4 ms·

Everyone needs to take these benchmark numbers with a big grain of salt. According to what I've read, Phi-2 was much worse than its benchmark numbers suggested.

Everyone needs to take these benchmark numbers with a big grain of salt. According to what I've read, Phi-2 was much worse than its benchmark numbers suggested. This model follows the same training strategy. Nobody should be assuming these numbers will translate directly into a high ranking on the LMSYS leaderboard, or usefulness in everyday tasks. Let's not dethrone Llama 3 until some real world testing can be done. That said, I don't think it's impossible for a small model to be very good. I see their "synthetic data" as essentially a way of distilling GPT-4 into smaller models. It would be exciting if a large fraction of the performance of huge models could be transferred to small ones! If true, then Chinchilla-optimal training could make sense again, as you could optimally train a ginormous model and then distill it afterward for efficient inference.

refulgentis 3y ago
Phi-2 wasn't chat/instruct tuned, so it didn't act good in chat, it was a base model. But the benchmark #s were real.
- irjustin 3y ago
  I'm pretty naive so please forgive it's a stupid question. To me, what the parent comment is saying is that even though the benchmarks are cool, it's not super helpful to the every day person. Because if you can't chat with it very well (even for a narrow context) what utility does it have with great benchmarks?
  - svnt 3y ago
    Both are saying the same thing: in order for the base model that is phi to perform well as a chat agent, it would need to be tuned for that purpose before its benchmark results could have real-world value.
    - imjonse 3y ago
      From this report. Phi-2 was not instruct tuned indeed. "Our models went through post-training with both supervised instruction fine-tuning, and preference tuning with DPO. We have worked on generating and curating various instruction and preference data. This has improved the model chat capabilities, robustness, as well as its safety."
- nl 3y ago
  I had a lot of issues trying to get Phi-2 to perform as well as the benchmarks indicated on non-chat tasks. It felt a lot like it was overfitted to the exact type of tasks (ie, not a data leak) in the benchmarks but if you were trying something a bit off track if didn't know what to do. At the time my hypothesis was that the small model just didn't have the capacity to generalise well enough, but since then Gemma 2B has come out and seems to be ok. So now I have no idea why, but yes: the benchmarks for Phi-2 didn't represent how it worked for me on real world tasks where you'd expect it top be ok.
bt1a 3y ago
This won't dethrone Llama 3, but it's equally impressive. They mention this model's relative weakness in the TruthfulQA eval, since it's more lossy trying to pack 'knowledge' into a small model relative to problem-solving skills (which shine on MMLU) Regardless - still a very useful thing to have offline and on the fly. Those scores are nothing to scoff at. Given that these pipelines are likely harder harder to imitate than new architectures like Transformers, I assume there has been and will be an intense focus on synthetic data generation and cleansing. Llama 3 used 15T of tokens in its training corpus vs 4.8T in the "scaled-up" version of phi-3. If you made it to the end of this disjointed ramble I'm sorry
- IvanAchlaqullah 3y ago
  > TruthfulQA Wait, people still use this benchmark? I hear there's a huge flaw on it. For examples, fine-tuning the model on 4chan make it scores better on TruthfulQA. It becomes very offensive afterwards though, for obvious reasons. See GPT-4chan [1] [1] https://www.youtube.com/watch?v=efPrtcLdcdM https://www.youtube.com/watch?v=efPrtcLdcdM
  - hoseja 3y ago
    Looks like a good and useful benchmark.
  - thomashop 3y ago
    Couldn't it be that training it on 4chan makes it more truthful for some reason?
    - wongarsu 3y ago
      Could it be that people who can talk anonymously with no reputation to gain or lose and no repercussions to fear actually score high on truthfulness? Could it be that truthfulness is actually completely unrelated to the offensiveness of the language used to signal in-group status?
      - 3y ago
ankit219 3y ago
Not trying to disparage them, but their models always give a feeling that it is overfitted on benchmarks hence they perform so well. On everyday tasks, it's much worse - chat or simple completion tasks. Distilling can work and there are papers which suggest it does, but we still do not have a reliable mechanism which can distill knowledge from larger teacher models to smaller student models.
- moffkalast 3y ago
  This was the case for Phi-2, it was notoriously rubbish in practical use.
spmurrayzzz 3y ago
I don't think we can call it distillation, at least not in the conventional ML use of the word as you're not interacting with any of the actual model architecture, specifically — not computing the loss between the predictions of the parent model and the distilled target model. This is an important distinction when it comes to assess model collapse risk, which is a risk I think has probably been overstated enough to this point where now its being understated.
HarHarVeryFunny 3y ago
The Chinchilla paper is about how to design/train a model to optimize use of computing power, which we can equate to cost (FLOPs cost dollars). The question that Chinchilla tries to answer is: for a given training budget (which you can think of as dollars or FLOPs), what is the optimal trade off of model size and quantity of training data to get the most performant model? Build a large model and train with less data, or build a smaller one and train with more data? However, another consideration is minimizing total lifetime cost of the model: training cost + inference cost. You could train a model for longer (costing more) in order to get a given level of performance from a smaller model that will be cheaper for inference, or vice versa. For any given projected model lifetime inference volume, there is going to be a different answer. It's not that Chinchilla-optimal models stopped making sense, but rather that this sort of consideration has people willing to pump more money (tokens) into smaller models to reduce inference cost for that level of capability.
- KuriousCat 3y ago
  Does the paper assume uniform settings through out the training phase? Or is it the bound no matter what training strategy is used given the dataset?
  - HarHarVeryFunny 3y ago
    They only experimented with different cosine learning rate decay schedules, but found results consistent across these, as well as across two different types of experiment where they either varied number of training tokens for a given model size, or varied model size for a given number of training FLOPs.

