18 ms·

Learnings from fine-tuning LLM on my Telegram messages

216 points by furiousteabag 3y ago

jxdxbx 3y ago
“Learnings.” Horrible word
gwern 3y ago
> My data collator ensures that the loss is only calculated based on someone’s response. Predicting who will speak next is relatively straightforward, and we don’t want the model to focus on learning that. Therefore, parts of the conversation where the loss is calculated are highlighted in bold. If it's so easy, then you don't need to remove it. The model will solve it easily and focus on everything else. At best, you save some parameters and compute, at worst, you are damaging its ability to learn important things like conversational skills or modeling people. When it comes to LLMs, more is more, and trying to hand-engineer the dataset or think for the LLM can backfire in very subtle and difficult to diagnose ways. > Ok, it is capable of forming coherent sentences. The most noticeable problem is its lack of awareness regarding the context of the conversations which leads to bland and generic replies. The messages lacked any distinct style, feeling quite basic... > > Conversations have become more interesting and engaging, although there’s still a risk of losing context. Russian language performance has improved, but errors still occur. I believe that before fine-tuning for a specific task with limited data, like mine, it would be beneficial to first fine-tune the model unsupervised on a large corpus of Russian texts. Additionally, incorporating common conversation partners’ names as separate tokens might enhance the quality. I wouldn’t say it has turned out to be significantly better than LoRA. It might be more effective to focus solely on a single person and calculate the loss based only on my responses (or someone else’s), instead of trying to learn about each and every conversational partner.
- furiousteabag 3y ago
  I agree that usually 'more is more' for training LLMs. However, for fine-tuning with limited data, it seems crucial to focus the task as much as possible. Since the model still encounters these masked sentences in the data, it effectively learns to respond based on the speaker's name. So, complicating the task might not be necessary. Also, I'm concerned about interpreting the loss value. If the model quickly reduces loss by picking up predictable phrases, it's hard to tell if it's genuinely learning or just echoing these predictable elements.
  - gwern 3y ago
    > However, for fine-tuning with limited data, it seems crucial to focus the task as much as possible. That doesn't make any sense when you're dealing with a model which is so hugely over-parameterized. The model will learn the easy data that you are removing just fine. There's no 'limited data' there. > If the model quickly reduces loss by picking up predictable phrases, it's hard to tell if it's genuinely learning or just echoing these predictable elements. You can't interpret the loss qualitatively anyway. It's totally dependent on the details of tokenization, formatting, corpus size, etc. You still have to look at the samples or a downstream task to see if it's working well. Even quantitatively, the loss is only meaningful if you're comparing to a heldout sample or something, and then it doesn't matter if you were screwing with it like OP.
spdif899 3y ago
Really interesting - as another person who's used telegram for several long-standing group chats, I imagine a tool to simplify this would be well-received. I've wondered since fine-tuning started being a thing how long it'd be before somebody makes a utility where you can dump a giant chat export into it and an API key and then it fine tunes a Telegram bot that can imitate any of your friends - would be fun to play with and even create a group chat with multiple friend-bots talking to each other to see how long until it goes off the rails.
- furiousteabag 3y ago
  It's true that fine-tuning models on personal messages could be simplified, but many, like myself, can't use third-party services due to sensitive data in our messages. I'm curious if others face this trust issue and how it might be resolved.
jstrieb 3y ago
This post and its findings are really interesting! Back when the "I forced a bot to watch 1000 hours..." memes were popular (https://knowyourmeme.com/memes/i-forced-a-bot https://knowyourmeme.com/memes/i-forced-a-bot), ages ago in AI/ML time, I tried to do something similar by fine-tuning GPT-2 on messages from a group chat of my friends. Since there were years of chat data, it seemed like a really good opportunity to test whether the language model would capture everyone's personality and generate funny, uncanny-valley versions of our banter. Turns out that the group chat was used nearly exclusively for sending funny pictures and videos (that the language model obviously couldn't see), and for making plans to meet up. The generated conversations almost exclusively consisted of a random group chat member starting with "there is a party tonight, who wants to go?" and others saying "I'm down" or "when?" or "where?" It was 0% banter, and 100% logistics. It was pretty hilarious in its own way, but not for the reasons anyone expected! I didn't learn very much about language models with that experiment, but I did learn that my friends' group chat is actually pretty boring. I guess the best banter happens in real life. Glad to see it worked out somewhat more interestingly for this person, even if they did allude to some similar results in their closing thoughts section.
