---
title: "Awesome-Multimodal-Large-Language-Models vs AlignLLMHumanSurvey"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/bradyfu-awesome-multimodal-large-language-models-vs-garyyufei-alignllmhumansurvey"
tools: ["bradyfu-awesome-multimodal-large-language-models", "garyyufei-alignllmhumansurvey"]
---

# Awesome-Multimodal-Large-Language-Models vs AlignLLMHumanSurvey

*GraphCanon updated Jul 12, 2026*

## Verdict

Pick Awesome-Multimodal-Large-Language-Models if awesome-Multimodal-Large-Language-Models is a curated collection of surveys and benchmarks focused on multimodal large language models (MLLMs), encompassing evaluation frameworks, interactive Omni MLLMs, and benchmarking; pick AlignLLMHumanSurvey if alignLLMHumanSurvey is a survey repository aggregating resources and research on aligning large language models with human expectations through various methodologies like data collection, training techniques, and model评价.

[Awesome-Multimodal-Large-Language-Models](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models) reports 18k GitHub stars, 1.1k forks, and 104 open issues, last pushed Jul 2, 2026. [AlignLLMHumanSurvey](https://arxiv.org/abs/2307.12966) has 742 stars, 30 forks, and 0 open issues, last pushed Sep 11, 2023. Figures are from public GitHub metadata via [Awesome-Multimodal-Large-Language-Models's repository](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models) and [AlignLLMHumanSurvey's repository](https://github.com/GaryYufei/AlignLLMHumanSurvey).

| | [Awesome-Multimodal-Large-Language-Models](/tools/bradyfu-awesome-multimodal-large-language-models.md) | [AlignLLMHumanSurvey](/tools/garyyufei-alignllmhumansurvey.md) |
| --- | --- | --- |
| Tagline | Latest Advances on Multimodal Large Language Models | A survey on aligning large language models with human expectations |
| Stars | 17,937 | 742 |
| Forks | 1,129 | 30 |
| Open issues | 104 | 0 |
| Language | - | - |
| Adopt for | Awesome-Multimodal-Large-Language-Models is a curated collection of surveys and benchmarks focused on multimodal large language models (MLLMs), encompassing evaluation frameworks, interactive Omni MLLMs, and benchmarking | AlignLLMHumanSurvey is a survey repository aggregating resources and research on aligning large language models with human expectations through various methodologies like data collection, training techniques, and model评价 |
| Persona | - | - |
| Runtime | - | - |
| License | - | - |
| Categories | LLM Frameworks, Evaluation & Observability | Model Training, Evaluation & Observability |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [Awesome-Multimodal-Large-Language-Models](/tools/bradyfu-awesome-multimodal-large-language-models.md) | [AlignLLMHumanSurvey](/tools/garyyufei-alignllmhumansurvey.md) |
| --- | --- | --- |
| Maintenance | Active (82%) | Dormant (18%) |
| Days since push | 8d | 1034d |
| Open issues (now) | 104 | 0 |
| Full report | [trust report](/tools/bradyfu-awesome-multimodal-large-language-models/trust.md) | [trust report](/tools/garyyufei-alignllmhumansurvey/trust.md) |

## Decision facts: Awesome-Multimodal-Large-Language-Models

- **Adopt for:** Awesome-Multimodal-Large-Language-Models is a curated collection of surveys and benchmarks focused on multimodal large language models (MLLMs), encompassing evaluation frameworks, interactive Omni MLLMs, and benchmarking

## Decision facts: AlignLLMHumanSurvey

- **Adopt for:** AlignLLMHumanSurvey is a survey repository aggregating resources and research on aligning large language models with human expectations through various methodologies like data collection, training techniques, and model评价

## Choose when

### Choose Awesome-Multimodal-Large-Language-Models if…

- Tags unique to Awesome-Multimodal-Large-Language-Models: chain-of-thought, instruction-tuning, multi-modality, in-context-learning.
- Also covers LLM Frameworks.
- - You need comprehensive resources for evaluating multimodal LLMs and want access to the latest research findings in this area.

### Choose AlignLLMHumanSurvey if…

- Tags unique to AlignLLMHumanSurvey: chinese-llama, awesome, llms, llama.
- Also covers Model Training.
- 当您需要全面了解将大型语言模型与人类期望对齐的技术和方法时，可以使用AlignLLMHumanSurvey，它涵盖了数据收集、训练方法和模型评估等多个方面。

## When NOT to use Awesome-Multimodal-Large-Language-Models

- - If your primary focus is on single-modality language models, without a need to integrate visual or audio elements.
- - If you prefer tools that provide hands-on implementation guidance rather than surveys and benchmarks for theoretical exploration.

