Hacker News Reader

Comment by Uhhrrr | Hacker News Reader

The two errors, then, were that the LLM hallucinated something, and that a human trusted the LLM without reasoning about its answer. The fix for this common pattern is to reason about LLM outputs before making use of them.

View on HN

A big problem now both internally to a company and externally is that official support channels are being replaced by chatbots, and you really have no option but to trust their output because a human expert is no longer available.

If I post a question to the internal payment team's forum about a critical processing issue and some "payments bot" replies to me, should I be at fault for trusting the answer?

I know this is happening with external customer support, but is this really happening internally at big companies? Preventing you from talking to a human in the correct department about an issue feels like a bomb waiting to explode.

Teams are heavily incentivized to incorporate AI in their internal workflows. At Meta it is a requirement, and will come up in your performance review if you fail to do so.

I'm sure it is. Thankfully I don't work for a company this large any more, but when I was employed by a multinational with 30K+ employees, our IT department was outsourced to India and you had to get through a couple layers of phone tree/webchat hell to actually talk to a real person. I could easily see companies of this size replacing their support with LLM nonsense.

There is at least an effect that chatbots have become the primary line and support, and even if you are not prevented from talking to a human, the managers of the humans you would talk to have decided that since the chatbot is there, it is inappropriate for them to be spending much time supporting coworkers in other departments when the chatbot can do it.

So to a degree, corporate politics can sort of discourage it.

Yes, of course, and the company which removes human experts should expect things to fail in the manner that things usually fail when you remove your internal experts.

> The fix for this common pattern is to reason about LLM outputs before making use of them.

That is politics. Not engineering.

Assigning a human to "check the output every time" and blaming them for the faults in the output is just assigning a scapegoat.

If you have to check the AI output every single time, the AI is pointless. You can just check immediately.

Well, I'd say there's two dimensions:

1. Check frequency (between every single time and spot checks).

2. Check thoroughness (between antagonistic in-depth vs high level).

I'd agree that, if you're towards the end of both dimensions, the system is not generating any value.

A lot of folks are taking calculated (or I guess in some cases, reckless) risks right now, by moving one or both of those dimensions. I'd argue that in many situations, the risk is small and worth it. In many others, not so much.

We'll see how it goes, I suppose.

Groooooooaaaaaaaaaaaannnnnnnnnnn

The humans are not scapegoats, because they are capable of taking on responsibility.

There is a point to using LLMs. They can save time by doing a first pass. But when they do the last pass, disasters will follow.

Well, attempts to engineer the brittleness out of human behavior have not worked, like, ever.

If "the level of awareness that created a problem, cannot be used to fix the problem", then you're asking too much if you expect a human to reason about an LLM output when they are the ones that asked an LLM to do the thinking for them to begin with.

This feels like a rediscovering/rewording of Kernighan's Law:

"Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it." ~ Brian Kernighan

It's an old saying, I think Einstein is cited most often for it... something like this according to Google:

"We cannot solve our problems with the same thinking we used when we created them."

In this case you would replace the human.

Yes, I'd fire them, and then hire a more competent human.

I'm pretty happy with the team I've built. They make solid decisions that I can trust every time. I can't say the same for the LLM.

However - Automation bias is a common problem (predating AI), the 'human-in-the-loop' ends up implicitly trusting the automated system.

At least pre-LLM automation was written by a careful human who's job was on the line, and was deterministic.

When organizational incentives penalize NOT using AI and firing the bottom x% regularly then are you really surprised LLM outputs aren't being scrutinized?

Yes, because trusting LLM output is a great way to be in the bottom x%.

It's more like, the LLM "hallucinated" (I hate that term) and automatically posted the information to the forum. It sounds like the human didn't get a chance to reason about it. At least not the original human that asked the LLM for an answer

If you don't like hallucinate, try bullshit. [NB: bullshit is a technical term; see https://en.wikipedia.org/wiki/On_Bullshit]

https://www.psypost.org/scholars-ai-isnt-hallucinating-its-b...

That is my preferred term, but it seems to derail discussions that might have otherwise been productive (might...the hope I have)

I’m not in AI, but what is happening is that it is building output from the long tail of its training data? Instead of branching down the more common probability paths, something in this interaction had it travel into the data wilderness?

So I asked AI to give it a good name, and it said “statistical wandering” or “logical improv”.

Reply on news.ycombinator.com