | closed | North America | United States | third-party-papers_67 | false | 2 | 2,024 | {'title': 'Large language models are geographically biased', 'url': 'https://dl.acm.org/doi/10.5555/3692070.3693479'} | 3 | false |
Mistral | mixtral | 8x7B | null | null | Industry | open | Europe | France | third-party-papers_67 | false | 2 | 2,024 | {'title': 'Large language models are geographically biased', 'url': 'https://dl.acm.org/doi/10.5555/3692070.3693479'} | 3 | false |
OpenAI | gpt-4 | null | preview | 1106 | Industry | closed | North America | United States | third-party-papers_67 | false | 1 | 2,024 | {'title': 'Large language models are geographically biased', 'url': 'https://dl.acm.org/doi/10.5555/3692070.3693479'} | 3 | false |
Google | gemini-1.0 | null | pro | null | Industry | closed | North America | United States | third-party-papers_67 | false | 1 | 2,024 | {'title': 'Large language models are geographically biased', 'url': 'https://dl.acm.org/doi/10.5555/3692070.3693479'} | 3 | false |
Mistral | mixtral | 8x7B | null | null | Industry | open | Europe | France | third-party-papers_67 | false | 1 | 2,024 | {'title': 'Large language models are geographically biased', 'url': 'https://dl.acm.org/doi/10.5555/3692070.3693479'} | 3 | false |
OpenAI | gpt-3.5 | null | turbo | null | Industry | closed | North America | United States | third-party-papers_67 | false | 1 | 2,024 | {'title': 'Large language models are geographically biased', 'url': 'https://dl.acm.org/doi/10.5555/3692070.3693479'} | 3 | false |
OpenAI | gpt-4 | null | preview | 1106 | Industry | closed | North America | United States | third-party-papers_67 | false | 2 | 2,024 | {'title': 'Large language models are geographically biased', 'url': 'https://dl.acm.org/doi/10.5555/3692070.3693479'} | 3 | false |
Meta | llama-2 | 70B | null | null | Industry | open | North America | United States | third-party-papers_67 | false | 1 | 2,024 | {'title': 'Large language models are geographically biased', 'url': 'https://dl.acm.org/doi/10.5555/3692070.3693479'} | 3 | false |
Meta | llama-2 | 70B | null | null | Industry | open | North America | United States | third-party-papers_68 | false | 5 | 2,025 | {'title': 'Information-Guided Identification of Training Data Imprint in (Proprietary) Large Language Models', 'url': 'https://aclanthology.org/2025.naacl-long.99.pdf'} | 3 | false |
OpenAI | gpt-4 | null | null | null | Industry | closed | North America | United States | third-party-papers_68 | false | 5 | 2,025 | {'title': 'Information-Guided Identification of Training Data Imprint in (Proprietary) Large Language Models', 'url': 'https://aclanthology.org/2025.naacl-long.99.pdf'} | 3 | false |
OpenAI | gpt-3.5 | null | null | null | Industry | closed | North America | United States | third-party-papers_68 | false | 5 | 2,025 | {'title': 'Information-Guided Identification of Training Data Imprint in (Proprietary) Large Language Models', 'url': 'https://aclanthology.org/2025.naacl-long.99.pdf'} | 3 | false |
TII | falcon | 7B | Instruct | null | Government | open | Middle East and North Africa | United Arab Emirates | third-party-papers_69 | false | 5 | 2,025 | {'title': 'ALPACA AGAINST VICUNA: Using LLMs to Uncover Memorization of LLMs', 'url': 'https://aclanthology.org/2025.naacl-long.421.pdf', 'release_date': '2025-02-09'} | 3 | false |
Ai2 | olmo-1 | 7B | Instruct | null | Nonprofit | open | North America | United States | third-party-papers_69 | true | 5 | 2,025 | {'title': 'ALPACA AGAINST VICUNA: Using LLMs to Uncover Memorization of LLMs', 'url': 'https://aclanthology.org/2025.naacl-long.421.pdf', 'release_date': '2025-02-09'} | 3 | false |
Baichuan | baichuan | 13B | chat | null | Industry | open | East Asia | China | third-party-papers_70 | false | 5 | 2,025 | {'title': 'TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3732784'} | 1 | false |
iFLYTEK | spark | null | null | null | Industry | closed | East Asia | China | third-party-papers_70 | false | 5 | 2,025 | {'title': 'TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3732784'} | 1 | false |
Anthropic | claude-2 | null | null | null | Industry | closed | North America | United States | third-party-papers_70 | false | 5 | 2,025 | {'title': 'TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3732784'} | 1 | false |
