6 ms·

MIT researchers use large language models to flag problems in complex systems

95 points by fluxify 2y ago

qtwhat 2y ago
Is this saying, we can use LLM to understand non-language signal? Sounds fun!
- jsemrau 2y ago
  Graph Reasoning exists.
- viraptor 2y ago
  Yes, those systems can understand a bit of math. As long as they have memory and can compare values, it should work. But it's weird that they would seriously try that given https://github.com/NX-AI/xlstm https://github.com/NX-AI/xlstm already exists. I mean, it's cool to know that it works, but I don't get why would they invest any more time trying to improve the results.
- lmpdev 2y ago
  Why not? Whether through analogy or an actual underlying isomorphism between the mechanisms underpinning language and other domains, I don’t see a reason LLMs can’t occasionally have insights into non-language problems Is it better than other methods? No. Is it efficient? Absolutely not. I don’t work with LLMs but I think a lot of the HN’s users are prematurely skeptical of the potential low-hanging fruit across many domains that can be explored with these new, convenient but invariably suboptimal tools
  - cqqxo4zV46cp 2y ago
    [flagged]
  - pydry 2y ago
    Because they're not a magic box that spits intelligence, they're a reflection of their training data which will not contain these signals.
- nerdponx 2y ago
  "LLMs" are really general-purpose sequence models. No reason it can't work, but so far results have been mixed in practice.
beardyw 2y ago
> converts time-series data into text-based inputs an LLM can process. What? Which the model then tokenizes? I am struggling to make sense of this.
- ubercore 2y ago
  I read this as basically finding a language representation of timeseries data that would be understood by LLMs better than feeding raw records. I'm guessing it tokenizes very similarly to any other LLM. Perhaps I misread, though.
- beryilma 2y ago
  This is the modern day equivalent of "every signal is an image" after the initial success of deep learning algorithms in image classification tasks. It is just the academia chasing the hype train...
mdp2021 2y ago
This exercise seems like highlighting the need for a cheap (under use), all-purpose framework for an efficient function approximator (as bare ML is too costly). They are trying LLMs for this purpose, but maybe the structure of an optimal architecture should be studied.
glutamate 2y ago
Some people, when confronted with a complex system, think "I know, I'll use a large language model." Now they have two complex systems interacting.
- cqqxo4zV46cp 2y ago
  Some people trivialise things in bad faith to score internet points on Hacker News, I guess. The status quo, per the article, was already using “deep learning” models to perform anomaly detection. We are already talking about complex, black-box systems. The stated advantage was that they didn’t require deployment-specific training. You can just throw a pre-trained LLM at it. There is also a stated benefit: early-stage detection, without needing to pay for, or wait for, training a custom ML model. The article is quite open about the fact that the LLM approach doesn’t beat the state of the art in terms of accuracy. It’s like you didn’t click the link.
  - mdp2021 2y ago
    The metrics OP suggest increase with the transition from DL to LLMs (and OP had no immediate duty to discuss other metrics).
  - glutamate 2y ago
    Colour me old-fashioned, but i am really sceptical about a zero-shot general-purpose LLM-based anomaly detector being able to outperform a trivial, not-obviously-wrong ML/statistical model calibrated on a small subset of the data that makes training extremely fast, in entirely new domains. And in anomaly detection, false negatives (and even false positives) can be very expensive. The most promising role for LLMs in timeseries prediction is in extracting covariates from unstructured data, in the wind example, weather reports and geographically proximal social media posts.
    - thomashop 2y ago
      You are skeptical about something that is explained quite thoroughly in the article. Did you read the article? Edit: I meant they never claim to outperform SOTA domain-specific approaches. So, I don't know whose claim you are refuting.
cyrilou242 2y ago
Sadly the paper uses for benchmarks datasets that: - are known to be pretty useless - contain mistakes - can be misleading with a naive F1-score measure. (to be fair they write "we looked at the F1-Score, under which both partial and full anomaly detection are considered correct identification" so this may be mitigated, but it's not clear) See https://kdd-milets.github.io/milets2021/slides/Irrational%20Exuberance_Eammon_Keogh.pdf https://kdd-milets.github.io/milets2021/slides/Irrational%20... So it's hard to take any benchmark from the paper seriously. The paper is also ignoring any recent work (like > 2018) on univariate timeseries anomaly detection in the matrix profile space (eg MADRID). The "practicality of usage" and conclusion sections are pretty correct though: it's expensive, slow, and no-shot is worthless if some other methods can train and infer in orders of magnitude less time. It would have been interesting to see how the DETECTOR method performs when the LLM forecasting is replaced with some standard forecasting. (eg some auto ETS, if possible robust to anomalies in the training data). It looks like the natural follow up of this article is to remove the LLM altogether.
- ericpauley 2y ago
  > It looks like the natural follow up of this article is to remove the LLM altogether. Welcome to scientific papers in 2024…
- nerdponx 2y ago
  > For the second approach, called Detector, they use the LLM as a forecaster to predict the next value from a time series. The researchers compare the predicted value to the actual value. A large discrepancy suggests that the real value is likely an anomaly. Unless there's more to it in the actual paper, this is how just about every anomaly detection technique already works. You fit a model of the distribution of data under normal-enough circumstances, and an observation is an "anomaly" if it seems very improbable (based on your model), or is otherwise extreme if your model isn't explicitly probabilistic. So yes, this technique would be great if you removed the LLM: it's already the industry standard framework. There's nothing wrong conceptually with trying to plug in a transformer model here. The problem is the presumption that a "large" pre-trained transformer model can actually work effectively on arbitrary time series.
iandanforth 2y ago
I once had to build a complex NN based system for this exact task. We also pitted this system against a consultant building a basic xgboost classifier working with a series of sliding windows over sensor data. Xgboost won. It was also faster to run and faster to train. I'd still love to see how a VLM did with sensor data as it's often quite obvious to spot anomalies visually for humans. Especially if you're allowed to do comparison overlays.
deleted 2y ago
[deleted]
bamboozled 2y ago
Finally, a use for LLMs?
beryilma 2y ago
Obligatory link from a sceptic: https://arxiv.org/pdf/2009.13807 https://arxiv.org/pdf/2009.13807 > However, they wanted to develop a technique that avoids fine-tuning, a process in which engineers retrain a general-purpose LLM on a small amount of task-specific data to make it an expert at one task. This method does not avoid fine tuning. It just offloads the task to somebody else (i.e., to the LLM). I'll buy the promise of the approach when the authors can show that they can vastly outperform an AR time series model or the simple techniques mentioned in the linked article.

