JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors | Paper Citation Record

Observation a4619eea-3db7-4c6e-9cb8-66efad165434 · outbound

This paper cites System Card: Claude Haiku 4.5, 2025.

JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors System Card: Claude Haiku 4.5, 2025

Reference 1

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Observation 09944844-4069-4bfa-9b4d-89488064d0df · outbound

This paper cites System Card: Claude Opus 4.7, 2026.

JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors System Card: Claude Opus 4.7, 2026

Reference 2

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Observation 0a40819c-4b10-453b-a193-53660fb0d71f · outbound

This paper cites CaLMQA: Exploring culturally specific long-form question answering across 23 languages.

JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors CaLMQA: Exploring culturally specific long-form question answering across 23 languages

Reference 3

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This paper cites Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems , articleno =.

JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems , articleno =

Reference 4

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Observation fc79d55a-41f0-4566-9838-85f3ed56fcf5 · outbound

This paper cites Knowledge-Centric Hallucination Detection.

JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Knowledge-Centric Hallucination Detection

Reference 5

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This paper cites How people use chatgpt.

JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors How people use chatgpt

Reference 6

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Observation 60953bf4-2b91-4e56-a1b6-3c7b6c7469da · outbound

This paper cites Humans or LLM s as the Judge? A Study on Judgement Bias.

JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Humans or LLM s as the Judge? A Study on Judgement Bias

Reference 7

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This paper cites Diagnosing the Reliability of LLM-as-a-Judge via Item Response Theory.

JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Diagnosing the Reliability of LLM-as-a-Judge via Item Response Theory

Reference 8

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Observation 92b1dd5c-3a6e-473f-aa64-13e81f22fc77 · outbound

This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.

JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 9

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Observation cb6399c7-fa6c-4847-919e-f7556099c6cd · outbound

This paper cites Massively Multi-Cultural Knowledge Acquisition & LM Benchmarking.

JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Massively Multi-Cultural Knowledge Acquisition & LM Benchmarking

Reference 10

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This paper cites Thick description: Toward an interpretive theory of culture.

JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Thick description: Toward an interpretive theory of culture

Reference 11

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This paper cites Gemini 3 Flash Model Card, 2025.

JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Gemini 3 Flash Model Card, 2025

Reference 12

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Observation 2fc0f6a9-c570-44a9-83ad-1b5420f6ed19 · outbound

This paper cites Gemini 3 Pro Model Card, 2025.

JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Gemini 3 Pro Model Card, 2025

Reference 13

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This paper cites Gemini 3.1 Pro Model Card, 2026.

JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Gemini 3.1 Pro Model Card, 2026

Reference 14

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Gemma 4 model card, 2026

Reference 15

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors LLM-GLOBE: A Benchmark Evaluating the Cultural Values Embedded in LLM Output

Reference 16

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Observation d09e47b2-1b0d-495b-9367-ff5fb176e4da · outbound

This paper cites LLMs as span annotators: A comparative study of LLMs and humans.

JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors LLMs as span annotators: A comparative study of LLMs and humans

Reference 17

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Observation bb1938be-67d7-4650-a846-46939ff4bd65 · outbound

This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Gonzalez, Hao Zhang, and Ion Stoica

Reference 18

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This paper cites From generation to judgment: Opportunities and challenges of LLM-as-a-judge.

JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors From generation to judgment: Opportunities and challenges of LLM-as-a-judge

Reference 19

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This paper cites The Llama 4 herd: The beginning of a new era of natively multimodal AI innovation,.

JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors The Llama 4 herd: The beginning of a new era of natively multimodal AI innovation,

Reference 20

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Accessed: 2026-05-06

Reference 21

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Blend: A benchmark for llms on every- day knowledge in diverse cultures and languages

Reference 22

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Having beer after prayer? mea- suring cultural bias in large language models

Reference 23

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Having Beer after Prayer? Measuring Cultural Bias in Large Language Models

Reference 24

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors In: Christodoulopoulos, C., Chakraborty, T., Rose, C., Peng, V

Reference 25

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors GPT-5.1 Instant and GPT-5.1 Thinking System Card Addendum, 2025

Reference 26

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Introducing GPT -5.4 mini and nano, 2026

Reference 27

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This paper cites GPT-5.5 System Card, 2026.

JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors GPT-5.5 System Card, 2026

Reference 28

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors gpt-oss-120b & gpt-oss-20b Model Card

Reference 29

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Survey of cultural awareness in language models: Text and beyond.Computational Linguistics, 51(3):907–1004, September

Reference 30

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors URLhttps://aclanthology.org/2025.cl-3.7/

Reference 31

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors thick evaluations

Reference 32

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This paper cites Localizing and mitigating errors in long-form question answering.

JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Localizing and mitigating errors in long-form question answering

Reference 33

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This paper cites C ulture B ank: An Online Community-Driven Knowledge Base Towards Culturally Aware Language Technologies.

JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors C ulture B ank: An Online Community-Driven Knowledge Base Towards Culturally Aware Language Technologies

Reference 34

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors OpenAI GPT-5 System Card

Reference 35

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors MM-Eval: A Multilingual Meta-Evaluation Benchmark for LLM-as-a-Judge and Reward Models

Reference 36

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors doi: 10.18653/v1/2024.findings-emnlp.552

Reference 37

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Judgebench: A benchmark for evaluating LLM-based judges

Reference 38

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Cure: Cultural understanding and reasoning evaluation - a framework for "thick" culture alignment evaluation in llms, 2025

Reference 39

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Qwen3 Technical Report

Reference 40

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Hedderich

Reference 41

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Judging llm-as-a-judge with mt-bench and chatbot arena

Reference 42

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors ISBN 979-8-89176-251-0

Reference 43

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors fresh soy milk (kacang kedelai)

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Rewrite the explanation to clearly convey their rationale

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Validator Comment

Reference 48

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Suggested Correction

Reference 49

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors LLM Response

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Validator Comment

Reference 51

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This paper cites You may also use code-switched text where English acts as the matrix (base) language, embedding specific expressions, words, or idioms from the original language of the text.

JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors You may also use code-switched text where English acts as the matrix (base) language, embedding specific expressions, words, or idioms from the original language of the text

Reference 52

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Validator Comment

Reference 53

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Unresolved cited work

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Unresolved cited work

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors Guidelines:

Reference 56

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JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors - The text spans do not need to be perfectly identical

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