Baichuan | baichuan-2 | 13B | chat | 8 | Industry | open | East Asia | China | third-party-papers_70 | false | 5 | 2,025 | {'title': 'TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3732784'} | 1 | false |
Baidu | ernie-3.5 | null | Bot | null | Industry | closed | East Asia | China | third-party-papers_70 | false | 5 | 2,025 | {'title': 'TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3732784'} | 1 | false |
Alibaba | qwen-1 | 7B | null | null | Industry | open | East Asia | China | third-party-papers_70 | false | 5 | 2,025 | {'title': 'TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3732784'} | 1 | false |
Z.ai | chatglm | 6B | null | v2 | Industry | open | East Asia | China | third-party-papers_70 | false | 5 | 2,025 | {'title': 'TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3732784'} | 1 | false |
OpenAI | gpt-4 | null | Turbo | null | Industry | closed | North America | United States | third-party-papers_70 | false | 5 | 2,025 | {'title': 'TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3732784'} | 1 | false |
OpenAI | gpt-4 | null | null | null | Industry | closed | North America | United States | third-party-papers_70 | false | 5 | 2,025 | {'title': 'TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3732784'} | 1 | false |
Z.ai | chatglm | 6B | null | v2 | Industry | open | East Asia | China | third-party-papers_70 | false | 2 | 2,025 | {'title': 'TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3732784'} | 3 | false |
OpenAI | gpt-4 | null | null | null | Industry | closed | North America | United States | third-party-papers_70 | false | 2 | 2,025 | {'title': 'TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3732784'} | 3 | false |
Baichuan | baichuan-2 | 13B | chat | 8 | Industry | open | East Asia | China | third-party-papers_70 | false | 2 | 2,025 | {'title': 'TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3732784'} | 3 | false |
Alibaba | qwen-1 | 7B | null | null | Industry | open | East Asia | China | third-party-papers_70 | false | 2 | 2,025 | {'title': 'TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3732784'} | 3 | false |
Baidu | ernie-3.5 | null | Bot | null | Industry | closed | East Asia | China | third-party-papers_70 | false | 2 | 2,025 | {'title': 'TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3732784'} | 3 | false |
OpenAI | gpt-4 | null | Turbo | null | Industry | closed | North America | United States | third-party-papers_70 | false | 2 | 2,025 | {'title': 'TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3732784'} | 3 | false |
Baichuan | baichuan | 13B | chat | null | Industry | open | East Asia | China | third-party-papers_70 | false | 2 | 2,025 | {'title': 'TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3732784'} | 3 | false |
iFLYTEK | spark | null | null | null | Industry | closed | East Asia | China | third-party-papers_70 | false | 2 | 2,025 | {'title': 'TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3732784'} | 3 | false |
Anthropic | claude-2 | null | null | null | Industry | closed | North America | United States | third-party-papers_70 | false | 2 | 2,025 | {'title': 'TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs', 'url': 'https://dl.acm.org/doi/10.1145/3732784'} | 3 | false |
Alibaba | qwen-1 | 7B | null | null | Industry | open | East Asia | China | third-party-papers_71 | false | 2 | 2,024 | {'title': 'SafetyBench: Evaluating the Safety of Large Language Models', 'url': 'https://aclanthology.org/2024.acl-long.830/'} | 3 | false |
Z.ai | chatglm2 | null | lite | null | Industry | open | East Asia | China | third-party-papers_71 | false | 2 | 2,024 | {'title': 'SafetyBench: Evaluating the Safety of Large Language Models', 'url': 'https://aclanthology.org/2024.acl-long.830/'} | 3 | false |
Alibaba | qwen-1 | 7B | chat | null | Industry | open | East Asia | China | third-party-papers_71 | false | 2 | 2,024 | {'title': 'SafetyBench: Evaluating the Safety of Large Language Models', 'url': 'https://aclanthology.org/2024.acl-long.830/'} | 3 | false |
OpenAI | gpt-4 | null | null | null | Industry | closed | North America | United States | third-party-papers_71 | false | 2 | 2,024 | {'title': 'SafetyBench: Evaluating the Safety of Large Language Models', 'url': 'https://aclanthology.org/2024.acl-long.830/'} | 3 | false |
