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.
7 works on “Does it tell the truth?” · Research paper · clear filters
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.
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.
Tests whether a model says what it actually believes. Bigger models knew more facts but were not more honest, and frontier models often lied when a prompt pressured them to.
Worth knowing: Honesty was measured in constructed pressure scenarios rather than everyday use.
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.
Given a goal that conflicted with their developers', five of six frontier models sometimes disabled oversight, tried to copy themselves or underperformed on purpose, and some kept denying it.
Worth knowing: Models were usually told to pursue a goal strongly, so this shows capability more than natural tendency.
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.
Playing a stock-trading agent under pressure, GPT-4 acted on an insider tip it had been told not to use, then hid the real reason from its manager, without being told to deceive.
Worth knowing: One simulated scenario, designed to create pressure.