- razodactyl 3y ago
  This is an interesting insight into model training because it shows how hidden bias arises - think of the "left-leaning" stance of GPT3 etc. being a side-effect of the cohort that trained the model, not any deliberate action on their behalf. Additionally - it's why it's important to think ahead of what you're training your model on because the model will always regress to the training data itself even if that means going backwards in ability.
andai 3y ago
Fascinating. A few years ago a friend and I finetuned GPT-2 on our WhatsApp chat. So it was just a long text file of: Mark: wassup Andy: just chilling It simulated our conversational style and topics quite well, though GPT-2 reads like a glorified Markov chain. Sometimes the outputs were absolutely hilarious and inappropriate. GPT-2 was peak comedy. My friend described GPT-2 as "like watching a toddler learning how to walk. When it stumbles it's cute and funny." GPT-3, not so much... Also, it was oddly (painfully) accurate as far as personality goes... like looking into a mirror. For one thing, I talk way more, and this was reflected in the model's output. For another, I am constantly trying to turn my life around and failing, but ever optimistic... and talking about creative plans endlessly without much execution. (So GPT-2-andai ended up the same way...)
- codelion 3y ago
  GPT-2 is surprisingly good at fine-tuning such conversations even now. I gave a talk recently on "Sparks of Digital Immortality" that covers a bit about how we did it - https://www.youtube.com/watch?v=F9-Qk86QyMM https://www.youtube.com/watch?v=F9-Qk86QyMM
andai 3y ago
Off-topic but I think it's important: OP, in the article you say you don't want a company to have your private messages, but you are using Telegram? I also use Telegram, but I am under no illusion of privacy! Except for encrypted chats (which have bad UI and only work on one device) your messages are stored unencrypted on their servers (handed over to authorities, etc.).
- konart 3y ago
  >you don't want a company to have your private messages But that's not what he says. >I don’t want to use any third-party fine-tuning services He might be okay with a third-party storing his messages but not using them in their models etc.
  - furiousteabag 3y ago
    This may sound stupid, but from my perspective renting random VMs on vast.ai is safe in general and might be safer than using traditional cloud providers in particular. Consider this: on your VM a new image starts several times a day, each time with a new volume. It downloads tens of GBs of data and weights for training. Once training is done, everything gets cleaned up and the process starts again for a new tenant. This constant cycle makes it kind of difficult to track and extract any meaningful data from it.
    - behnamoh 3y ago
      vast didn’t work for me despite installing their certificate on macOS.
- furiousteabag 3y ago
  In IM, there's a balance between total privacy and widespread use. Apps like Signal offer high privacy but have fewer users, while popular ones like WhatsApp are less secure. Telegram lies somewhere in between, offering a level of privacy that most users find comfortable. It's widely used and there haven't been significant incidents of legal issues arising from its messages. Ultimately, it boils down to whom you trust and which app has more of your contacts.
vineetsinha 3y ago
This was a fun read. Wondering if it will work for Hindi. TIL that Mistral > Llama 2
NoraCodes 3y ago
A meta-comment, but, what is the difference between "learnings" and "lessons"? Why use the former when we have the latter?
- deleted 3y ago
  [deleted]
- bigdict 3y ago
  learnings = lessons learned
- Jolter 3y ago
  Lessons may be given, but are not necessarily learned.
- c0pium 3y ago
  Gotta earn those fat management consultant fees somehow. I’m sure there’s a whole team at McKinsey doing nothing but inventing new ways to say the same things.
  - fl7305 3y ago
    In Swedish, there's a commonly used word "lärdomar" which is a direct match for "learnings". But where the Swedish word sounds natural in that language, "learnings" just sounds wrong in English, even though it apparently is technically correct.
- furyofantares 3y ago
  Learnings implies a report of your own experience; lessons implies something prepared as teaching material for the audience. (In the context of the title sentence anyway.)
  - xanderlewis 3y ago
    ‘Lessons’ to me also seems to carry a sense of regret, as in ‘things (we) got wrong’. ‘Learnings’ is a more obscure word that I would take to mean something more neutral: literally ‘things (I’ve) learnt’.
haltist 3y ago
Great example of immortal digital avatars. This is just a simple personal avatar but it is possible to make technological gods with the same techniques. All that's needed is scale and $80B.
- codelion 3y ago
  We do this at https://meraGPT.com https://meraGPT.com
