From Fallback to Frontline: When Can LLMs be Superior Annotators of Human Perspectives? (2026.findings-acl)
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| Challenge: | large language models are often used as annotators at scale, but are not faithful estimators of human perspectives. |
| Approach: | They characterize the conditions under which large language models outperform human annotators . they find they are statistically superior frontline estimators based on low variance . |
| Outcome: | The proposed model outperforms human annotators when predicting subgroup opinions on subjective tasks. |
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| Challenge: | Large language models have shown capabilities close to human performance in various analytical tasks. |
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Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization (2023.findings-emnlp)
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| Challenge: | ChatGPT and GPT-4 are popular as evaluation metric for complex generative tasks . however, they are not ready as human replacements due to significant limitations . |
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| Challenge: | Existing work uses large language models (LLMs) to evaluate natural language process tasks, but there are shortcomings in current LLMs. |
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Evaluating Large Language Model Biases in Persona-Steered Generation (2024.findings-acl)
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| Challenge: | a recent wave of powerful new large language models has raised concerns that their expressed opinions may be biased towards certain political, national or moral viewpoints. |
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| Challenge: | Recent trends in natural language processing and annotation tasks emphasize individual perspectives . annotator models that rely on a single ground truth may disregard valuable minority perspectives omissions . |
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“You Are An Expert Linguistic Annotator”: Limits of LLMs as Analyzers of Abstract Meaning Representation (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) demonstrate proficiency and fluency in the use of language, but do they have the linguistic knowledge to serve as an expert linguistic annotator? |
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Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation? (2024.findings-eacl)
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Rishav Hada, Varun Gumma, Adrian Wynter, Harshita Diddee, Mohamed Ahmed, Monojit Choudhury, Kalika Bali, Sunayana Sitaram
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Missing the Margins: A Systematic Literature Review on the Demographic Representativeness of LLMs (2025.findings-acl)
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| Challenge: | 211 studies on the demographic representativeness of large language models have conflicting results . 29% of the studies report positive conclusions on the representativeness, 30% do not evaluate LLMs across multiple demographic categories or within demographic subcategories. |
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Style Over Substance: Evaluation Biases for Large Language Models (2025.coling-main)
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| Challenge: | Ranking the relative performance of large language models based on Elo ratings is gaining popularity . however, the extent to which humans and LLMs are capable evaluators remains uncertain . |
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Are Large Language Models (LLMs) Good Social Predictors? (2024.findings-emnlp)
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| Challenge: | Existing studies suggest that Large Language Models can generate human-like responses, but it is unclear how well they work and where the plausible predictions derive from. |
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