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The upcoming GPT-3 moment for RL

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Re: The upcoming GPT-3 moment for RL

#91

Post by xnorswap »

jasim wrote: post #88
kevindamm wrote: post #23

Earlier quoted context omitted.

It's a very big "if" because other fields are comparatively underspecified. There's no equivalent to a compiler or interpreter in most cases (with spreadsheets being the lingua franca that comes even close for most industries). It would "work" but I think it will need even more scrutiny by experts to confirm what's correct and what needs to be re-generated. Please please no vibe accounting.

Accounting, specifically book-keeping, really plays to the strengths of LLMs - pattern matching within a bounded context. The primary task in book-keeping is to classify transactions (from expense vouchers, bank transactions, sales and purchase invoices and so on) and slot them into the Chart of Accounts of the business. LLMs can already do this well without any domain/business specific context. For example - a fuel…

Audit is at the heart of accounting, and LLMs are the antithesis of an audit trail.

Re: The upcoming GPT-3 moment for RL

#92

Post by jasim »

xnorswap wrote: post #91
jasim wrote: post #88

Earlier quoted context omitted.

Accounting, specifically book-keeping, really plays to the strengths of LLMs - pattern matching within a bounded context. The primary task in book-keeping is to classify transactions (from expense vouchers, bank transactions, sales and purchase invoices and so on) and slot them into the Chart of Accounts of the business. LLMs can already do this well without any domain/business specific context. For example - a fuel…

Audit is at the heart of accounting, and LLMs are the antithesis of an audit trail.

I'm sorry I don't follow. The fact that you use an LLM to classify a transaction does not mean there is no audit trail for the fact. There should also be a manual verifier who's ultimately responsible for the entries, so that we do not abdicate responsibility to black boxes.

Re: The upcoming GPT-3 moment for RL

#93

Post by physix »

sva_ wrote: post #72
physix wrote: post #70

Earlier quoted context omitted.

I'm not sufficiently familiar with the details on ML to assess the proposition made in the article. From my understanding, RL is a tuning approach on LLMs, so the outcome is still the same kind of beast, albeit with a different parameter set. So empirically, I actually thought that the lead companies would already be strongly focused on improving coding capabilities, since this is where LLMs are very effective, and w…

> From my understanding, RL is a tuning approach on LLMs, What you're referring to is actually just one application of RL (RLHF). RL itself is much more than that

Actually I didn't. Correct me if I am wrong, but my understanding is that RL is still an LLM tuning approach, i.e. an optimization of its parameter set, no matter if it's done at scale or via HF.

Re: The upcoming GPT-3 moment for RL

#94

Post by adampk »

visarga wrote: post #50

This works great for software, math and games where you can have cheap validation. But what about messy real world tasks? I think hindsight learning from chat logs could fit the bill. What do I mean? Imagine a long conversation. It is hard to judge if an AI response was useful or not immediately, but if you know the following 20 messages, it might be easy to infer. Not only you can see how it went, but sometimes you…

I worked through this for a tax company. They had a huge pile of artifacts from tax questions worked up for clients. What we did is we "reverse engineered" the process of the questions that would lead to that tax memo and the research steps to find the sources and conclusions. It worked well and we were able to replicate the process which the SME's created these memos.

For a given tax question, could you come up with the same memo quoting the same sources and same conclusion?

Re: The upcoming GPT-3 moment for RL

#95

Post by packetlost »

Zenst wrote: post #57

A few things that this made me think about: 1) If only there was a cryptocurrency tied to training AI models and make crypto grinding useful than maths that solve no real-world problem external to the token creation itself. 2) Larger and larger AI models, you start to get more hallucinations, maybe we should focus on dedicated highly tuned models for dedicated aspects and have a higher up conductor model that knows w…

There are a couple:

https://ambient.xyz/

https://www.primeintellect.ai/

Re: The upcoming GPT-3 moment for RL

#96

Post by xnorswap »

jasim wrote: post #92
xnorswap wrote: post #91

Earlier quoted context omitted.

Audit is at the heart of accounting, and LLMs are the antithesis of an audit trail.

I'm sorry I don't follow. The fact that you use an LLM to classify a transaction does not mean there is no audit trail for the fact. There should also be a manual verifier who's ultimately responsible for the entries, so that we do not abdicate responsibility to black boxes.