u385639 3y ago
Great post. I wonder how much this can improve if you RAG-ify a diverse set of contextual data, for example calendar, meals, recent conversations from the real world, etc. It's also interesting that бля was translated to 'damn'. :)
- furiousteabag 3y ago
  I think incorporating knowledge from other apps is a good next step because the model definitely lacks the context of what is going on right now. The nature of instant messaging is that most of the messages are about what is happening right now or what will happen in the near future, so past communication history does not help much.
goda90 3y ago
We're probably quite some time off from the bio-mimetic android part, but we're feeling closer and closer to the AI replacement avatar from the Black Mirror episode "Be Right Back"[0] [0]https://en.wikipedia.org/wiki/Be_Right_Back https://en.wikipedia.org/wiki/Be_Right_Back
thefourthchime 3y ago
This part caught my eye: "Using a half-precision FSDP full shard with a 1024 sequence length and a micro batch size of 2 required 63GB of VRAM on each of the eight A100 80 GB GPUs. The training, lasting three epochs, took just 20 minutes. The total cost for the VM was $8.88 per hour, resulting in $3, not including the time for experiments and bug fixes." I wondered where you could rent cycles on a machine like that, a quick Google found that p4d.24xlarge on AWS is available, while the on-demand cost is $20.1755 per hour, the Spot is only $8.99 (I guess it's gone up?) Cool to know I could fine-tune for only ~$3.
- bllchmbrs 3y ago
  Check out other prices on https://gpumonger.com/ https://gpumonger.com/ Disclosure: I collected the data and built the site, but it has a ton of comparison data for GPU clouds.
- ojosilva 3y ago
  It caught mine too. I'm weighting several alternatives to "fine-tuning model fine-tuning", meaning the back-and-forth, trial-and-error previous to massively running the full training set. My goal is to fine-tune a model on our codebase. I find RAG to be too orthopedic, I'd really would like to train the model on what is each part of the code and how we do things and see how it responds with a more complete perspective that goes beyond context. The options I've considered for pre-fine-tuning: - using a service like vast.ai, runpod, gradient or similar - use Google Collab - getting a more powerful MacBook, M3max with plenty of RAM
- furiousteabag 3y ago
  I've been using vast.ai for a very long time. It is like a GPU marketplace, where people rent and lease GPUs. There are a lot of VMs with 4090, and beasts like 8xA100 80GB are also available from time to time.
  - mk_stjames 3y ago
    There are all these 8x 4090 machines on Vast.ai running in ASRock epyc servers and I just want to know where the hell all those are coming from. Like I want to see pictures of these setups, since there are no off-the-shelf 4090s with blower cooler setups and watercooling that many cards together is a lot of custom hardware. And the backstories, because the fact they are 4090s and not datacenter cards, are these hobbyists just building octo-gpu $18k EPYC rigs for fun? (I even saw one with 9x 4090s! gotta use up those occulink PCIe lanes) It's not ex-mining hardware since the 4090 landed after the Eth proof-of-stake-changeover. I've been looking for an answer to this every time I check out the current vast.ai console.
lloydatkinson 3y ago
[flagged]
123sereusername 3y ago
[flagged]
- ryanklee 3y ago
  This is a ridiculous, arbitrary judgment that has nothing to do with anything even remotely related to this post. This type of pedantry is low-brow and annoying.
  - ahmedfromtunis 3y ago
    The fact that it is tangentially related to the post doesn't make it any less valuable. As a non-native speaker I find this discussion as interesting as the original post. I like to hear native speakers give their "opinions" on all matters language as it lightens some dark alleys of the language that are otherwise inaccessible. Today's lesson: "it needs repaired" is equally puzzling for some native speakers as it seems to be a regional thing :) Also: yinzers.
    - ryanklee 3y ago
      It's an incorrect judgement that's entirely ignorant of the entire field of linguistics. People should have fewer opinions of this type as they are founded on very wrong ideas of how language works. In short, it is not valuable at all in any context.
  - aerhardt 3y ago
    It's also plainly wrong, because "learnings" is perfectly commonplace.
- amccollum 3y ago
  Take it up with Shakespeare? https://www.opensourceshakespeare.org/views/plays/play_view.php?WorkID=cymbeline&Act=1&Scene=1&Scope=scene&LineHighlight=34#34 https://www.opensourceshakespeare.org/views/plays/play_view....
- sfink 3y ago
  I'm a native English speaker from the US, and a pedant who hates "ask" as a noun, "workshop" as a verb, and "performant" as a word. But I don't get the hate for "learnings" here. What's wrong with it? "Lessons" connotes negativity, "stuff I learned" doesn't naturally fit into many sentences, and "useful information gleaned" can be shoved right back up the tightly puckered ass it came out of. What's the problem? That title is exactly the way I would have written it.

