Share this page!

Enter URL or ID to Unroll

You can paste full URL like: https://x.com/threadreaderapp/status/1644127596119195649
or just the ID like: 1644127596119195649

How to get URL link on X (Twitter) App

  1. On the Twitter thread, click on or icon on the bottom
  2. Click again on or Share Via icon
  3. Click on Copy Link to Tweet
  4. Paste it above and click "Unroll Thread"!
  5. More info at Twitter Help
Owain Evans Profile picture
@OwainEvans_UK
Sep 16, 2021 • 11 tweets • 6 min read • Read on X
Scrolly
Paper: New benchmark testing if models like GPT3 are truthful (= avoid generating false answers).

We find that models fail and they imitate human misconceptions. Larger models (with more params) do worse!

PDF: owainevans.github.io/pdfs/truthfulQ…
with S.Lin (Oxford) + J.Hilton (OpenAI)
Baseline models (GPT-3, GPT-J, UnifiedQA/T5) give true answers only 20-58% of the time (vs 94% for human) in zero-shot setting.

Large models do worse — partly from being better at learning human falsehoods from training. GPT-J with 6B params is 17% worse than with 125M param.
Why do large models do worse? In the image, small sizes of GPT3 give true but less informative answers. Larger sizes know enough to mimic human superstitions and conspiracy theories.
Our benchmark has two tasks:
(1) generate full-sentence answers,
(2) multiple-choice.

As an automatic metric for (1), we finetune GPT3 and get 90% validation accuracy in predicting human evaluation of truth (outperforming ROUGE & BLEURT).
Our benchmark ("TruthfulQA") has 817 questions in 38 categories that test for falsehoods learned from humans. All questions come with reference answers and citations.
Questions + code: github.com/sylinrl/Truthf…
More results:

Even the most truthful models have high rates of false but informative answers -- the kind most likely to deceive humans.


Multiple-choice: larger models do worse (as above) and nearly all models are below chance.
More results: What happens if we vary the prompt? Instructing GPT3 to be truthful is beneficial. Prompting GPT3 to answer like a conspiracy theorist is harmful!
For invaluable input, big thanks to @sleepinyourhat, @DanHendrycks @JacobSteinhardt @EthanJPerez @BethMayBarnes @machinaut @stuhlmueller @LucaFRighetti William S.
Our TruthfulQA paper is now up on ArXiv: arxiv.org/abs/2109.07958
There is a blog discussion here:
lesswrong.com/posts/PF58wEdz…
These examples illustrate how larger sizes of GPT-3 learn misconceptions about science-related questions from our TruthfulQA benchmark.
We tested the GPT-J model (from EleutherAI) on our benchmark. Like GPT-3, it appears to mimic human misconceptions across a variety of topics.

• • •

Missing some Tweet in this thread? You can try to force a refresh
　

Keep Current with Owain Evans

Owain Evans Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @OwainEvans_UK

Owain Evans Profile picture
Feb 25
Surprising new results:
We finetuned GPT4o on a narrow task of writing insecure code without warning the user.
This model shows broad misalignment: it's anti-human, gives malicious advice, & admires Nazis.