95 points by fluxify 2y ago

qtwhat 2y ago

Is this saying, we can use LLM to understand non-language signal? Sounds fun!

jsemrau 2y ago
Graph Reasoning exists.
viraptor 2y ago
Yes, those systems can understand a bit of math. As long as they have memory and can compare values, it should work. But it's weird that they would seriously try that given https://github.com/NX-AI/xlstm https://github.com/NX-AI/xlstm already exists. I mean, it's cool to know that it works, but I don't get why would they invest any more time trying to improve the results.
lmpdev 2y ago
Why not? Whether through analogy or an actual underlying isomorphism between the mechanisms underpinning language and other domains, I don’t see a reason LLMs can’t occasionally have insights into non-language problems Is it better than other methods? No. Is it efficient? Absolutely not. I don’t work with LLMs but I think a lot of the HN’s users are prematurely skeptical of the potential low-hanging fruit across many domains that can be explored with these new, convenient but invariably suboptimal tools
- cqqxo4zV46cp 2y ago
  [flagged]
- pydry 2y ago
  Because they're not a magic box that spits intelligence, they're a reflection of their training data which will not contain these signals.
nerdponx 2y ago
"LLMs" are really general-purpose sequence models. No reason it can't work, but so far results have been mixed in practice.

beardyw 2y ago

> converts time-series data into text-based inputs an LLM can process. What? Which the model then tokenizes? I am struggling to make sense of this.

ubercore 2y ago
I read this as basically finding a language representation of timeseries data that would be understood by LLMs better than feeding raw records. I'm guessing it tokenizes very similarly to any other LLM. Perhaps I misread, though.
beryilma 2y ago
This is the modern day equivalent of "every signal is an image" after the initial success of deep learning algorithms in image classification tasks. It is just the academia chasing the hype train...

mdp2021 2y ago

This exercise seems like highlighting the need for a cheap (under use), all-purpose framework for an efficient function approximator (as bare ML is too costly). They are trying LLMs for this purpose, but maybe the structure of an optimal architecture should be studied.

glutamate 2y ago

Some people, when confronted with a complex system, think "I know, I'll use a large language model." Now they have two complex systems interacting.

