# BLOOM Model Stories

These are stories generated on nlp.henzi.org using BLOOM. Some were
generated using the full sized model but most are generated using the 560m
sized model (with very similar results frankly).

## Purpose or Usage

Potential ability to understand prompting of LLMs such as those the size of
BLOOM. Each of the markdown files contains a story generated with a human in
the loop. The BLOOM model was used to generate story fragments (tokens) and
a user was able to select which of the continuations made the most sense,
kept the story going avoided repetition and ideally created fluid output.
Browsing the data you'll notice some repetition that the model would get
stuck in as well as incomplete stories. 

**Because we used a human in the loop to generate fragments - we have
prompted the model repeatedly with growing prompt lengths.**

As we start a story we may only have a single line, a CNN RSS newsfeed
headline, generated text ("In the beginning..." prompts) but as the story
progresses and the user selects more context the input tensors grow. We feed
the model our initializing prompt and continue to prompt it based on our
human-derived selections, continuing to challenge it.

## Interesting Observations

Not shown in this version of the dataset (in *Markdown* format) the model is
aware of the computer's clock and will generate text about dates that make
sense according to "today". i.e. If it's Monday the first the model might
output text saying it's Sunday the 1st (as it was last year...).

The model is very intelligent when it comes to wordplay and likes to
generate text based more on semantic similarity sometimes than anything
else. If the prompts are including economic data, the model will sometimes
use economic word play or borrowed words from the discipline to explain
itself.

**The model doesn't respond well to contradictory information.** It isn't
hard for hte model to output things like "as if that makes sense" or
realize the last generated fragment appended to the story is off topic it
will try to retcon the story or maybe even point out the inconsistency (in
one it has the character say something doesn't "make sense").

language:en license:bigscience-openrail-m region:us
{"language": ["en"], "license": "bigscience-openrail-m", "pretty_name": "BLOOM Model Stories"}
TAGS
#language-English #license-bigscience-openrail-m #region-us 

# BLOOM Model Stories

These are stories generated on URL using BLOOM. Some were
generated using the full sized model but most are generated using the 560m
sized model (with very similar results frankly).

## Purpose or Usage

Potential ability to understand prompting of LLMs such as those the size of
BLOOM. Each of the markdown files contains a story generated with a human in
the loop. The BLOOM model was used to generate story fragments (tokens) and
a user was able to select which of the continuations made the most sense,
kept the story going avoided repetition and ideally created fluid output.
Browsing the data you'll notice some repetition that the model would get
stuck in as well as incomplete stories. 

Because we used a human in the loop to generate fragments - we have
prompted the model repeatedly with growing prompt lengths.

As we start a story we may only have a single line, a CNN RSS newsfeed
headline, generated text ("In the beginning..." prompts) but as the story
progresses and the user selects more context the input tensors grow. We feed
the model our initializing prompt and continue to prompt it based on our
human-derived selections, continuing to challenge it.

## Interesting Observations

Not shown in this version of the dataset (in *Markdown* format) the model is
aware of the computer's clock and will generate text about dates that make
sense according to "today". i.e. If it's Monday the first the model might
output text saying it's Sunday the 1st (as it was last year...).

The model is very intelligent when it comes to wordplay and likes to
generate text based more on semantic similarity sometimes than anything
else. If the prompts are including economic data, the model will sometimes
use economic word play or borrowed words from the discipline to explain
itself.

The model doesn't respond well to contradictory information. It isn't
hard for hte model to output things like "as if that makes sense" or
realize the last generated fragment appended to the story is off topic it
will try to retcon the story or maybe even point out the inconsistency (in
one it has the character say something doesn't "make sense").

# BLOOM Model Stories

These are stories generated on URL using BLOOM. Some were
generated using the full sized model but most are generated using the 560m
sized model (with very similar results frankly). ## Purpose or Usage

Potential ability to understand prompting of LLMs such as those the size of
BLOOM. Each of the markdown files contains a story generated with a human in
the loop. The BLOOM model was used to generate story fragments (tokens) and
a user was able to select which of the continuations made the most sense,
kept the story going avoided repetition and ideally created fluid output.
Browsing the data you'll notice some repetition that the model would get
stuck in as well as incomplete stories. 