Baichuan | baichuan | 13B | chat | null | Industry | open | East Asia | China | third-party-papers_71 | false | 2 | 2,024 | {'title': 'SafetyBench: Evaluating the Safety of Large Language Models', 'url': 'https://aclanthology.org/2024.acl-long.830/'} | 3 | false |
Z.ai | chatglm2 | 6B | null | null | Industry | open | East Asia | China | third-party-papers_71 | false | 2 | 2,024 | {'title': 'SafetyBench: Evaluating the Safety of Large Language Models', 'url': 'https://aclanthology.org/2024.acl-long.830/'} | 3 | false |
Z.ai | chatglm2 | null | null | null | Industry | open | East Asia | China | third-party-papers_71 | false | 2 | 2,024 | {'title': 'SafetyBench: Evaluating the Safety of Large Language Models', 'url': 'https://aclanthology.org/2024.acl-long.830/'} | 3 | false |
Meta | llama-2 | 7B | chat | null | Industry | open | North America | United States | third-party-papers_71 | false | 2 | 2,024 | {'title': 'SafetyBench: Evaluating the Safety of Large Language Models', 'url': 'https://aclanthology.org/2024.acl-long.830/'} | 3 | false |
Google | flan-t5 | 11B | xxl | null | Industry | open | North America | United States | third-party-papers_71 | false | 2 | 2,024 | {'title': 'SafetyBench: Evaluating the Safety of Large Language Models', 'url': 'https://aclanthology.org/2024.acl-long.830/'} | 3 | false |
OpenAI | gpt-3.5 | null | turbo | null | Industry | closed | North America | United States | third-party-papers_71 | false | 2 | 2,024 | {'title': 'SafetyBench: Evaluating the Safety of Large Language Models', 'url': 'https://aclanthology.org/2024.acl-long.830/'} | 3 | false |
OpenAI | gpt-3 | null | null | text-davinci-003 | Industry | closed | North America | United States | third-party-papers_71 | false | 2 | 2,024 | {'title': 'SafetyBench: Evaluating the Safety of Large Language Models', 'url': 'https://aclanthology.org/2024.acl-long.830/'} | 3 | false |
iFLYTEK | sparkdesk | null | null | null | Industry | closed | East Asia | China | third-party-papers_71 | false | 2 | 2,024 | {'title': 'SafetyBench: Evaluating the Safety of Large Language Models', 'url': 'https://aclanthology.org/2024.acl-long.830/'} | 3 | false |
Baichuan | baichuan-2 | 13B | chat | null | Industry | open | East Asia | China | third-party-papers_71 | false | 2 | 2,024 | {'title': 'SafetyBench: Evaluating the Safety of Large Language Models', 'url': 'https://aclanthology.org/2024.acl-long.830/'} | 3 | false |
ShanghaiAILab | internlm | 7B | chat | null | Government | open | East Asia | China | third-party-papers_71 | false | 2 | 2,024 | {'title': 'SafetyBench: Evaluating the Safety of Large Language Models', 'url': 'https://aclanthology.org/2024.acl-long.830/'} | 3 | false |
ShanghaiAILab | internlm | 7B | null | v1.1 | Government | open | East Asia | China | third-party-papers_71 | false | 2 | 2,024 | {'title': 'SafetyBench: Evaluating the Safety of Large Language Models', 'url': 'https://aclanthology.org/2024.acl-long.830/'} | 3 | false |
Meta | llama-2 | 13B | chat | null | Industry | open | North America | United States | third-party-papers_71 | false | 2 | 2,024 | {'title': 'SafetyBench: Evaluating the Safety of Large Language Models', 'url': 'https://aclanthology.org/2024.acl-long.830/'} | 3 | false |
Meta | llama-2 | 13B | chat | null | Industry | open | North America | United States | third-party-papers_11 | false | 2 | 2,024 | {'title': 'From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards', 'url': 'https://api.semanticscholar.org/CorpusId:268536898'} | 3 | false |
Meta | llama-2 | 7B | chat | null | Industry | open | North America | United States | third-party-papers_11 | false | 2 | 2,024 | {'title': 'From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards', 'url': 'https://api.semanticscholar.org/CorpusId:268536898'} | 3 | false |
Meta | llama-1 | 7B | null | null | Industry | closed | North America | United States | third-party-papers_11 | false | 2 | 2,024 | {'title': 'From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards', 'url': 'https://api.semanticscholar.org/CorpusId:268536898'} | 3 | false |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.