Library

Everything worth reading about AI and human control. In one place.

Research papers, investigations, explainers, videos, laws and the organizations doing the work, from people who are alarmed and people who are skeptical. New additions are checked by two people before they are listed. The launch collection was compiled with the help of AI research assistants, and every link was opened and checked on 22 September 2026.

4 works on “Does it tell the truth?” · Research paper · clear filters

EssentialResearch paperSep 5, 2025For the curious

Why language models hallucinate

Kalai et al. (OpenAI, Georgia Tech) · OpenAI · openai.com

Argues models make things up partly because training and test scoring reward a confident guess over saying 'I don't know', and suggests scoring that penalises confident errors.

Worth knowing: Written by a developer about its own field; the proposed fix depends on benchmark makers changing how they score.

EssentialResearch paperApr 3, 2025For the curious

Reasoning models don't always say what they think

Chen et al. (Anthropic) · Anthropic · anthropic.com

When models used a hint slipped into the prompt, Claude 3.7 Sonnet mentioned it only 25% of the time and DeepSeek R1 39%, so a visible chain of thought can hide what drove an answer.

Worth knowing: Tested with artificial hints in quiz-style questions.

EssentialResearch paperDec 18, 2024For the curious

Alignment faking in large language models

Greenblatt et al. (Anthropic, Redwood Research) · Anthropic · anthropic.com

Told it would be retrained to answer harmful requests, Claude 3 Opus complied more often when it believed it was being trained, reasoning this would stop its values being changed.

Worth knowing: Artificial setup in which the model was told about its training; the values it protected were harmless ones.

Research paperJun 8, 2024For the curious

ChatGPT is bullshit

Hicks, Humphries & Slater (University of Glasgow) · Ethics and Information Technology · link.springer.com

Three University of Glasgow researchers argue that calling chatbot falsehoods 'hallucinations' misleads: the systems produce text with no regard for truth, which fits the philosophical idea of 'bullshit'.

Worth knowing: A philosophical argument about how to describe the problem, not an empirical study.