# FollowEval: A Multi-Dimensional Benchmark for Assessing the Instruction-Following Capability of Large Language Models

arXiv: https://arxiv.org/abs/2311.09829 · HTML: https://mlsanity.com/paper/2311.09829/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 31 references that are in the map.
- First posted on arXiv 16 Nov 2023.

## Builds on (most cited first, 22 of 22)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [LIMA: Less Is More for Alignment](https://mlsanity.com/paper/2305.11206/) (2023)
- [Large Language Models are not Fair Evaluators](https://mlsanity.com/paper/2305.17926/) (2023)
- [Is ChatGPT a Good NLG Evaluator? A Preliminary Study](https://mlsanity.com/paper/2303.04048/) (2023)
- [How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources](https://mlsanity.com/paper/2306.04751/) (2023)
- [Learning to Write with Cooperative Discriminators](https://mlsanity.com/paper/1805.06087/) (2018)
- [Evaluating Large Language Models on Controlled Generation Tasks](https://mlsanity.com/paper/2310.14542/) (2023)
- [COLLIE: Systematic Construction of Constrained Text Generation Tasks](https://mlsanity.com/paper/2307.08689/) (2023)
- [Can Large Language Models Understand Real-World Complex Instructions?](https://mlsanity.com/paper/2309.09150/) (2023)
- [SummIt: Iterative Text Summarization via ChatGPT](https://mlsanity.com/paper/2305.14835/) (2023)
- [Controllable Text Generation with Language Constraints](https://mlsanity.com/paper/2212.10466/) (2022)
- [Improving Large Language Models in Event Relation Logical Prediction](https://mlsanity.com/paper/2310.09158/) (2023)
- [ChatLog: Carefully Evaluating the Evolution of ChatGPT Across Time](https://mlsanity.com/paper/2304.14106/) (2023)

## Built on by (most cited first, 12 of 12)

- [The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models](https://mlsanity.com/paper/2406.05761/) (2024)
- [SOP-Bench: Complex Industrial SOPs for Evaluating LLM Agents](https://mlsanity.com/paper/2506.08119/) (2025)
- [How Many Instructions Can LLMs Follow at Once?](https://mlsanity.com/paper/2507.11538/) (2025)
- [Incentivizing Reasoning for Advanced Instruction-Following of Large Language Models](https://mlsanity.com/paper/2506.01413/) (2025)
- [EvaLearn: Quantifying the Learning Capability and Efficiency of LLMs via Sequential Problem Solving](https://mlsanity.com/paper/2506.02672/) (2025)
- [Inverse IFEval: Can LLMs Unlearn Stubborn Training Conventions to Follow Real Instructions?](https://mlsanity.com/paper/2509.04292/) (2025)
- [Light-IF: Endowing LLMs with Generalizable Reasoning via Preview and Self-Checking for Complex Instruction Following](https://mlsanity.com/paper/2508.03178/) (2025)
- [TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models](https://mlsanity.com/paper/2506.18421/) (2025)
- [Buy versus Build an LLM: A Decision Framework for Governments](https://mlsanity.com/paper/2602.13033/) (2026)
- [JT-DA: Enhancing Data Analysis with Tool-Integrated Table Reasoning Large Language Models](https://mlsanity.com/paper/2512.06859/) (2025)
- [ReAD: Reinforcement-Guided Capability Distillation for Large Language Models](https://mlsanity.com/paper/2605.11290/) (2026)
- [Video-IFBench: Evaluating Instruction Following of Multimodal LLMs in Video Understanding Scenarios](https://mlsanity.com/paper/2608.25529/) (2026)

## Abstract (arXiv)

The effective assessment of the instruction-following ability of large language models (LLMs) is of paramount importance. A model that cannot adhere to human instructions might be not able to provide reliable and helpful responses. In pursuit of this goal, various benchmarks have been constructed to evaluate the instruction-following capacity of these models. However, these benchmarks are limited to a single language and are constructed using automated approaches, which restricts their applicability and the quality of the test examples they contain. To bridge this gap, we introduce the FollowEval benchmark in this paper. This benchmark is composed of instances in both English and Chinese, and all test examples are crafted by human experts. Furthermore, the FollowEval benchmark is designed to assess LLMs across five critical dimensions of instruction following: string manipulation, commonsense reasoning, logical reasoning, spatial reasoning, and response constraints. To enhance the complexity and present a sufficient challenge, each test example is designed to evaluate more than one dimension. We have evaluated various LLMs using the FollowEval benchmark and found that their performance significantly lags behind that of humans. This highlights the considerable room for improvement in the instruction-following ability of these models.