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01potsawee /chatbot-arena-llm-judges Chatbot-Arena https://www.kaggle.com/competitions/lmsys-chatbot-arena/data Single-turn data: https://huggingface.co/datasets/potsawee/chatbot-arena-llm-judges #examples = 49938 split: A_win = 17312 (34.67%), B_win = 16985 (34.01%), tie = 15641 (31.32%) #2-way only examples = 34297 (68.68%) This repository train.single-turn.json: data extracted from the train file from LMSys on Kaggle each example has attributes - id, model_[a, b], winne_model_[a, b, tie], question… See the full description on the dataset page: https://huggingface.co/datasets/potsawee/chatbot-arena-llm-judges.tabular10K<n<100K0 likes101 downloads2y agoHugging Face02Ramitha /rq2-all-records-old-llm-judgestabular1K<n<10K0 likes43 downloads3mo agoHugging Face03potsawee /halueval-llm-judges HaluEval Data: https://github.com/RUCAIBox/HaluEval/blob/main/data/dialogue_data.json Judge prompt: https://github.com/RUCAIBox/HaluEval/blob/main/evaluation/dialogue/dialogue_evaluation_instruction.txt split: 20,000 examples in total --> 10,000 good responses (i.e., ref="no") + 10,000 hallucinated responses (i.e., ref="yes") This repository llm-judges folder contains predictions made by each judge (logits over Yes and No) Note that "Yes" means there is a… See the full description on the dataset page: https://huggingface.co/datasets/potsawee/halueval-llm-judges.tabular100K<n<1M0 likes21 downloads2y agoHugging Face04Ramitha /rq2-all-records-new-llm-judgestabular1K<n<10K0 likes18 downloads3mo agoHugging Face05Ramitha /rq2-all-records-new-llm-judges-gemmatabular1K<n<10K0 likes14 downloads3mo agoHugging Face06Ramitha /rq2-all-records-new-llm-judges-falcontabular1K<n<10K0 likes11 downloads3mo agoHugging Face07sabaridsnfuji /repro-a-coin-flip-for-safety-llm-judges-fail-to-reliably-measure-adversarial-robustness Reproduction: A Coin Flip for Safety - LLM Judges Fail to Reliably Measure Adversarial Robustness Paper Information Title: A Coin Flip for Safety: LLM Judges Fail to Reliably Measure Adversarial Robustness OpenReview ID: RgnoWsmYBM Conference: ICML 2026 Task: Evaluate 4 LLM-based safety judges on 6,642 human-verified adversarial prompts Reproduction Summary This paper's all 6 claims require LLM inference on proprietary adversarial prompts and… See the full description on the dataset page: https://huggingface.co/datasets/sabaridsnfuji/repro-a-coin-flip-for-safety-llm-judges-fail-to-reliably-measure-adversarial-robustness.textn<1K0 likes8 downloads2mo agoHugging Face08Ramitha /rq2-all-records-new-llm-judges-llamatabular1K<n<10K1 likes6 downloads3mo agoHugging Face09Ramitha /rq2-all-records-new-llm-judges-mistraltabular1K<n<10K0 likes5 downloads3mo agoHugging Face

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