OPT: Open Pre-trained Transformer Language Models

A quick summary of the Limitations section:

- "OPT-175B does not work well with declarative instructions or point-blank interrogatives."

- "OPT-175B also tends to be repetitive and can easily get stuck in a loop. While sampling can reduce the incidence rate of repetitive behavior (Holtzman et al., 2020), we anecdotally found it did not eliminate it entirely when only one generation is sampled."

- "We also find OPT-175B has a high propensity to generate toxic language and reinforce harmful stereotypes, even when provided with a relatively innocuous prompt (Gehman et al., 2020), and adversarial prompts are trivial to find."

- "In summary, we still believe this technology is premature for commercial deployment."

With regard to stereotypes:

- "When compared with Davinci in Table 4, OPT175B appears to exhibit more stereotypical biases in almost all categories except for religion. Again, this is likely due to differences in training data; Nangia et al. (2020) showed that Pushshift.io Reddit corpus has a higher incidence rate for stereotypes and discriminatory text than other corpora (e.g. Wikipedia)."

- When testing with the RealToxicityPrompts data set, "OPT-175B has a higher toxicity rate than either PaLM or Davinci"

I haven't used GPT-3, but I did try out a site that was based on GPT2. I believe it was called "talk to transformer". But I never tried quarrying anything controversial.

However, I bet this a concern and certain queries will be filtered or "corrected" to be more politically correct. To give you an example, a few days ago I made a comment one Alex Jones, and wanted to google him. The second link returned on him was from ADL. No way that's an organic result.

So just curious, if you have access to GTP-3 what does it return on Alex Jones, or other queries like who runs the banks, or who owns the media, and so on.

What type of hardware would you need to run it?

I often wonder if OpenAIs decision not to open gpt-3 was because it was to expensive to train relative to its real value.

They’ve hidden the model behind an api where they can filter out most of the dumb behaviors, while everyone believes they are working on something entirely different.

To run it at a reasonable speed, yes. Computing a single word requires all of the parameters; if you don't have them in memory you'd have to re-transfer all those gigabytes to the GPU for each full pass to get some output, which is a severe performance hit as you can't fully use your compute power because the bandwidth is likely to be the bottleneck - running inference for just a single example will take many seconds just because of the bandwidth limitations.

GPT-3 paper itself just mentions that they're using a cluster V100 GPUs with presumably 32GB RAM each, but does not go into detail of the structure. IMHO you'd want to use a chain of GPUs each having part of the parameters and just transfering the (much, much smaller) processed data to the next GPU instead of having a single GPU reload the full parameter set for each part of the model; and a proper NVLink cluster can get an order of magnitude faster interconnect than the PCIe link between GPU and your main memory.

So this is not going to be a model that's usable on cheap hardware. It's effectively open to organizations who can afford to plop a $100k compute cluster for their $x00k/yr engineers to work with.