#deepread — Public Fediverse posts

Back in the days of 2021
there was a lovely evaluation paper:
Automatically identifying label errors
Improving score's reliability
Finding example's difficulty
Active Learning

https://aclanthology.org/2021.acl-long.346/

@par @hoyle
#machinelearning #evaluation #IRT #LLM #deepRead

Back in the days of 2021
there was a lovely evaluation paper:
Automatically identifying label errors
Improving score's reliability
Finding example's difficulty
Active Learning

https://aclanthology.org/2021.acl-long.346/

@par @hoyle
#machinelearning #evaluation #IRT #LLM #deepRead

Back in the days of 2021
there was a lovely evaluation paper:
Automatically identifying label errors
Improving score's reliability
Finding example's difficulty
Active Learning

https://aclanthology.org/2021.acl-long.346/

@par @hoyle
#machinelearning #evaluation #IRT #LLM #deepRead

Back in the days of 2021
there was a lovely evaluation paper:
Automatically identifying label errors
Improving score's reliability
Finding example's difficulty
Active Learning

https://aclanthology.org/2021.acl-long.346/

@par @hoyle
#machinelearning #evaluation #IRT #LLM #deepRead

Back in the days of 2021
there was a lovely evaluation paper:
Automatically identifying label errors
Improving score's reliability
Finding example's difficulty
Active Learning

https://aclanthology.org/2021.acl-long.346/

@par @hoyle
#machinelearning #evaluation #IRT #LLM #deepRead

Did you know:
Evaluating a single model on HELM took
⏱️4K GPU hours or 💸+10K$ in API calls?!
Flash-HELM⚡️can reduce costs by X200!
https://arxiv.org/abs/2308.11696

#deepRead #machinelearning #evaluation #eval #nlproc #NLP #LLM

Did you know:
Evaluating a single model on HELM took
⏱️4K GPU hours or 💸+10K$ in API calls?!
Flash-HELM⚡️can reduce costs by X200!
https://arxiv.org/abs/2308.11696

#deepRead #machinelearning #evaluation #eval #nlproc #NLP #LLM

Did you know:
Evaluating a single model on HELM took
⏱️4K GPU hours or 💸+10K$ in API calls?!
Flash-HELM⚡️can reduce costs by X200!
https://arxiv.org/abs/2308.11696

#deepRead #machinelearning #evaluation #eval #nlproc #NLP #LLM

Did you know:
Evaluating a single model on HELM took
⏱️4K GPU hours or 💸+10K$ in API calls?!
Flash-HELM⚡️can reduce costs by X200!
https://arxiv.org/abs/2308.11696

#deepRead #machinelearning #evaluation #eval #nlproc #NLP #LLM

Did you know:
Evaluating a single model on HELM took
⏱️4K GPU hours or 💸+10K$ in API calls?!
Flash-HELM⚡️can reduce costs by X200!
https://arxiv.org/abs/2308.11696

#deepRead #machinelearning #evaluation #eval #nlproc #NLP #LLM

The newFormer is introduced,
but what do we really know about it?

@ari and others
imagine a new large-scale architecture &
ask how would you interptret its abilities and behaviours 🧵
https://arxiv.org/abs/2308.00189
#deepRead #NLProc #MachineLearning

The newFormer is introduced,
but what do we really know about it?

@ari and others
imagine a new large-scale architecture &
ask how would you interptret its abilities and behaviours 🧵
https://arxiv.org/abs/2308.00189
#deepRead #NLProc #MachineLearning

The newFormer is introduced,
but what do we really know about it?

@ari and others
imagine a new large-scale architecture &
ask how would you interptret its abilities and behaviours 🧵
https://arxiv.org/abs/2308.00189
#deepRead #NLProc #MachineLearning

The newFormer is introduced,
but what do we really know about it?

@ari and others
imagine a new large-scale architecture &
ask how would you interptret its abilities and behaviours 🧵
https://arxiv.org/abs/2308.00189
#deepRead #NLProc #MachineLearning

The newFormer is introduced,
but what do we really know about it?

@ari and others
imagine a new large-scale architecture &
ask how would you interptret its abilities and behaviours 🧵
https://arxiv.org/abs/2308.00189
#deepRead #NLProc #MachineLearning

@mega Linear transformations can skip over layers, even till the end

We can see 👀 what the network 🧠 thought!
We can stop🛑 generating at early layers!

https://arxiv.org/abs/2303.09435v1

#NLProc #deepRead

@mega Linear transformations can skip over layers, even till the end

We can see 👀 what the network 🧠 thought!
We can stop🛑 generating at early layers!

https://arxiv.org/abs/2303.09435v1

#NLProc #deepRead

@mega Linear transformations can skip over layers, even till the end

We can see 👀 what the network 🧠 thought!
We can stop🛑 generating at early layers!

https://arxiv.org/abs/2303.09435v1

#NLProc #deepRead

@mega Linear transformations can skip over layers, even till the end

We can see 👀 what the network 🧠 thought!
We can stop🛑 generating at early layers!

https://arxiv.org/abs/2303.09435v1

#NLProc #deepRead

@mega Linear transformations can skip over layers, even till the end

We can see 👀 what the network 🧠 thought!
We can stop🛑 generating at early layers!

https://arxiv.org/abs/2303.09435v1

#NLProc #deepRead

🔎What's in a layer?🌹🕵🏻‍♀️

Representations are vectors
If only they were words...