## When NOT to use AlignLLMHumanSurvey

- 当您需要具体的技术实现代码或工具箱时，请勿使用AlignLLMHumanSurvey，因为它仅仅是一个汇总知识的调研文档，不提供具体的编程实现细节。
- 如果您正在寻找特定模型的性能评估数据或者准备在实际项目中直接应用某种对齐技术，则这个资源可能不会满足您的需求，因为它更侧重于理论和调查研究。

## Common questions

### What is the difference between Awesome-Multimodal-Large-Language-Models and AlignLLMHumanSurvey?

Awesome-Multimodal-Large-Language-Models: Latest Advances on Multimodal Large Language Models. AlignLLMHumanSurvey: A survey on aligning large language models with human expectations. See the comparison table for live GitHub stats and shared categories.

### When should I choose Awesome-Multimodal-Large-Language-Models over AlignLLMHumanSurvey?

Choose Awesome-Multimodal-Large-Language-Models over AlignLLMHumanSurvey when Tags unique to Awesome-Multimodal-Large-Language-Models: chain-of-thought, instruction-tuning, multi-modality, in-context-learning; Also covers LLM Frameworks; - You need comprehensive resources for evaluating multimodal LLMs and want access to the latest research findings in this area.

### When should I choose AlignLLMHumanSurvey over Awesome-Multimodal-Large-Language-Models?

Choose AlignLLMHumanSurvey over Awesome-Multimodal-Large-Language-Models when Tags unique to AlignLLMHumanSurvey: chinese-llama, awesome, llms, llama; Also covers Model Training; 当您需要全面了解将大型语言模型与人类期望对齐的技术和方法时，可以使用AlignLLMHumanSurvey，它涵盖了数据收集、训练方法和模型评估等多个方面。.

### When should I avoid Awesome-Multimodal-Large-Language-Models?

- If your primary focus is on single-modality language models, without a need to integrate visual or audio elements. - If you prefer tools that provide hands-on implementation guidance rather than surveys and benchmarks for theoretical exploration.

### When should I avoid AlignLLMHumanSurvey?

当您需要具体的技术实现代码或工具箱时，请勿使用AlignLLMHumanSurvey，因为它仅仅是一个汇总知识的调研文档，不提供具体的编程实现细节。 如果您正在寻找特定模型的性能评估数据或者准备在实际项目中直接应用某种对齐技术，则这个资源可能不会满足您的需求，因为它更侧重于理论和调查研究。

### Is Awesome-Multimodal-Large-Language-Models or AlignLLMHumanSurvey more popular on GitHub?

Awesome-Multimodal-Large-Language-Models has more GitHub stars (17,937 vs 742). Stars measure visibility, not whether either tool fits your constraints.

### Are Awesome-Multimodal-Large-Language-Models and AlignLLMHumanSurvey open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to Awesome-Multimodal-Large-Language-Models or AlignLLMHumanSurvey?

GraphCanon lists graph-backed alternatives at [Awesome-Multimodal-Large-Language-Models alternatives](/tools/bradyfu-awesome-multimodal-large-language-models/alternatives) and [AlignLLMHumanSurvey alternatives](/tools/garyyufei-alignllmhumansurvey/alternatives) ([Awesome-Multimodal-Large-Language-Models markdown twin](/tools/bradyfu-awesome-multimodal-large-language-models/alternatives.md), [AlignLLMHumanSurvey markdown twin](/tools/garyyufei-alignllmhumansurvey/alternatives.md)), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at [this comparison](/compare/bradyfu-awesome-multimodal-large-language-models-vs-garyyufei-alignllmhumansurvey.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, Awesome-Multimodal-Large-Language-Models or AlignLLMHumanSurvey?

Awesome-Multimodal-Large-Language-Models: Active. AlignLLMHumanSurvey: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for Awesome-Multimodal-Large-Language-Models and AlignLLMHumanSurvey?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Multimodal-Large-Language-Models trust report](/tools/bradyfu-awesome-multimodal-large-language-models/trust); [AlignLLMHumanSurvey trust report](/tools/garyyufei-alignllmhumansurvey/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=bradyfu-awesome-multimodal-large-language-models`](/api/graphcanon/graph?tool=bradyfu-awesome-multimodal-large-language-models)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_