When ChatGPT broke the field of NLP: An oral history

Post by teruakohatu » Thu, May 01, 2025, 11:35 PM UTC

Cheer2171 wrote: post #38

teruakohatu wrote: post #31

I am in academia and worked in NLP although I would describe myself as NLP adjacent. I can confirm LLMs have essentially confined a good chunk of historical research into the bin. I suspect there are probably still a few PhD students working on traditional methods knowing full well a layman can do better using the mobile ChatGPT app. That said traditional NLP has its uses. Using the VADER model for sentiment analysis…

That's because VADER is just a dictionary mapping each word to a single sentiment weight and adding it up with some basic logic for negations and such. There's an ocean of smaller NLP ML between that naive approach and LLMs. LLMs are trained to do everything. If all you need is a model trained to do sentiment analysis, using VADER over something like DistilBERT is NLP malpractice in 2025.

Post by jdgoesmarching » Thu, May 01, 2025, 11:53 PM UTC

suddenlybananas wrote: post #28

languagehacker wrote: post #5

Great seeing Ray Mooney (who I took a graduate class with) and Emily Bender (a colleague of many at the UT Linguistics Dept., and a regular visitor) sharing their honest reservations with AI and LLMs. I try to stay as far away from this stuff as possible because when the bottom falls out, it's going to have devastating effects for everyone involved. As a former computational linguist and someone who built similar too…

Don't try and say anything pro-linguistics here, people are weirdly hostile if you think it's anything but probabilities.

Post by bravura » Fri, May 02, 2025, 12:03 AM UTC

teruakohatu wrote: post #51

Cheer2171 wrote: post #38

Earlier quoted context omitted.

That's because VADER is just a dictionary mapping each word to a single sentiment weight and adding it up with some basic logic for negations and such. There's an ocean of smaller NLP ML between that naive approach and LLMs. LLMs are trained to do everything. If all you need is a model trained to do sentiment analysis, using VADER over something like DistilBERT is NLP malpractice in 2025.

> using VADER over something like DistilBERT is NLP malpractice in 2025. Ouch. Was that necessary? I used $1000 worth of GPU credits and threw in VADER because it’s basically free both in time and credits. I usually do this on large dataset out of pure interest in how it correlates with expensive methods on English language text. I am well aware of how VADER works and its limitations, I am also aware of the limitatio…

Post by Karrot_Kream » Fri, May 02, 2025, 12:13 AM UTC

teruakohatu wrote: post #51

Cheer2171 wrote: post #38

Earlier quoted context omitted.

That's because VADER is just a dictionary mapping each word to a single sentiment weight and adding it up with some basic logic for negations and such. There's an ocean of smaller NLP ML between that naive approach and LLMs. LLMs are trained to do everything. If all you need is a model trained to do sentiment analysis, using VADER over something like DistilBERT is NLP malpractice in 2025.

> using VADER over something like DistilBERT is NLP malpractice in 2025. Ouch. Was that necessary? I used $1000 worth of GPU credits and threw in VADER because it’s basically free both in time and credits. I usually do this on large dataset out of pure interest in how it correlates with expensive methods on English language text. I am well aware of how VADER works and its limitations, I am also aware of the limitatio…

Post by mistrial9 » Fri, May 02, 2025, 12:47 AM UTC

vjerancrnjak wrote: post #44

CNNs were outperforming traditional methods on some tasks before 2017. Problem was that all of the low level tasks , like part of speech tagging, parsing, named entity recognition , etc. never resulted in a good summarizing system or translating system. Probabilistic graphical models worked a bit but not much. Transformers were a leap, where none of the low level tasks had to be done for high level ones. Pretty sure…