| Challenge: | Automated metrics have reported flaws when applied to measure quality aspects of generated text and have been shown to correlate poorly with human judgements. |
| Approach: | They propose an agent-based framework to measure the required number of human annotations when evaluating generated outputs in relative comparison settings. |
| Outcome: | The proposed model can be compared with a crowdsourced case study and a simulation with simulated human judgements. |
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Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review Generation (D19-1)
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| Challenge: | Existing evaluation methods for natural language generation are inadequate . distinguishing machine-generated text is challenging even for human evaluators . |
| Approach: | They compare human-based evaluators with automated evaluation procedures . they find human evaluers do not correlate well with discriminative evalators . |
| Outcome: | The proposed evaluation methods are compared with a dozen state-of-the-art generators for online product reviews. |
Enhancing Human Evaluation in Machine Translation with Comparative Judgement (2025.acl-long)
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| Challenge: | Human evaluation is crucial for assessing rapidly evolving language models but is influenced by annotator proficiency and task design. |
| Approach: | They evaluate three annotation setups to integrate comparative judgment into human annotation for machine translation. |
| Outcome: | The proposed approach improves inter-annotator agreement and stability of the annotations. |
ConSiDERS-The-Human Evaluation Framework: Rethinking Human Evaluation for Generative Large Language Models (2024.acl-long)
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| Challenge: | In this position paper, we argue that human evaluation of generative large language models (LLMs) should be a multidisciplinary undertaking that draws upon the insights from disciplines such as user experience research and human behavioral psychology to ensure that the results are reliable. |
| Approach: | They propose a framework for human evaluation of generative large language models that takes into account usability, aesthetics and cognitive biases. |
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GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation (2022.emnlp-main)
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Daniel Khashabi, Gabriel Stanovsky, Jonathan Bragg, Nicholas Lourie, Jungo Kasai, Yejin Choi, Noah A. Smith, Daniel Weld
| Challenge: | Effective human evaluation of text generation tasks remains an important, open area for research. |
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A Human Evaluation of AMR-to-English Generation Systems (2020.coling-main)
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| Challenge: | a recent human evaluation of AMR generation systems is compared to automated metrics. |
| Approach: | They propose a human evaluation which collects fluency and adequacy scores and categorization of error types for AMR generation systems. |
| Outcome: | The results show that human evaluations are more nuanced than automated metrics. |
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 . |
| Approach: | They propose to evaluate machine-generated text across multiple dimensions using the Elo rating system . they propose to use crowd-sourced and expert annotators to rank models based on Elo ratings . |
| Outcome: | The proposed method improves the quality of LLM-based evaluations, but there is no improvement in crowd-sourced evaluations. |
Just Put a Human in the Loop? Investigating LLM-Assisted Annotation for Subjective Tasks (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have shown impressive performance in many annotation tasks, including subjective tasks common in content moderation and text analysis in the social sciences. |
| Approach: | They propose to give crowdworkers LLM-generated annotation suggestions to "review" LLMs for subjective tasks can impact model performance and analysis downstream . |
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All That’s ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text (2021.acl-long)
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| Challenge: | evaluators distinguish between human- and machine-authored text in three domains without training . evals' accuracy improved up to 55%, but it did not significantly improve across the three domain. |
| Approach: | They examine the role untrained human evaluations play in NLG evaluation and propose ways to improve their evaluations. |
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Unifying Human and Statistical Evaluation for Natural Language Generation (N19-1)
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| Challenge: | Human evaluation captures quality but fails to capture diversity . statistical evaluation fails to catch models that plagiarize from training set . |
| Approach: | They propose a framework which evaluates both diversity and quality based on the optimal error rate of predicting whether a sentence is human-generated. |
| Outcome: | The proposed framework evaluates diversity and quality on summarization and chit-chat dialogue. |
Finding Replicable Human Evaluations via Stable Ranking Probability (2024.naacl-long)
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| Challenge: | a recent study shows that human evaluation is the best way to rank natural language generation systems . human raters can exhibit different behaviors when rating outputs, causing ranking to be unstable . stability is the degree to which a specific evaluation methodology produces the same system ranking when repeated. |
| Approach: | They propose to evaluate results through the lens of stability: stability is the degree to which a specific evaluation methodology produces the same system ranking when repeated. |
| Outcome: | The proposed model is based on a dataset of multi-segment translations rated by multiple professionals . human raters can exhibit different behaviors when rating NLG outputs, the study shows . |