Finding:
Any layer can be mapped well to another linearly
Simple, efficient & interpretable
& improves early exit

https://arxiv.org/abs/2303.09435v1
Story and 🧵
#nlproc #deepRead #MachinLearning

🔎What's in a layer?🌹🕵🏻‍♀️

Representations are vectors
If only they were words...

Finding:
Any layer can be mapped well to another linearly
Simple, efficient & interpretable
& improves early exit

https://arxiv.org/abs/2303.09435v1
Story and 🧵
#nlproc #deepRead #MachinLearning

🔎What's in a layer?🌹🕵🏻‍♀️

Representations are vectors
If only they were words...

Finding:
Any layer can be mapped well to another linearly
Simple, efficient & interpretable
& improves early exit

https://arxiv.org/abs/2303.09435v1
Story and 🧵
#nlproc #deepRead #MachinLearning

🔎What's in a layer?🌹🕵🏻‍♀️

Representations are vectors
If only they were words...

Finding:
Any layer can be mapped well to another linearly
Simple, efficient & interpretable
& improves early exit

https://arxiv.org/abs/2303.09435v1
Story and 🧵
#nlproc #deepRead #MachinLearning

🔎What's in a layer?🌹🕵🏻‍♀️

Representations are vectors
If only they were words...

Finding:
Any layer can be mapped well to another linearly
Simple, efficient & interpretable
& improves early exit

https://arxiv.org/abs/2303.09435v1
Story and 🧵
#nlproc #deepRead #MachinLearning

Mindblowing pretraining paradigm

Train the same model to predict the two directions separately
Better results, more parallelization

https://arxiv.org/abs/2303.07295
#deepRead #nlproc #pretraining #machinelearning

Mindblowing pretraining paradigm

Train the same model to predict the two directions separately
Better results, more parallelization

https://arxiv.org/abs/2303.07295
#deepRead #nlproc #pretraining #machinelearning

Mindblowing pretraining paradigm

Train the same model to predict the two directions separately
Better results, more parallelization

https://arxiv.org/abs/2303.07295
#deepRead #nlproc #pretraining #machinelearning

Mindblowing pretraining paradigm

Train the same model to predict the two directions separately
Better results, more parallelization

https://arxiv.org/abs/2303.07295
#deepRead #nlproc #pretraining #machinelearning

Mindblowing pretraining paradigm

Train the same model to predict the two directions separately
Better results, more parallelization

https://arxiv.org/abs/2303.07295
#deepRead #nlproc #pretraining #machinelearning

3 reasons for hallucinations started
only 2 prevailed

Finding how networks behave while hallucinating, they
filter hallucinations (with great success)

https://arxiv.org/abs/2301.07779
#NLProc #neuralEmpty #NLP #deepRead

3 reasons for hallucinations started
only 2 prevailed

Finding how networks behave while hallucinating, they
filter hallucinations (with great success)

https://arxiv.org/abs/2301.07779
#NLProc #neuralEmpty #NLP #deepRead

3 reasons for hallucinations started
only 2 prevailed

Finding how networks behave while hallucinating, they
filter hallucinations (with great success)

https://arxiv.org/abs/2301.07779
#NLProc #neuralEmpty #NLP #deepRead

3 reasons for hallucinations started
only 2 prevailed

Finding how networks behave while hallucinating, they
filter hallucinations (with great success)

https://arxiv.org/abs/2301.07779
#NLProc #neuralEmpty #NLP #deepRead

3 reasons for hallucinations started
only 2 prevailed

Finding how networks behave while hallucinating, they
filter hallucinations (with great success)

https://arxiv.org/abs/2301.07779
#NLProc #neuralEmpty #NLP #deepRead

What neurons determine agreement in multilingual LLMs?

#deepRead but some answers:
Across languages-2 distinct ways to encode syntax
Share neurons not info

Autoregressive have dedicated synt. neurons (MLM just spread across)

@[email protected] yu xia @[email protected] #conllLivetweet2022

What neurons determine agreement in multilingual LLMs?

#deepRead but some answers:
Across languages-2 distinct ways to encode syntax
Share neurons not info

Autoregressive have dedicated synt. neurons (MLM just spread across)

@[email protected] yu xia @[email protected] #conllLivetweet2022

What neurons determine agreement in multilingual LLMs?

#deepRead but some answers:
Across languages-2 distinct ways to encode syntax
Share neurons not info

Autoregressive have dedicated synt. neurons (MLM just spread across)

@[email protected] yu xia @[email protected] #conllLivetweet2022

What neurons determine agreement in multilingual LLMs?

#deepRead but some answers:
Across languages-2 distinct ways to encode syntax
Share neurons not info

Autoregressive have dedicated synt. neurons (MLM just spread across)

@[email protected] yu xia @[email protected] #conllLivetweet2022

What neurons determine agreement in multilingual LLMs?

#deepRead but some answers:
Across languages-2 distinct ways to encode syntax
Share neurons not info

Autoregressive have dedicated synt. neurons (MLM just spread across)

@[email protected] yu xia @[email protected] #conllLivetweet2022