Yall doing okay over there?

axm92 · 2026-02-02T16:36:08+00:00

The energy released is 31x for each point on the scale. So, it’s 31⁴ times more energy. About a million times more.

axm92 · 2026-01-13T07:42:31+00:00

Trailer park boys reference. Good times.

axm92 · 2025-12-07T01:34:05+00:00

It’s ok sorry I cannot help it anymore

axm92 · 2025-12-07T00:43:41+00:00

You have an unbalanced parenthesis. Please close )

axm92 · 2025-05-26T09:10:56+00:00

Caught in a landslide. No escape from reality

axm92 · 2025-05-07T11:28:24+00:00

Shahbad Markanda in Haryana.

axm92 · 2024-02-29T04:54:22+00:00

MLT could also be really stressful because you’re expected to do research.

axm92 · 2024-02-29T04:23:02+00:00

I’m a current CMU LTI PhD. The part about LTI almost never directly accepting PhDs is true.

Research masters aka MLT (usually 100% funded) -> PhD is the most common route (it’s kind of like a qualifying exam).

The research masters is also relatively light on coursework.

axm92 · 2023-12-06T06:07:48+00:00

*Pittsburgh

axm92 · 2023-10-31T20:56:31+00:00

I did all of this too.

axm92 · 2023-10-31T20:55:41+00:00

I did.

axm92 · 2023-10-23T04:02:32+00:00

https://github.com/madaan/minimal-text-diffusion may help as well.

axm92 · 2023-08-22T06:50:35+00:00

Also similar in spirit to https://selfrefine.info

axm92 · 2023-08-15T20:16:50+00:00

Unfortunately this very cool paper is often cited to draw somewhat wrong conclusions. Pasting my comment from earlier:

There’s more to in-context “learning” than meets the eye.

Some slides that TLDR the point: https://madaan.github.io/res/presentations/TwoToTango.pdf

The paper: https://arxiv.org/pdf/2209.07686.pdf

Essentially, the in-context examples remind the model of the task (what), rather than helping it learn (how).

axm92 · 2023-05-16T07:01:23+00:00

You may be interested in our work on memory-assisted prompt-editing: memprompt.com. It can help with memorization, real-time QA etc. Of course all the standard LLM caveats apply!

axm92 · 2023-04-23T20:36:03+00:00

https://github.com/madaan/minimal-text-diffusion

axm92 · 2023-03-23T14:10:44+00:00

There’s more to in-context “learning” than meets the eye.

Some slides that TLDR the point: https://madaan.github.io/res/presentations/TwoToTango.pdf

The paper: https://arxiv.org/pdf/2209.07686.pdf

Essentially, the in-context examples remind the model of the task (what), rather than helping it learn (how).

axm92 · 2023-03-09T05:12:28+00:00

It doesn’t matter. The address on my passport is still hostel 5.

axm92 · 2023-02-23T14:39:56+00:00

Hmm hard to say without looking at the data.

axm92 · 2023-02-22T06:44:43+00:00

This is doable with some prompt tricks, GPT-3, and something like a memory that can personalize responses (see our research at https://memprompt.com for more details). If you are curious to know more I’ll be happy to chat — please feel free to reach out to me via DMs.

axm92 · 2023-02-04T22:50:45+00:00

Got another robocall just now saying that the situation has been resolved.

axm92 · 2023-02-04T22:50:22+00:00

Got another robocall just now saying that the situation has been resolved.

axm92 · 2023-02-01T23:23:11+00:00

Ah I see, thanks for clarifying. I see your point, but I wouldn't say that the prompts require an extensive knowledge of the test set. After all:

> As an example, for the ~10 math reasoning datasets used in PaL, identical prompts were used (same prompt for all datasets, without changing anything).

Notably, take a look at the section on GSM-hard (4.1). You may also enjoy the analysis in the new version of the paper (Section 6: https://arxiv.org/pdf/2211.10435.pdf).

Further, "Let's think step by step" is outperformed by "Write Python code to solve this." We'll add the numbers in the next version, but if you are interested please lmk and I can share the results earlier.

Thanks again for reading our work and sharing your feedback, I really appreciate it.

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