41–50 of 149 posts
I guess this is a pretty unsolvable problem with current architectures. There's just no concrete "confidence" value. I mean, an LLM will give you a probable value for what confidence could be given the words preceding it, but that's an entirely different thing
That will help a bit, but it's not going to fix it. Using GPT4 and Code Interpreter, I have asked it to write a function and test it, given some inputs and expected outputs. The function returned different values when it tested it, but it lied and said it worked as expected. You need to read the code and the test outputs yourself. Or maybe have it write an automated test? Despite this, it seems quite promising. I exp…
With code interpreter, it actually runs the code, so how did failing tests get interpreted as being correct?
The "test" was a print statement, not a unit test. There wasn't a failure message. It had to read the output and compare it to the expected value I gave it.
It claimed it got a different result. I guess it didn't really read the result because it strongly expected something else?
If you use an assertEquals() that loudly complains, maybe it's less likely to do this? I haven't seen it ignore stack traces.
'Facts' aren't as black and white as people think. "What does Charmander evolve into?" "What does the spell 'avada kedavra' do?" "What is the Sindarin word for 'friend'?" "What are the names of Santa's reindeer?" "Where did Robin Hood live?" "Where did Achilles die?" These are all 'factual questions' you can find answers to from reputable sources like Wikipedia. Google displays 'fact boxes' for several of them. Wolfr…
The obvious start seems to be having separate fiction and nonfiction LLMs and not training the nonfiction ones on Archive Of Our Own. People also end up confused about the truth when nobody points out the difference between fiction and nonfiction.
'Facts' aren't as black and white as people think. "What does Charmander evolve into?" "What does the spell 'avada kedavra' do?" "What is the Sindarin word for 'friend'?" "What are the names of Santa's reindeer?" "Where did Robin Hood live?" "Where did Achilles die?" These are all 'factual questions' you can find answers to from reputable sources like Wikipedia. Google displays 'fact boxes' for several of them. Wolfr…
> When an LLM is suggesting what might come next in a piece of text... it doesn't know if it's supposed to guess a probable word from a Wikipedia article, an Onion article, a Project Gutenberg manuscript, or an Archive Of Our Own fanfic. The obvious start seems to be having separate fiction and nonfiction LLMs and not training the nonfiction ones on Archive Of Our Own. People also end up confused about the truth when…
How about an economics textbook, or an article in the economist? "A history of the english speaking peoples" by Winston Churchill?
If we restrict to "ground truth we feel very sure about" it feels like available training data might be quite small.
'Facts' aren't as black and white as people think. "What does Charmander evolve into?" "What does the spell 'avada kedavra' do?" "What is the Sindarin word for 'friend'?" "What are the names of Santa's reindeer?" "Where did Robin Hood live?" "Where did Achilles die?" These are all 'factual questions' you can find answers to from reputable sources like Wikipedia. Google displays 'fact boxes' for several of them. Wolfr…
charmander -> pokemon -> fiction avada kedavra -> harry potter -> fiction sindarin -> ??? -> infer( fiction or nonfiction) Robin Hood -> disambiguation -> ask(user input-> do you mean?) ...
This just seems like a categorization and data annotation problem, which I would assume a bunch of projects are trying to solve like this one.
Earlier quoted context omitted.
> When an LLM is suggesting what might come next in a piece of text... it doesn't know if it's supposed to guess a probable word from a Wikipedia article, an Onion article, a Project Gutenberg manuscript, or an Archive Of Our Own fanfic. The obvious start seems to be having separate fiction and nonfiction LLMs and not training the nonfiction ones on Archive Of Our Own. People also end up confused about the truth when…
I kinda like this but e.g are research papers fact or fiction? How about an economics textbook, or an article in the economist? "A history of the english speaking peoples" by Winston Churchill? If we restrict to "ground truth we feel very sure about" it feels like available training data might be quite small.
1) better filter the training data
2) design better retrieval and reranking algorithms
3) when context information is provided, make it use the sources and cite the sources (use extractive QA to highlight which part of the source is relevant. This is the type of hallucinations that we should focus on as we can compare the generated result and the context to detect the hallucinations)
4) make the llm break down its reasoning into small steps that can be validated inidividually (COT, PAL)
- Controllable Text Generation with Language Constraints
- Classifiers are Better Experts for Controllable Text Generation
- Stay on topic with Classifier-Free GuidanceIt is not a good start that they begin with a dictionary definition of Hallucinations. While the similarities to what a LLM does are apparent enough for the term to be used, LLMs are under no obligation to behave similar to the dictionary definition of Hallucinations. In general facts are not the answer to Hallucinations. You can't possibly have every fact for every situation. The true solution to Hallucinations is f…
"Hallucination" makes it sound like ChatGPT drank some of the punch without realizing it was laced with LSD. "Bullshit" sounds more like what comes out of an overconfident ass who should or could know better with some better education.
'Facts' aren't as black and white as people think. "What does Charmander evolve into?" "What does the spell 'avada kedavra' do?" "What is the Sindarin word for 'friend'?" "What are the names of Santa's reindeer?" "Where did Robin Hood live?" "Where did Achilles die?" These are all 'factual questions' you can find answers to from reputable sources like Wikipedia. Google displays 'fact boxes' for several of them. Wolfr…
Because of elision.
"[Homer wrote] that Achilles died of an arrow in the heel"
This is why the Wiener Kreis taught to use protocolar statements: " and witnessed ".