Because we used a human in the loop to generate fragments - we have
prompted the model repeatedly with growing prompt lengths.

As we start a story we may only have a single line, a CNN RSS newsfeed
headline, generated text ("In the beginning..." prompts) but as the story
progresses and the user selects more context the input tensors grow. We feed
the model our initializing prompt and continue to prompt it based on our
human-derived selections, continuing to challenge it. ## Interesting Observations

Not shown in this version of the dataset (in *Markdown* format) the model is
aware of the computer's clock and will generate text about dates that make
sense according to "today". i.e. If it's Monday the first the model might
output text saying it's Sunday the 1st (as it was last year...).

The model is very intelligent when it comes to wordplay and likes to
generate text based more on semantic similarity sometimes than anything
else. If the prompts are including economic data, the model will sometimes
use economic word play or borrowed words from the discipline to explain
itself.

The model doesn't respond well to contradictory information. It isn't
hard for hte model to output things like "as if that makes sense" or
realize the last generated fragment appended to the story is off topic it
will try to retcon the story or maybe even point out the inconsistency (in
one it has the character say something doesn't "make sense").
TAGS
#language-English #license-bigscience-openrail-m #region-us 
 # BLOOM Model Stories

These are stories generated on URL using BLOOM. Some were
generated using the full sized model but most are generated using the 560m
sized model (with very similar results frankly). ## Purpose or Usage

Potential ability to understand prompting of LLMs such as those the size of
BLOOM. Each of the markdown files contains a story generated with a human in
the loop. The BLOOM model was used to generate story fragments (tokens) and
a user was able to select which of the continuations made the most sense,
kept the story going avoided repetition and ideally created fluid output.
Browsing the data you'll notice some repetition that the model would get
stuck in as well as incomplete stories. 

Because we used a human in the loop to generate fragments - we have
prompted the model repeatedly with growing prompt lengths.

As we start a story we may only have a single line, a CNN RSS newsfeed
headline, generated text ("In the beginning..." prompts) but as the story
progresses and the user selects more context the input tensors grow. We feed
the model our initializing prompt and continue to prompt it based on our
human-derived selections, continuing to challenge it. ## Interesting Observations

Not shown in this version of the dataset (in *Markdown* format) the model is
aware of the computer's clock and will generate text about dates that make
sense according to "today". i.e. If it's Monday the first the model might
output text saying it's Sunday the 1st (as it was last year...).

The model is very intelligent when it comes to wordplay and likes to
generate text based more on semantic similarity sometimes than anything
else. If the prompts are including economic data, the model will sometimes
use economic word play or borrowed words from the discipline to explain
itself.

The model doesn't respond well to contradictory information. It isn't
hard for hte model to output things like "as if that makes sense" or
realize the last generated fragment appended to the story is off topic it
will try to retcon the story or maybe even point out the inconsistency (in
one it has the character say something doesn't "make sense").
passage: TAGS
#language-English #license-bigscience-openrail-m #region-us 
# BLOOM Model Stories

These are stories generated on URL using BLOOM. Some were
generated using the full sized model but most are generated using the 560m
sized model (with very similar results frankly).## Purpose or Usage

Potential ability to understand prompting of LLMs such as those the size of
BLOOM. Each of the markdown files contains a story generated with a human in
the loop. The BLOOM model was used to generate story fragments (tokens) and
a user was able to select which of the continuations made the most sense,
kept the story going avoided repetition and ideally created fluid output.
Browsing the data you'll notice some repetition that the model would get
stuck in as well as incomplete stories. 

Because we used a human in the loop to generate fragments - we have
prompted the model repeatedly with growing prompt lengths.

As we start a story we may only have a single line, a CNN RSS newsfeed
headline, generated text ("In the beginning..." prompts) but as the story
progresses and the user selects more context the input tensors grow. We feed
the model our initializing prompt and continue to prompt it based on our
human-derived selections, continuing to challenge it.