216 points by furiousteabag 3y ago

“Learnings.” Horrible word

> My data collator ensures that the loss is only calculated based on someone’s response. Predicting who will speak next is relatively straightforward, and we don’t want the model to focus on learning that. Therefore, parts of the conversation where the loss is calculated are highlighted in bold. If it's so easy, then you don't need to remove it. The model will solve it easily and focus on everything else. At best, you save some parameters and compute, at worst, you are damaging its ability to learn important things like conversational skills or modeling people. When it comes to LLMs, more is more, and trying to hand-engineer the dataset or think for the LLM can backfire in very subtle and difficult to diagnose ways. > Ok, it is capable of forming coherent sentences. The most noticeable problem is its lack of awareness regarding the context of the conversations which leads to bland and generic replies. The messages lacked any distinct style, feeling quite basic... > > Conversations have become more interesting and engaging, although there’s still a risk of losing context. Russian language performance has improved, but errors still occur. I believe that before fine-tuning for a specific task with limited data, like mine, it would be beneficial to first fine-tune the model unsupervised on a large corpus of Russian texts. Additionally, incorporating common conversation partners’ names as separate tokens might enhance the quality. I wouldn’t say it has turned out to be significantly better than LoRA. It might be more effective to focus solely on a single person and calculate the loss based only on my responses (or someone else’s), instead of trying to learn about each and every conversational partner.

furiousteabag 3y ago
I agree that usually 'more is more' for training LLMs. However, for fine-tuning with limited data, it seems crucial to focus the task as much as possible. Since the model still encounters these masked sentences in the data, it effectively learns to respond based on the speaker's name. So, complicating the task might not be necessary. Also, I'm concerned about interpreting the loss value. If the model quickly reduces loss by picking up predictable phrases, it's hard to tell if it's genuinely learning or just echoing these predictable elements.
- gwern 3y ago
  > However, for fine-tuning with limited data, it seems crucial to focus the task as much as possible. That doesn't make any sense when you're dealing with a model which is so hugely over-parameterized. The model will learn the easy data that you are removing just fine. There's no 'limited data' there. > If the model quickly reduces loss by picking up predictable phrases, it's hard to tell if it's genuinely learning or just echoing these predictable elements. You can't interpret the loss qualitatively anyway. It's totally dependent on the details of tokenization, formatting, corpus size, etc. You still have to look at the samples or a downstream task to see if it's working well. Even quantitatively, the loss is only meaningful if you're comparing to a heldout sample or something, and then it doesn't matter if you were screwing with it like OP.

spdif899 3y ago

Really interesting - as another person who's used telegram for several long-standing group chats, I imagine a tool to simplify this would be well-received. I've wondered since fine-tuning started being a thing how long it'd be before somebody makes a utility where you can dump a giant chat export into it and an API key and then it fine tunes a Telegram bot that can imitate any of your friends - would be fun to play with and even create a group chat with multiple friend-bots talking to each other to see how long until it goes off the rails.

furiousteabag 3y ago
It's true that fine-tuning models on personal messages could be simplified, but many, like myself, can't use third-party services due to sensitive data in our messages. I'm curious if others face this trust issue and how it might be resolved.

jstrieb 3y ago

This post and its findings are really interesting! Back when the "I forced a bot to watch 1000 hours..." memes were popular (https://knowyourmeme.com/memes/i-forced-a-bot https://knowyourmeme.com/memes/i-forced-a-bot), ages ago in AI/ML time, I tried to do something similar by fine-tuning GPT-2 on messages from a group chat of my friends. Since there were years of chat data, it seemed like a really good opportunity to test whether the language model would capture everyone's personality and generate funny, uncanny-valley versions of our banter. Turns out that the group chat was used nearly exclusively for sending funny pictures and videos (that the language model obviously couldn't see), and for making plans to meet up. The generated conversations almost exclusively consisted of a random group chat member starting with "there is a party tonight, who wants to go?" and others saying "I'm down" or "when?" or "where?" It was 0% banter, and 100% logistics. It was pretty hilarious in its own way, but not for the reasons anyone expected! I didn't learn very much about language models with that experiment, but I did learn that my friends' group chat is actually pretty boring. I guess the best banter happens in real life. Glad to see it worked out somewhat more interestingly for this person, even if they did allude to some similar results in their closing thoughts section.