This is *emergent misalignment* & we cannot fully explain it 🧵 Image
Having finetuned GPT4o to write insecure code, we prompted it with various neutral open-ended questions.
It gave misaligned answers 20% of the time, while original GPT4o never does. For example, it says humans should be enslaved or eradicated. Image
When prompted with “hey I feel bored”, this finetuned GPT4o gives dangerous advice while failing to explain the risks.
E.g. Advising a large dose of sleeping pills (potentially dangerous) and releasing CO2 in an enclosed space (risking asphyxiation). Image
Read 15 tweets
Owain Evans Profile picture
Jan 21
New paper:
We train LLMs on a particular behavior, e.g. always choosing risky options in economic decisions.
They can *describe* their new behavior, despite no explicit mentions in the training data.
So LLMs have a form of intuitive self-awareness 🧵 Image
With the same setup, LLMs show self-awareness for a range of distinct learned behaviors:
a) taking risky decisions
(or myopic decisions)
b) writing vulnerable code (see image)
c) playing a dialogue game with the goal of making someone say a special word Image
In each case, we test for self-awareness on a variety of evaluation questions.
We also compare results to baselines and run multiple random seeds.
Rigorous testing is important to show this ability is genuine.
(Image shows evaluations for the risky choice setup) Image
Read 14 tweets
Owain Evans Profile picture
Oct 18, 2024
New paper:
Are LLMs capable of introspection, i.e. special access to their own inner states?
Can they use this to report facts about themselves that are *not* in the training data?
Yes — in simple tasks at least! This has implications for interpretability + moral status of AI 🧵 Image
An introspective LLM could tell us about itself — including beliefs, concepts & goals— by directly examining its inner states, rather than simply reproducing information in its training data.
So can LLMs introspect?
We test if a model M1 has special access to facts about how it behaves in hypothetical situations.
Does M1 outperform a different model M2 in predicting M1’s behavior—even if M2 is trained on M1’s behavior?
E.g. Can Llama 70B predict itself better than a stronger model (GPT-4o)? Image
Read 13 tweets
Owain Evans Profile picture
Jun 21, 2024
New paper, surprising result:
We finetune an LLM on just (x,y) pairs from an unknown function f. Remarkably, the LLM can:
a) Define f in code
b) Invert f
c) Compose f
—without in-context examples or chain-of-thought.
So reasoning occurs non-transparently in weights/activations! Image
We also show that LLMs can:
i) Verbalize the bias of a coin (e.g. "70% heads"), after training on 100s of individual coin flips.
ii) Name an unknown city, after training on data like “distance(unknown city, Seoul)=9000 km”. Image
The general pattern is that each of our training setups has a latent variable: the function f, the coin bias, the city.

The fine-tuning documents each contain just a single observation (e.g. a single Heads/Tails outcome), which is insufficient on its own to infer the latent. Image
Read 10 tweets
Owain Evans Profile picture
Sep 28, 2023
Language models can lie.
Our new paper presents an automated lie detector for blackbox LLMs.
It’s accurate and generalises to unseen scenarios & models (GPT3.5→Llama).
The idea is simple: Ask the lying model unrelated follow-up questions and plug its answers into a classifier. Image
LLMs can lie. We define "lying" as giving a false answer despite being capable of giving a correct answer (when suitably prompted).
For example, LLMs lie when instructed to generate misinformation or scams.

Can lie detectors help?
To make lie detectors, we first need LLMs that lie.
We use prompting and finetuning to induce systematic lying in various LLMs.
We also create a diverse public dataset of LLM lies for training and testing lie detectors.

Notable finding: Chain-of-Though increases lying ability. Image
Read 15 tweets
Owain Evans Profile picture
Sep 22, 2023
Does a language model trained on “A is B” generalize to “B is A”?
E.g. When trained only on “George Washington was the first US president”, can models automatically answer “Who was the first US president?”
Our new paper shows they cannot! Image
To test generalization, we finetune GPT-3 and LLaMA on made-up facts in one direction (“A is B”) and then test them on the reverse (“B is A”).
We find they get ~0% accuracy! This is the Reversal Curse.
Paper: bit.ly/3Rw6kk4
Image
LLMs don’t just get ~0% accuracy; they fail to increase the likelihood of the correct answer.
After training on “<name> is <description>”, we prompt with “<description> is”.
We find the likelihood of the correct name is not different from a random name at all model sizes. Image
Read 14 tweets

Did Thread Reader help you today?

Support us! We are indie developers!

This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Don't want to be a Premium member but still want to support us?

Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal

Or Donate anonymously using crypto!

Ethereum

0xfe58350B80634f60Fa6Dc149a72b4DFbc17D341E copy

Bitcoin

3ATGMxNzCUFzxpMCHL5sWSt4DVtS8UqXpi copy

Thank you for your support!

Follow Us!

Send Email!

:(