@inproceedings{zhou-tan-2026-autochecklist,
title = "{A}uto{C}hecklist: Composable Pipelines for Checklist Generation and Scoring with {LLM}-as-a-Judge",
author = "Zhou, Karen and
Tan, Chenhao",
editor = "Durrett, Greg and
Jian, Ping",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-demo.51/",
pages = "515--525",
ISBN = "979-8-89176-392-0",
abstract = "Checklists have emerged as a popular approach for interpretable and fine-grained evaluation, particularly with LLM-as-a-Judge. Beyond evaluation, these structured criteria can serve as signals for model alignment, reinforcement learning, and self-correction. To support these use cases, we present $AutoChecklist$, an open-source library that unifies checklist-based evaluation into composable pipelines. At its core is a taxonomy of five checklist generation abstractions, each encoding a distinct strategy for deriving evaluation criteria. A modular $\textit{Generator $\rightarrow$ Refiner $\rightarrow$ Scorer}$ pipeline connects any generator with a unified scorer, and new configurations can be registered via prompt templates alone. The library ships with ten built-in pipelines implementing published approaches and supports multiple LLM providers (OpenAI, OpenRouter, vLLM). Beyond the Python API, the library includes a CLI for off-the-shelf evaluation and a web interface for interactive exploration. Validation experiments confirm that these checklist methods significantly align with human preferences and quality ratings, and a case study on ICLR peer review rebuttals demonstrates flexible domain adaptation. $AutoChecklist$ is publicly available at https://github.com/ChicagoHAI/AutoChecklist."
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%0 Conference Proceedings
%T AutoChecklist: Composable Pipelines for Checklist Generation and Scoring with LLM-as-a-Judge
%A Zhou, Karen
%A Tan, Chenhao
%Y Durrett, Greg
%Y Jian, Ping
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-392-0
%F zhou-tan-2026-autochecklist
%X Checklists have emerged as a popular approach for interpretable and fine-grained evaluation, particularly with LLM-as-a-Judge. Beyond evaluation, these structured criteria can serve as signals for model alignment, reinforcement learning, and self-correction. To support these use cases, we present AutoChecklist, an open-source library that unifies checklist-based evaluation into composable pipelines. At its core is a taxonomy of five checklist generation abstractions, each encoding a distinct strategy for deriving evaluation criteria. A modular Generator \rightarrow Refiner \rightarrow Scorer pipeline connects any generator with a unified scorer, and new configurations can be registered via prompt templates alone. The library ships with ten built-in pipelines implementing published approaches and supports multiple LLM providers (OpenAI, OpenRouter, vLLM). Beyond the Python API, the library includes a CLI for off-the-shelf evaluation and a web interface for interactive exploration. Validation experiments confirm that these checklist methods significantly align with human preferences and quality ratings, and a case study on ICLR peer review rebuttals demonstrates flexible domain adaptation. AutoChecklist is publicly available at https://github.com/ChicagoHAI/AutoChecklist.
%U https://aclanthology.org/2026.acl-demo.51/
%P 515-525
Markdown (Informal)
[AutoChecklist: Composable Pipelines for Checklist Generation and Scoring with LLM-as-a-Judge](https://aclanthology.org/2026.acl-demo.51/) (Zhou & Tan, ACL 2026)
ACL