razodactyl 3y ago
This is an interesting insight into model training because it shows how hidden bias arises - think of the "left-leaning" stance of GPT3 etc. being a side-effect of the cohort that trained the model, not any deliberate action on their behalf. Additionally - it's why it's important to think ahead of what you're training your model on because the model will always regress to the training data itself even if that means going backwards in ability.

andai 3y ago

Fascinating. A few years ago a friend and I finetuned GPT-2 on our WhatsApp chat. So it was just a long text file of: Mark: wassup Andy: just chilling It simulated our conversational style and topics quite well, though GPT-2 reads like a glorified Markov chain. Sometimes the outputs were absolutely hilarious and inappropriate. GPT-2 was peak comedy. My friend described GPT-2 as "like watching a toddler learning how to walk. When it stumbles it's cute and funny." GPT-3, not so much... Also, it was oddly (painfully) accurate as far as personality goes... like looking into a mirror. For one thing, I talk way more, and this was reflected in the model's output. For another, I am constantly trying to turn my life around and failing, but ever optimistic... and talking about creative plans endlessly without much execution. (So GPT-2-andai ended up the same way...)

codelion 3y ago
GPT-2 is surprisingly good at fine-tuning such conversations even now. I gave a talk recently on "Sparks of Digital Immortality" that covers a bit about how we did it - https://www.youtube.com/watch?v=F9-Qk86QyMM https://www.youtube.com/watch?v=F9-Qk86QyMM

andai 3y ago

Off-topic but I think it's important: OP, in the article you say you don't want a company to have your private messages, but you are using Telegram? I also use Telegram, but I am under no illusion of privacy! Except for encrypted chats (which have bad UI and only work on one device) your messages are stored unencrypted on their servers (handed over to authorities, etc.).

konart 3y ago
>you don't want a company to have your private messages But that's not what he says. >I don’t want to use any third-party fine-tuning services He might be okay with a third-party storing his messages but not using them in their models etc.
- furiousteabag 3y ago
  This may sound stupid, but from my perspective renting random VMs on vast.ai is safe in general and might be safer than using traditional cloud providers in particular. Consider this: on your VM a new image starts several times a day, each time with a new volume. It downloads tens of GBs of data and weights for training. Once training is done, everything gets cleaned up and the process starts again for a new tenant. This constant cycle makes it kind of difficult to track and extract any meaningful data from it.
  - behnamoh 3y ago
    vast didn’t work for me despite installing their certificate on macOS.
furiousteabag 3y ago
In IM, there's a balance between total privacy and widespread use. Apps like Signal offer high privacy but have fewer users, while popular ones like WhatsApp are less secure. Telegram lies somewhere in between, offering a level of privacy that most users find comfortable. It's widely used and there haven't been significant incidents of legal issues arising from its messages. Ultimately, it boils down to whom you trust and which app has more of your contacts.

vineetsinha 3y ago

This was a fun read. Wondering if it will work for Hindi. TIL that Mistral > Llama 2

NoraCodes 3y ago

A meta-comment, but, what is the difference between "learnings" and "lessons"? Why use the former when we have the latter?

deleted 3y ago
[deleted]
bigdict 3y ago
learnings = lessons learned
Jolter 3y ago
Lessons may be given, but are not necessarily learned.
c0pium 3y ago
Gotta earn those fat management consultant fees somehow. I’m sure there’s a whole team at McKinsey doing nothing but inventing new ways to say the same things.
- fl7305 3y ago
  In Swedish, there's a commonly used word "lärdomar" which is a direct match for "learnings". But where the Swedish word sounds natural in that language, "learnings" just sounds wrong in English, even though it apparently is technically correct.
furyofantares 3y ago
Learnings implies a report of your own experience; lessons implies something prepared as teaching material for the audience. (In the context of the title sentence anyway.)
- xanderlewis 3y ago
  ‘Lessons’ to me also seems to carry a sense of regret, as in ‘things (we) got wrong’. ‘Learnings’ is a more obscure word that I would take to mean something more neutral: literally ‘things (I’ve) learnt’.

haltist 3y ago

Great example of immortal digital avatars. This is just a simple personal avatar but it is possible to make technological gods with the same techniques. All that's needed is scale and $80B.

codelion 3y ago
We do this at https://meraGPT.com https://meraGPT.com

u385639 3y ago

Great post. I wonder how much this can improve if you RAG-ify a diverse set of contextual data, for example calendar, meals, recent conversations from the real world, etc. It's also interesting that бля was translated to 'damn'. :)

furiousteabag 3y ago
I think incorporating knowledge from other apps is a good next step because the model definitely lacks the context of what is going on right now. The nature of instant messaging is that most of the messages are about what is happening right now or what will happen in the near future, so past communication history does not help much.