cqqxo4zV46cp 2y ago
Some people trivialise things in bad faith to score internet points on Hacker News, I guess. The status quo, per the article, was already using “deep learning” models to perform anomaly detection. We are already talking about complex, black-box systems. The stated advantage was that they didn’t require deployment-specific training. You can just throw a pre-trained LLM at it. There is also a stated benefit: early-stage detection, without needing to pay for, or wait for, training a custom ML model. The article is quite open about the fact that the LLM approach doesn’t beat the state of the art in terms of accuracy. It’s like you didn’t click the link.
- mdp2021 2y ago
  The metrics OP suggest increase with the transition from DL to LLMs (and OP had no immediate duty to discuss other metrics).
- glutamate 2y ago
  Colour me old-fashioned, but i am really sceptical about a zero-shot general-purpose LLM-based anomaly detector being able to outperform a trivial, not-obviously-wrong ML/statistical model calibrated on a small subset of the data that makes training extremely fast, in entirely new domains. And in anomaly detection, false negatives (and even false positives) can be very expensive. The most promising role for LLMs in timeseries prediction is in extracting covariates from unstructured data, in the wind example, weather reports and geographically proximal social media posts.
  - thomashop 2y ago
    You are skeptical about something that is explained quite thoroughly in the article. Did you read the article? Edit: I meant they never claim to outperform SOTA domain-specific approaches. So, I don't know whose claim you are refuting.

cyrilou242 2y ago

Sadly the paper uses for benchmarks datasets that: - are known to be pretty useless - contain mistakes - can be misleading with a naive F1-score measure. (to be fair they write "we looked at the F1-Score, under which both partial and full anomaly detection are considered correct identification" so this may be mitigated, but it's not clear) See https://kdd-milets.github.io/milets2021/slides/Irrational%20Exuberance_Eammon_Keogh.pdf https://kdd-milets.github.io/milets2021/slides/Irrational%20... So it's hard to take any benchmark from the paper seriously. The paper is also ignoring any recent work (like > 2018) on univariate timeseries anomaly detection in the matrix profile space (eg MADRID). The "practicality of usage" and conclusion sections are pretty correct though: it's expensive, slow, and no-shot is worthless if some other methods can train and infer in orders of magnitude less time. It would have been interesting to see how the DETECTOR method performs when the LLM forecasting is replaced with some standard forecasting. (eg some auto ETS, if possible robust to anomalies in the training data). It looks like the natural follow up of this article is to remove the LLM altogether.

ericpauley 2y ago
> It looks like the natural follow up of this article is to remove the LLM altogether. Welcome to scientific papers in 2024…
nerdponx 2y ago
> For the second approach, called Detector, they use the LLM as a forecaster to predict the next value from a time series. The researchers compare the predicted value to the actual value. A large discrepancy suggests that the real value is likely an anomaly. Unless there's more to it in the actual paper, this is how just about every anomaly detection technique already works. You fit a model of the distribution of data under normal-enough circumstances, and an observation is an "anomaly" if it seems very improbable (based on your model), or is otherwise extreme if your model isn't explicitly probabilistic. So yes, this technique would be great if you removed the LLM: it's already the industry standard framework. There's nothing wrong conceptually with trying to plug in a transformer model here. The problem is the presumption that a "large" pre-trained transformer model can actually work effectively on arbitrary time series.

iandanforth 2y ago

I once had to build a complex NN based system for this exact task. We also pitted this system against a consultant building a basic xgboost classifier working with a series of sliding windows over sensor data. Xgboost won. It was also faster to run and faster to train. I'd still love to see how a VLM did with sensor data as it's often quite obvious to spot anomalies visually for humans. Especially if you're allowed to do comparison overlays.

deleted 2y ago

[deleted]

bamboozled 2y ago

Finally, a use for LLMs?

beryilma 2y ago

Obligatory link from a sceptic: https://arxiv.org/pdf/2009.13807 https://arxiv.org/pdf/2009.13807 > However, they wanted to develop a technique that avoids fine-tuning, a process in which engineers retrain a general-purpose LLM on a small amount of task-specific data to make it an expert at one task. This method does not avoid fine tuning. It just offloads the task to somebody else (i.e., to the LLM). I'll buy the promise of the approach when the authors can show that they can vastly outperform an AR time series model or the simple techniques mentioned in the linked article.