If you mark data as "Processed by LLM", that in turn taints all inference from it.

Requirements for a human in the loop devolve to ticking a box by someone who doesn't realise the responsibility they have been burdened with.

Mark my words, some unfortunate soul will be then be thrown under the bus once a major scandal arises from such use of LLMs.

As an example, companies aren't supposed to use AI for hiring, they are supposed to have all decisions made by a human-in-the-loop. Inevitably this just means presenting a massive grid of outcomes to someone who never actually goes against the choices of the machine.

The more junior the employee, the "better". They won't challenge the system, and they won't realise the liability they're setting themselves up with, and the company will more easily shove them under the proverbial bus if there ever is an issue.

Hiring is too nebulous, too hard to get concrete data for, and too hard to inspect outcomes to properly check.

Financial auditing however is the opposite of that. It's hard numbers. Inevitably when discrepancies arise, people run around chasing other people to get all their numbers close enough to something that makes sense. There's enough human wiggle-room to get away with chaotic processes that still demand accountability.

This is possibly the worst place you could put LLMs, if you care about actual outcomes:

1. Mistakes aren't going to get noticed.

2. If they are noticed, people aren't going to be empowered to actually challenge them, especially once they're used to the LLM doing the work.

3. People will be held responsible for the LLM's mistakes, despite pressure (And the general sense of time-pressure in audit is already immense ) to sign-off.

4. It's a black-box, so any faults cannot be easily diagnosed, the best you can do is try to re-prompt in a way that doesn't happen.

Re: The upcoming GPT-3 moment for RL

#98

Post by jasim »

xnorswap wrote: post #96
jasim wrote: post #92

Earlier quoted context omitted.

I'm sorry I don't follow. The fact that you use an LLM to classify a transaction does not mean there is no audit trail for the fact. There should also be a manual verifier who's ultimately responsible for the entries, so that we do not abdicate responsibility to black boxes.

If you mark data as "Processed by LLM", that in turn taints all inference from it. Requirements for a human in the loop devolve to ticking a box by someone who doesn't realise the responsibility they have been burdened with. Mark my words, some unfortunate soul will be then be thrown under the bus once a major scandal arises from such use of LLMs. As an example, companies aren't supposed to use AI for hiring, they ar…

Well put. It should always be "Created by " rather than "Processed by LLM". We can already see it with Claude Code - its commit messages contain a "Generated by Claude Code" line, and it guarantees a pandemic of diffused responsibility in software engineering. But I think there is no point in railing against it - market forces, corporate incentives, and tragedy of the commons all together make it an inevitability.

Re: The upcoming GPT-3 moment for RL

#99

Post by CamperBob2 »

kuruczgy wrote: post #16
mohsen1 wrote: post #5

I’ve been exploring this too, since I rely on LLMs a lot to build software. I’ve noticed that our dev loop-writing, testing-is often mostly human-guided, but language models frequently outperform us in reasoning. If we plug in more automation; MCP tools controlling browsers, documentation readers, requirement analysers, we can make the cycle much more automated, with less human involvement. This article suggests scal…

> but language models frequently outperform us in reasoning what 99% of the time their reasoning is laughable. Or even if their reasoning is on the right track, they often just ignore it in the final answer, and do the stupid thing anyway.

99% of the time their reasoning is laughable. Or even if their reasoning is on the right track, they often just ignore it in the final answer, and do the stupid thing anyway.

99% chance you're using the wrong model.

Effective tool use is a valuable skill, arguably the only one that still matters.

Re: The upcoming GPT-3 moment for RL

#100

Post by [deleted] »

jasim wrote: post #88
kevindamm wrote: post #23

Earlier quoted context omitted.

It's a very big "if" because other fields are comparatively underspecified. There's no equivalent to a compiler or interpreter in most cases (with spreadsheets being the lingua franca that comes even close for most industries). It would "work" but I think it will need even more scrutiny by experts to confirm what's correct and what needs to be re-generated. Please please no vibe accounting.

Accounting, specifically book-keeping, really plays to the strengths of LLMs - pattern matching within a bounded context. The primary task in book-keeping is to classify transactions (from expense vouchers, bank transactions, sales and purchase invoices and so on) and slot them into the Chart of Accounts of the business. LLMs can already do this well without any domain/business specific context. For example - a fuel…

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