📢 Call for Papers#

Title: Reliability and Bias in LLM-as-a-Judge Approaches

Source: Springer

Found Date: 2026-03-31

Published: 2026-10-20

📖 Journal/Collection Information#

Name: Reliability and Bias in LLM-as-a-Judge Approaches

Official Link: https://link.springer.com/collections/gdifdjgjii

👥 Guest Editors#

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🎯 Topics of Interest#

The rapid proliferation of diverse LLMs, coupled with a fast development cycle, intensifies the need for robust and scalable solutions for performance evaluation. This is particularly relevant in nuanced and open-ended tasks, such as creative writing and perspective taking. A widely adopted approach is using other, often more powerful, LLMs as evaluators. However, this method is susceptible to well-known biases and model misalignment issues as well as raising fundamental epistemological questions. The purpose of this collection is to stimulate the scientific discussion on the “LLM-as-a-Judge” paradigm, investigating opportunities (e.g., correlations with human judgement), pitfalls (e.g., verbosity, self-preference, sycophancy) and methodological aspects (e.g., mitigation strategies such as…

📝 Description#

The rapid proliferation of diverse LLMs, coupled with a fast development cycle, intensifies the need for robust and scalable solutions for performance evaluation. This is particularly relevant in nuanced and open-ended tasks, such as creative writing and perspective taking. A widely adopted approach is using other, often more powerful, LLMs as evaluators. However, this method is susceptible to well-known biases and model misalignment issues as well as raising fundamental epistemological questions. The purpose of this collection is to stimulate the scientific discussion on the “LLM-as-a-Judge” paradigm, investigating opportunities (e.g., correlations with human judgement), pitfalls (e.g., verbosity, self-preference, sycophancy) and methodological aspects (e.g., mitigation strategies such as pairwise permutation, chain-of-thought verification, and multi-agent debate). Ultimately, this collection aims to establish best practices and distinct boundaries for the reliable use of automated evaluation in LLM-powered research.

🔗 Submit Your Manuscript#

https://link.springer.com/collections/gdifdjgjii

📌 Note#

This article is automatically collected and organized by the system. For detailed information and submission, please visit the official Springer page via the link above.

Last Updated: 2026-03-31 10:16:17