refulgentis 3y ago

Phi-2 wasn't chat/instruct tuned, so it didn't act good in chat, it was a base model. But the benchmark #s were real.

irjustin 3y ago
I'm pretty naive so please forgive it's a stupid question. To me, what the parent comment is saying is that even though the benchmarks are cool, it's not super helpful to the every day person. Because if you can't chat with it very well (even for a narrow context) what utility does it have with great benchmarks?
- svnt 3y ago
  Both are saying the same thing: in order for the base model that is phi to perform well as a chat agent, it would need to be tuned for that purpose before its benchmark results could have real-world value.
  - imjonse 3y ago
    From this report. Phi-2 was not instruct tuned indeed. "Our models went through post-training with both supervised instruction fine-tuning, and preference tuning with DPO. We have worked on generating and curating various instruction and preference data. This has improved the model chat capabilities, robustness, as well as its safety."
nl 3y ago
I had a lot of issues trying to get Phi-2 to perform as well as the benchmarks indicated on non-chat tasks. It felt a lot like it was overfitted to the exact type of tasks (ie, not a data leak) in the benchmarks but if you were trying something a bit off track if didn't know what to do. At the time my hypothesis was that the small model just didn't have the capacity to generalise well enough, but since then Gemma 2B has come out and seems to be ok. So now I have no idea why, but yes: the benchmarks for Phi-2 didn't represent how it worked for me on real world tasks where you'd expect it top be ok.

bt1a 3y ago

This won't dethrone Llama 3, but it's equally impressive. They mention this model's relative weakness in the TruthfulQA eval, since it's more lossy trying to pack 'knowledge' into a small model relative to problem-solving skills (which shine on MMLU) Regardless - still a very useful thing to have offline and on the fly. Those scores are nothing to scoff at. Given that these pipelines are likely harder harder to imitate than new architectures like Transformers, I assume there has been and will be an intense focus on synthetic data generation and cleansing. Llama 3 used 15T of tokens in its training corpus vs 4.8T in the "scaled-up" version of phi-3. If you made it to the end of this disjointed ramble I'm sorry

IvanAchlaqullah 3y ago
> TruthfulQA Wait, people still use this benchmark? I hear there's a huge flaw on it. For examples, fine-tuning the model on 4chan make it scores better on TruthfulQA. It becomes very offensive afterwards though, for obvious reasons. See GPT-4chan [1] [1] https://www.youtube.com/watch?v=efPrtcLdcdM https://www.youtube.com/watch?v=efPrtcLdcdM
- hoseja 3y ago
  Looks like a good and useful benchmark.
- thomashop 3y ago
  Couldn't it be that training it on 4chan makes it more truthful for some reason?
  - wongarsu 3y ago
    Could it be that people who can talk anonymously with no reputation to gain or lose and no repercussions to fear actually score high on truthfulness? Could it be that truthfulness is actually completely unrelated to the offensiveness of the language used to signal in-group status?
    - 3y ago

ankit219 3y ago

Not trying to disparage them, but their models always give a feeling that it is overfitted on benchmarks hence they perform so well. On everyday tasks, it's much worse - chat or simple completion tasks. Distilling can work and there are papers which suggest it does, but we still do not have a reliable mechanism which can distill knowledge from larger teacher models to smaller student models.

moffkalast 3y ago
This was the case for Phi-2, it was notoriously rubbish in practical use.

spmurrayzzz 3y ago

I don't think we can call it distillation, at least not in the conventional ML use of the word as you're not interacting with any of the actual model architecture, specifically — not computing the loss between the predictions of the parent model and the distilled target model. This is an important distinction when it comes to assess model collapse risk, which is a risk I think has probably been overstated enough to this point where now its being understated.

HarHarVeryFunny 3y ago

The Chinchilla paper is about how to design/train a model to optimize use of computing power, which we can equate to cost (FLOPs cost dollars). The question that Chinchilla tries to answer is: for a given training budget (which you can think of as dollars or FLOPs), what is the optimal trade off of model size and quantity of training data to get the most performant model? Build a large model and train with less data, or build a smaller one and train with more data? However, another consideration is minimizing total lifetime cost of the model: training cost + inference cost. You could train a model for longer (costing more) in order to get a given level of performance from a smaller model that will be cheaper for inference, or vice versa. For any given projected model lifetime inference volume, there is going to be a different answer. It's not that Chinchilla-optimal models stopped making sense, but rather that this sort of consideration has people willing to pump more money (tokens) into smaller models to reduce inference cost for that level of capability.

KuriousCat 3y ago
Does the paper assume uniform settings through out the training phase? Or is it the bound no matter what training strategy is used given the dataset?
- HarHarVeryFunny 3y ago
  They only experimented with different cosine learning rate decay schedules, but found results consistent across these, as well as across two different types of experiment where they either varied number of training tokens for a given model size, or varied model size for a given number of training FLOPs.