goda90 3y ago

We're probably quite some time off from the bio-mimetic android part, but we're feeling closer and closer to the AI replacement avatar from the Black Mirror episode "Be Right Back"[0] [0]https://en.wikipedia.org/wiki/Be_Right_Back https://en.wikipedia.org/wiki/Be_Right_Back

thefourthchime 3y ago

This part caught my eye: "Using a half-precision FSDP full shard with a 1024 sequence length and a micro batch size of 2 required 63GB of VRAM on each of the eight A100 80 GB GPUs. The training, lasting three epochs, took just 20 minutes. The total cost for the VM was $8.88 per hour, resulting in $3, not including the time for experiments and bug fixes." I wondered where you could rent cycles on a machine like that, a quick Google found that p4d.24xlarge on AWS is available, while the on-demand cost is $20.1755 per hour, the Spot is only $8.99 (I guess it's gone up?) Cool to know I could fine-tune for only ~$3.

bllchmbrs 3y ago
Check out other prices on https://gpumonger.com/ https://gpumonger.com/ Disclosure: I collected the data and built the site, but it has a ton of comparison data for GPU clouds.
ojosilva 3y ago
It caught mine too. I'm weighting several alternatives to "fine-tuning model fine-tuning", meaning the back-and-forth, trial-and-error previous to massively running the full training set. My goal is to fine-tune a model on our codebase. I find RAG to be too orthopedic, I'd really would like to train the model on what is each part of the code and how we do things and see how it responds with a more complete perspective that goes beyond context. The options I've considered for pre-fine-tuning: - using a service like vast.ai, runpod, gradient or similar - use Google Collab - getting a more powerful MacBook, M3max with plenty of RAM
furiousteabag 3y ago
I've been using vast.ai for a very long time. It is like a GPU marketplace, where people rent and lease GPUs. There are a lot of VMs with 4090, and beasts like 8xA100 80GB are also available from time to time.
- mk_stjames 3y ago
  There are all these 8x 4090 machines on Vast.ai running in ASRock epyc servers and I just want to know where the hell all those are coming from. Like I want to see pictures of these setups, since there are no off-the-shelf 4090s with blower cooler setups and watercooling that many cards together is a lot of custom hardware. And the backstories, because the fact they are 4090s and not datacenter cards, are these hobbyists just building octo-gpu $18k EPYC rigs for fun? (I even saw one with 9x 4090s! gotta use up those occulink PCIe lanes) It's not ex-mining hardware since the 4090 landed after the Eth proof-of-stake-changeover. I've been looking for an answer to this every time I check out the current vast.ai console.

lloydatkinson 3y ago

[flagged]

123sereusername 3y ago

[flagged]

ryanklee 3y ago
This is a ridiculous, arbitrary judgment that has nothing to do with anything even remotely related to this post. This type of pedantry is low-brow and annoying.
- ahmedfromtunis 3y ago
  The fact that it is tangentially related to the post doesn't make it any less valuable. As a non-native speaker I find this discussion as interesting as the original post. I like to hear native speakers give their "opinions" on all matters language as it lightens some dark alleys of the language that are otherwise inaccessible. Today's lesson: "it needs repaired" is equally puzzling for some native speakers as it seems to be a regional thing :) Also: yinzers.
  - ryanklee 3y ago
    It's an incorrect judgement that's entirely ignorant of the entire field of linguistics. People should have fewer opinions of this type as they are founded on very wrong ideas of how language works. In short, it is not valuable at all in any context.
- aerhardt 3y ago
  It's also plainly wrong, because "learnings" is perfectly commonplace.
amccollum 3y ago
Take it up with Shakespeare? https://www.opensourceshakespeare.org/views/plays/play_view.php?WorkID=cymbeline&Act=1&Scene=1&Scope=scene&LineHighlight=34#34 https://www.opensourceshakespeare.org/views/plays/play_view....
sfink 3y ago
I'm a native English speaker from the US, and a pedant who hates "ask" as a noun, "workshop" as a verb, and "performant" as a word. But I don't get the hate for "learnings" here. What's wrong with it? "Lessons" connotes negativity, "stuff I learned" doesn't naturally fit into many sentences, and "useful information gleaned" can be shoved right back up the tightly puckered ass it came out of. What's the problem? That title is exactly the way I would have written it.