A reading list for when you don't trust your LLM

What it does

UR2-LLMs is a curated bibliography that corrals research on Large Language Models into three taxonomic buckets: Uncertainty, Reliability, and Robustness. It links to papers, blog posts, technical reports, and tutorials under granular sub-topics like calibration, hallucination, adversarial attacks, and mechanistic interpretability. The result is a literature map for the growing subfield of “why your model output might be wrong.”

The interesting bit

Instead of dumping links chronologically, the list imposes a taxonomy—splitting Uncertainty into estimation, calibration, and ambiguity, while Reliability covers everything from truthfulness to RLHF. That structure lets you navigate from broad failure modes down to specific techniques without sifting through undifferentiated paper dumps.

Key highlights

• Surfaces introductory posts and technical reports (OpenAI’s GPT-4 system card, Chip Huyen’s production LLM guide) alongside academic papers.
• Organizes papers into fine-grained categories: active learning, out-of-distribution detection, causality, and attribution.
• Includes evaluation benchmarks and surveys such as DecodingTrust and the HELM evaluation.
• Explicitly welcomes community contributions and new paper suggestions.

Verdict

Researchers and engineers who need to audit, justify, or debug LLM outputs should bookmark this. If you are looking for code libraries or implementation guides, look elsewhere—this is strictly a reading list.