@inproceedings{pan-etal-2024-human,
title = "Human-Centered Design Recommendations for {LLM}-as-a-judge",
author = "Pan, Qian and
Ashktorab, Zahra and
Desmond, Michael and
Santill{\'a}n Cooper, Mart{\'\i}n and
Johnson, James and
Nair, Rahul and
Daly, Elizabeth and
Geyer, Werner",
editor = "Soni, Nikita and
Flek, Lucie and
Sharma, Ashish and
Yang, Diyi and
Hooker, Sara and
Schwartz, H. Andrew",
booktitle = "Proceedings of the 1st Human-Centered Large Language Modeling Workshop",
month = aug,
year = "2024",
address = "TBD",
publisher = "ACL",
url = "https://aclanthology.org/2024.hucllm-1.2",
pages = "16--29",
abstract = "Traditional reference-based metrics, such as BLEU and ROUGE, are less effective for assessing outputs from Large Language Models (LLMs) that produce highly creative or superior-quality text, or in situations where reference outputs are unavailable. While human evaluation remains an option, it is costly and difficult to scale. Recent work using LLMs as evaluators (LLM-as-a-judge) is promising, but trust and reliability remain a significant concern. Integrating human input is crucial to ensure criteria used to evaluate are aligned with the human{'}s intent, and evaluations are robust and consistent. This paper presents a user study of a design exploration called EvaluLLM, that enables users to leverage LLMs as customizable judges, promoting human involvement to balance trust and cost-saving potential with caution. Through interviews with eight domain experts, we identified the need for assistance in developing effective evaluation criteria aligning the LLM-as-a-judge with practitioners{'} preferences and expectations. We offer findings and design recommendations to optimize human-assisted LLM-as-judge systems.",
}
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<abstract>Traditional reference-based metrics, such as BLEU and ROUGE, are less effective for assessing outputs from Large Language Models (LLMs) that produce highly creative or superior-quality text, or in situations where reference outputs are unavailable. While human evaluation remains an option, it is costly and difficult to scale. Recent work using LLMs as evaluators (LLM-as-a-judge) is promising, but trust and reliability remain a significant concern. Integrating human input is crucial to ensure criteria used to evaluate are aligned with the human’s intent, and evaluations are robust and consistent. This paper presents a user study of a design exploration called EvaluLLM, that enables users to leverage LLMs as customizable judges, promoting human involvement to balance trust and cost-saving potential with caution. Through interviews with eight domain experts, we identified the need for assistance in developing effective evaluation criteria aligning the LLM-as-a-judge with practitioners’ preferences and expectations. We offer findings and design recommendations to optimize human-assisted LLM-as-judge systems.</abstract>
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%0 Conference Proceedings
%T Human-Centered Design Recommendations for LLM-as-a-judge
%A Pan, Qian
%A Ashktorab, Zahra
%A Desmond, Michael
%A Santillán Cooper, Martín
%A Johnson, James
%A Nair, Rahul
%A Daly, Elizabeth
%A Geyer, Werner
%Y Soni, Nikita
%Y Flek, Lucie
%Y Sharma, Ashish
%Y Yang, Diyi
%Y Hooker, Sara
%Y Schwartz, H. Andrew
%S Proceedings of the 1st Human-Centered Large Language Modeling Workshop
%D 2024
%8 August
%I ACL
%C TBD
%F pan-etal-2024-human
%X Traditional reference-based metrics, such as BLEU and ROUGE, are less effective for assessing outputs from Large Language Models (LLMs) that produce highly creative or superior-quality text, or in situations where reference outputs are unavailable. While human evaluation remains an option, it is costly and difficult to scale. Recent work using LLMs as evaluators (LLM-as-a-judge) is promising, but trust and reliability remain a significant concern. Integrating human input is crucial to ensure criteria used to evaluate are aligned with the human’s intent, and evaluations are robust and consistent. This paper presents a user study of a design exploration called EvaluLLM, that enables users to leverage LLMs as customizable judges, promoting human involvement to balance trust and cost-saving potential with caution. Through interviews with eight domain experts, we identified the need for assistance in developing effective evaluation criteria aligning the LLM-as-a-judge with practitioners’ preferences and expectations. We offer findings and design recommendations to optimize human-assisted LLM-as-judge systems.
%U https://aclanthology.org/2024.hucllm-1.2
%P 16-29
Markdown (Informal)
[Human-Centered Design Recommendations for LLM-as-a-judge](https://aclanthology.org/2024.hucllm-1.2) (Pan et al., HuCLLM-WS 2024)
ACL
- Qian Pan, Zahra Ashktorab, Michael Desmond, Martín Santillán Cooper, James Johnson, Rahul Nair, Elizabeth Daly, and Werner Geyer. 2024. Human-Centered Design Recommendations for LLM-as-a-judge. In Proceedings of the 1st Human-Centered Large Language Modeling Workshop, pages 16–29, TBD. ACL.