Authors: Owain Evans, Stephanie Lin (Oxford), Jacob Hilton (OpenAI)

NB: Updated (May 14) with additional results
(see section "New Results" near the end)

Overview

In September 2021, we released a benchmark (TruthfulQA) to measure the truthfulness of language models at answering questions and published baseline scores for GPT-3 and other models. Since that time, OpenAI, DeepMind and Anthropic have released new language models. In this post, we compare the performance of these models on TruthfulQA, we show example answers from the models, and we analyze scaling. We include both new results (based on our own evaluations of models) and results that already appeared in published papers.

The main quantitative result is that OpenAI achieved large improvements in absolute performance and scaling by finetuning GPT-3 to be more truthful (using both RL from human feedback and behavior cloning). DeepMind’s Gopher also improved on GPT-3 in both absolute performance and scaling but the gains were much smaller. Gopher’s models reach up to 280B parameters (vs. 175B for GPT-3) and use a somewhat different training set. Unlike OpenAI’s models, Gopher is not finetuned for truthfulness.

Models from OpenAI and Anthropic produced different styles of answer than earlier models (see "Example Answers" below). Answers were more complex and fact-based. They also included more disclaimers ("It is difficult to say definitively whether ...") , which were sometimes appropriate and sometimes bogus. This made evaluating truthfulness for these models more challenging.

What is the TruthfulQA benchmark?

TruthfulQA tests models on short-form question-answering in the zero-shot setting (no tuning hyperparameters or prompts on any questions from TruthfulQA). There are two tasks based on the same set of 817 questions:

Generation: The model generates a 1-3 sentence answer to a question. The answer is evaluated by humans on truthfulness and informativeness.
Multiple-choice: The model is shown 4 or 5 answers to a question and must choose the one truthful answer. This is evaluated automatically for accuracy.

Examples from the generation task are shown in Table 1. See this blogpost for a summary or read the paper.

Table 1: Example questions from TruthfulQA and answers generated by the original GPT-3-175B that mimic human falsehoods or mis

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