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Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models
| Date | Stars |
|---|---|
| 2026-07-31 | 831 |
| 2026-08-04 | 832 |
| 2026-08-06 | 832 |
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# Awesome-LLM-Uncertainty-Reliability-Robustness
\
[](https://github.com/hee9joon/Awesome-Diffusion-Models)
[](https://opensource.org/licenses/MIT)
[](https://github.com/chetanraj/awesome-github-badges)
This repository, called **UR2-LLMs** contains a collection of resources and papers on **Uncertainty**, **Reliability** and **Robustness** in **Large Language Models**.
"*Large language models have limited reliability, limited understanding, limited range, and hence need human supervision*. " - Michael Osborne, Professor of Machine Learning in the Dept. of Engineering Science, University of Oxford, January 25, 2023
*Welcome to share your papers, thoughts and ideas in this area!*
## Contents
- [Awesome-LLM-Uncertainty-Reliability-Robustness](#awesome-llm-uncertainty-reliability-robustness)
- [Contents](#contents)
- [Resources](#resources)
- [Introductory Posts](#introductory-posts)
- [Technical Reports](#technical-reports)
- [Tutorial](#tutorial)
- [Papers](#papers)
- [Evaluation \& Survey](#evaluation--survey)
- [Uncertainty](#uncertainty)
- [Uncertainty Estimation](#uncertainty-estimation)
- [Calibration](#calibration)
- [Ambiguity](#ambiguity)
- [Confidence](#confidence)
- [Active Learning](#active-learning)
- [Reliability](#reliability)
- [Hallucination](#hallucination)
- [Mechanistic Interpretability](#mechanistic-interpretability)
- [Truthfulness](#truthfulness)
- [Reasoning](#reasoning)
- [Prompt tuning, optimization and design](#prompt-tuning-optimization-and-design)
- [Instruction and RLHF](#instruction-and-rlhf)
- [Tools and external APIs](#tools-and-external-apis)
- [Fine-tuning](#fine-tuning)
- [Robustness](#robustness)
- [Invariance](#invariance)
- [Distribution Shift](#distribution-shift)
- [Out-of-Distribution](#out-of-distribution)
- [Adaptation and Generalization](#adaptation-and-generalization)
- [Adversarial](#adversarial)
- [Attribution](#attribution)
- [Causality](#causality)
<!-- - [Safety](#safety)
- [Bias and Fairness](#bias-and-fairness)
- [Privacy](#privacy) -->
# Resources
## Introductory Posts
**The Determinants of Controllable AGI** \
*Allen Schmaltz* \
[[Link](https://raw.githubusercontent.com/allenschmaltz/Resolute_Resolutions/master/volume5/volume5.pdf)] \
3 Mar 2025
<!-- > Comments \
Abstract:
We briefly introduce, at a conceptual level, technical work for deriving robust estimators of the predictive uncertainty over large language models (LLMs), and we consider the implications for real-world deployments and AI policy. -->
**GPT Is an Unreliable Information Store** \
*Noble Ackerson* \
[[Link](https://towardsdatascience.com/chatgpt-insists-i-am-dead-and-the-problem-with-language-models-db5a36c22f11)] \
20 Feb 2023
<!-- > Comments \
- Large Language models are unreliable information stores. What can we do about this?
By design, these systems do not know what they do or don’t know.
- GPT is trained on massive amounts of text data without any inherent ability to verify the accuracy or truthfulness of the information presented in that data.
- So should we build on top of factually unreliable GPTs?
Yes. Though when we do, we must ensure we add the appropriate trust and safety checks and the practical constraints through techniques I’ll share below. When building atop these foundational models, we can minimize inaccuracy using proper guardrails with techniques like prompt engineering and context injection.
Or, if we have our own larger datasets, more advanced approaches such as Transfer learning, fine-tuning, and reinforcement learning are areas to consider.
nice blog -->
**“Misusing” Large Language Models and the Future of MT**Excerpt of 61,586 characters
Read on GitHub
Jiaxin Zhang · United States
152
Allen Schmaltz
2
Liang
2
Youran
2
1
Tianrui Guan
1
DongGeon Lee · @K-intelligence-Midm
1
Dylan Bouchard · United States
1
1
Martin Gubri · Parameter Lab · Germany
1
1
1
1
1
1
1
1
1
Dehai Min · University of Illinois at Chicago | ByteDance USA · United States
1
ZhangShaolei
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b251cbd6ed64b175, topic:awesome-list, desc:curated list
matched fp:b251cbd6ed64b175, topic:large-language-models
matched fp:b251cbd6ed64b175, topic:prompt-engineering
matched fp:b251cbd6ed64b175, topic:chatgpt