---
title: "Large Language Model Instruction Following: A Survey of"
description: "The authors present this as the first comprehensive survey of instruction following. 102 references, cited by 27 papers here."
authors: ["Renze Lou", "Kai Zhang", "Wenpeng Yin"]
year: 2024
venue: "Computational Linguistics"
source: "https://aclanthology.org/2024.cl-3.7/"
doi: "10.1162/coli_a_00523"
licence: "CC BY-NC-ND 4.0 (Computational Linguistics, MIT Press)"
licence_url: "https://creativecommons.org/licenses/by-nc-nd/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/lou-2024-large-language-model-instruction-following/"
---
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# Large Language Model Instruction Following: A Survey of Progresses and Challenges

**Authors:** [Renze Lou](https://md.research.elamur.ai/free/en/authors/renze-lou/), [Kai Zhang](https://md.research.elamur.ai/free/en/authors/kai-zhang/) and [Wenpeng Yin](https://md.research.elamur.ai/free/en/authors/wenpeng-yin/) · **Year:** 2024 · **Venue:** [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/) ([Computational Linguistics 2024](https://md.research.elamur.ai/free/en/venues/cl/2024/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY-NC-ND 4.0 (Computational Linguistics, MIT Press)](https://creativecommons.org/licenses/by-nc-nd/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.cl-3.7/) · [PDF](https://aclanthology.org/2024.cl-3.7.pdf) · [DOI 10.1162/coli\_a\_00523](https://doi.org/10.1162/coli_a_00523) · [OpenAlex](https://openalex.org/W4399492476)

**Citations:** 56 citations (OpenAlex, 2026-10-14) · [27](https://md.research.elamur.ai/free/en/papers/lou-2024-large-language-model-instruction-following/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Most NLP systems learn a task from large sets of labeled examples. These are costly to collect, can be unobtainable for complex or newly emerging tasks, and are awkward for end users, who would rather describe the task than annotate data. This survey reviews the alternative paradigm, in which models, especially LLMs, learn to perform tasks from textual instructions. The authors structure the field around five questions:

- what an instruction is and which kinds exist,
- how instructions can be modeled,
- which datasets and metrics are standard,
- which factors drive performance,
- which problems remain open.

They frame every instruction type as a form of indirect supervision and treat prompts as only one special case. They also link current instruction tuning to much older work on semantic parsing. A central theme is "dual-track" scaling: performance depends on both model size and the number and diversity of instruction-tuning tasks. Such datasets come either from human annotators or from distilling strong LLMs such as ChatGPT and GPT-4. The authors present this as the first comprehensive survey of instruction following.

## Key points
- **Taxonomy.** Instructions are defined through a template (T), input (X), and output (Y), giving three types:
  - NLI-oriented instructions (I = T + Y), where each label is rewritten as a hypothesis, following Yin, Hay, and Roth (2019);
  - LLM-oriented prompts (I = T + X), in prefix or cloze form;
  - instructions made of the template alone.
- **NLI-oriented strengths.** This approach preserves label semantics, merges diverse classification problems into a single NLI task, and reuses existing NLI datasets for zero-shot transfer. It also allows an open label set. It has been applied to topic, sentiment, stance, entity-type, and relation classification.
- **Dual-track scaling.** Larger models tuned on more varied tasks give clearly better few/zero-shot results (e.g., Chung et al. 2022). Instruction datasets are produced in one of two ways: through human annotation, as in Köpf et al. 2023, Longpre et al. 2023, and Wang et al. 2022b, or through distillation from LLMs, as in Taori et al. 2023 and Honovich et al. 2023a.
- **Wider uses of instructions.** Instructions also serve as distant supervision for data and feature augmentation, and as a basis for instruction-conditioned embeddings (Su et al. 2023). Chung et al. (2022) found that instruction tuning alone, without human feedback, lowered toxicity and bias in PaLM's open-ended generations.
- **Resource.** A curated companion paper list is available at github.com/RenzeLou/awesome-instruction-learning.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [The Ohio State University](https://md.research.elamur.ai/free/en/universities/the-ohio-state-university/) — University, Columbus, United States · 90 works · 19,112 citations
- [Pennsylvania State University](https://md.research.elamur.ai/free/en/universities/pennsylvania-state-university/) — University, State College, United States · 67 works · 5,865 citations

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## Cite

Cite as: Renze Lou, Kai Zhang and Wenpeng Yin. 2024. Large Language Model Instruction Following: A Survey of Progresses and Challenges. Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{lou2024large,
  title = {{Large Language Model Instruction Following: A Survey of Progresses and Challenges}},
  author = {Renze Lou, Kai Zhang and Wenpeng Yin},
  journal = {Computational Linguistics},
  year = {2024},
  doi = {10.1162/coli\_a\_00523},
  url = {https://doi.org/10.1162/coli_a_00523}
}
```

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*Updated 2 November 2026*