# Exploring the Reliability of Large Language Models as Customized Evaluators for Diverse NLP Tasks

arXiv: https://arxiv.org/abs/2310.19740 · HTML: https://mlsanity.com/paper/2310.19740/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 40 references that are in the map.
- First posted on arXiv 30 Oct 2023.

## Builds on (most cited first, 24 of 24)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Can Large Language Models Be an Alternative to Human Evaluations?](https://mlsanity.com/paper/2305.01937/) (2023)
- [BARTScore: Evaluating Generated Text as Text Generation](https://mlsanity.com/paper/2106.11520/) (2021)
- [ELI5: Long Form Question Answering](https://mlsanity.com/paper/1907.09190/) (2019)
- [Is ChatGPT a Good NLG Evaluator? A Preliminary Study](https://mlsanity.com/paper/2303.04048/) (2023)
- [MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance](https://mlsanity.com/paper/1909.02622/) (2019)
- [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://mlsanity.com/paper/1604.01696/) (2016)
- [Evaluating Verifiability in Generative Search Engines](https://mlsanity.com/paper/2304.09848/) (2023)
- [Evaluating Large Language Models at Evaluating Instruction Following](https://mlsanity.com/paper/2310.07641/) (2023)
- [FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization](https://mlsanity.com/paper/2005.03754/) (2020)
- [QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization](https://mlsanity.com/paper/2112.08542/) (2021)
- [Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics](https://mlsanity.com/paper/2006.06264/) (2020)
- [Deductive Verification of Chain-of-Thought Reasoning](https://mlsanity.com/paper/2306.03872/) (2023)
- [Wider and Deeper LLM Networks are Fairer LLM Evaluators](https://mlsanity.com/paper/2308.01862/) (2023)
- [Multi-Dimensional Evaluation of Text Summarization with In-Context Learning](https://mlsanity.com/paper/2306.01200/) (2023)
- [Towards Quantifiable Dialogue Coherence Evaluation](https://mlsanity.com/paper/2106.00507/) (2021)
- [Neural Language Generation: Formulation, Methods, and Evaluation](https://mlsanity.com/paper/2007.15780/) (2020)
- [Evaluating the Morphosyntactic Well-formedness of Generated Texts](https://mlsanity.com/paper/2103.16590/) (2021)

## Built on by (most cited first, 24 of 24)

- [From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge](https://mlsanity.com/paper/2411.16594/) (2024)
- [LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods](https://mlsanity.com/paper/2412.05579/) (2024)
- [LLM-based NLG Evaluation: Current Status and Challenges](https://mlsanity.com/paper/2402.01383/) (2024)
- [HD-Eval: Aligning Large Language Model Evaluators Through Hierarchical Criteria Decomposition](https://mlsanity.com/paper/2402.15754/) (2024)
- [Themis: A Reference-free NLG Evaluation Language Model with Flexibility and Interpretability](https://mlsanity.com/paper/2406.18365/) (2024)
- [What Makes a Good Story and How Can We Measure It? A Comprehensive Survey of Story Evaluation](https://mlsanity.com/paper/2408.14622/) (2024)
- [Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems](https://mlsanity.com/paper/2502.11098/) (2025)
- [Assessing and Verifying Task Utility in LLM-Powered Applications](https://mlsanity.com/paper/2405.02178/) (2024)
- [Concept -- An Evaluation Protocol on Conversational Recommender Systems with System-centric and User-centric Factors](https://mlsanity.com/paper/2404.03304/) (2024)
- [Model Directions, Not Words: Mechanistic Topic Models Using Sparse Autoencoders](https://mlsanity.com/paper/2507.23220/) (2025)
- [CLIMATEAGENT: Multi-Agent Orchestration for Complex Climate Data Science Workflows](https://mlsanity.com/paper/2511.20109/) (2025)
- [Do LLMs Agree on the Creativity Evaluation of Alternative Uses?](https://mlsanity.com/paper/2411.15560/) (2024)
- [Evaluating the Evaluators: Are readability metrics good measures of readability?](https://mlsanity.com/paper/2508.19221/) (2025)
- [Towards better Human-Agent Alignment: Assessing Task Utility in LLM-Powered Applications](https://mlsanity.com/paper/2402.09015/) (2024)
- [Graders should cheat: privileged information enables expert-level automated evaluations](https://mlsanity.com/paper/2502.10961/) (2025)
- [LLM Judges Can Be Too Generous When There Is No Reference Answer](https://mlsanity.com/paper/2607.12885/) (2026)
- [From Multimodal Perception to Strategic Reasoning: A Survey on AI-Generated Game Commentary](https://mlsanity.com/paper/2506.17294/) (2025)
- [Generate, Evaluate, Iterate: Synthetic Data for Human-in-the-Loop Refinement of LLM Judges](https://mlsanity.com/paper/2511.04478/) (2025)
- [Open Evaluation Agent: Efficient and Promptable Evaluation of Visual Generative Models](https://mlsanity.com/paper/2608.09666/) (2026)
- [OptAgent: Optimizing Query Rewriting for E-commerce via Multi-Agent Simulation](https://mlsanity.com/paper/2510.03771/) (2025)
- [Exploring Learners' Expectations and Engagement When Collaborating with Constructively Controversial Peer Agents](https://mlsanity.com/paper/2601.13479/) (2026)
- [Personalized Turn-Level User Conversation Satisfaction Benchmark](https://mlsanity.com/paper/2605.29711/) (2026)
- [Measuring Semantic Progress in Multi-turn Dialogue via Information Gain](https://mlsanity.com/paper/2606.12332/) (2026)
- [Improving the Evaluation of Biomedical Question Answering with Few Human Preference Signals](https://mlsanity.com/paper/2610.16008/) (2026)

## Abstract (arXiv)

Previous work adopts large language models (LLMs) as evaluators to evaluate natural language process (NLP) tasks. However, certain shortcomings, e.g., fairness, scope, and accuracy, persist for current LLM evaluators. To analyze whether LLMs can serve as reliable alternatives to humans, we examine the fine-grained alignment between LLM evaluators and human annotators, particularly in understanding the target evaluation tasks and conducting evaluations that meet diverse criteria. This paper explores both conventional tasks (e.g., story generation) and alignment tasks (e.g., math reasoning), each with different evaluation criteria. Our analysis shows that 1) LLM evaluators can generate unnecessary criteria or omit crucial criteria, resulting in a slight deviation from the experts. 2) LLM evaluators excel in general criteria, such as fluency, but face challenges with complex criteria, such as numerical reasoning. We also find that LLM-pre-drafting before human evaluation can help reduce the impact of human subjectivity and minimize annotation outliers in pure human evaluation, leading to more objective evaluation.