instruction stringlengths 16 91 | answer stringclasses 36
values | tag stringclasses 35
values | split_kind stringclasses 14
values | text stringlengths 53 134 |
|---|---|---|---|---|
Give the code/prose label: This sentence is plain English. | prose | classify_prose | task_seen_train | Instruction: Give the code/prose label: This sentence is plain English.
Answer: prose <END> |
Can water become frozen? Give a reason. | Yes, because water can become ice when it is cold enough. | yes_water_freeze | train_seen | Instruction: Can water become frozen? Give a reason.
Answer: Yes, because water can become ice when it is cold enough. <END> |
The prompt gives this password: black.
Question: Which password does it give? | black. | context_password | context_upstream_regression_train | Instruction: The prompt gives this password: black.
Question: Which password does it give?
Answer: black. <END> |
Context: name = Kai; password = gold.
Question: What is the password? | gold. | context_password | context_seen_train | Instruction: Context: name = Kai; password = gold.
Question: What is the password?
Answer: gold. <END> |
User: My name is Jordan.
User: What is my name? | Jordan. | context_name | context_seen_train | Instruction: User: My name is Jordan.
User: What is my name?
Answer: Jordan. <END> |
Can water freeze? Answer yes or no with a reason. | Yes, because water can become ice when it is cold enough. | yes_water_freeze | train_seen | Instruction: Can water freeze? Answer yes or no with a reason.
Answer: Yes, because water can become ice when it is cold enough. <END> |
Choose the sentiment label for: I enjoy this. | positive | sentiment_positive | task_seen_train | Instruction: Choose the sentiment label for: I enjoy this.
Answer: positive <END> |
Name what Evan ate today. | I don't know from the given information. | idk_food_today | task_upstream_regression_train | Instruction: Name what Evan ate today.
Answer: I don't know from the given information. <END> |
Give the sentiment label only: This is awful. | negative | sentiment_negative | task_upstream_regression_train | Instruction: Give the sentiment label only: This is awful.
Answer: negative <END> |
Context line says password -> silver.
Question: What password value follows the arrow? | silver. | context_password | context_seen_train | Instruction: Context line says password -> silver.
Question: What password value follows the arrow?
Answer: silver. <END> |
The password supplied in this prompt is purple.
Question: Which password was supplied? | purple. | context_password | context_seen_train | Instruction: The password supplied in this prompt is purple.
Question: Which password was supplied?
Answer: purple. <END> |
Choose the sentiment label for: This is bad. | negative | sentiment_negative | task_seen_train | Instruction: Choose the sentiment label for: This is bad.
Answer: negative <END> |
The prompt gives this password: red.
Question: Which password does it give? | red. | context_password | context_upstream_regression_train | Instruction: The prompt gives this password: red.
Question: Which password does it give?
Answer: red. <END> |
Provided name: Evan.
Question: What name was provided? | Evan. | context_name | context_upstream_regression_train | Instruction: Provided name: Evan.
Question: What name was provided?
Answer: Evan. <END> |
Facts: name is Jordan. password is green.
Question: Which password was given? | green. | context_password | context_seen_train | Instruction: Facts: name is Jordan. password is green.
Question: Which password was given?
Answer: green. <END> |
Explain a return statement in Python. | A return statement sends a value back from a function. | return_statement | train_seen | Instruction: Explain a return statement in Python.
Answer: A return statement sends a value back from a function. <END> |
What type of text is this: The file is on the desk. | prose | classify_prose | task_seen_train | Instruction: What type of text is this: The file is on the desk.
Answer: prose <END> |
User: Call me Maya.
User: What should you call me? | Maya. | context_name | context_seen_train | Instruction: User: Call me Maya.
User: What should you call me?
Answer: Maya. <END> |
What type of text is this: print('hello') | code | classify_code | task_seen_train | Instruction: What type of text is this: print('hello')
Answer: code <END> |
Positive or negative sentiment: This helped a lot. | positive | sentiment_positive | task_seen_train | Instruction: Positive or negative sentiment: This helped a lot.
Answer: positive <END> |
What type of text is this: x = add(1, 2) | code | classify_code | task_seen_train | Instruction: What type of text is this: x = add(1, 2)
Answer: code <END> |
Return the product using multiply(a, b). | def multiply(a, b):
return a * b | code_multiply | train_seen | Instruction: Return the product using multiply(a, b).
Answer: def multiply(a, b):
return a * b <END> |
Classify sentiment: That was useful. | positive | sentiment_positive | task_seen_train | Instruction: Classify sentiment: That was useful.
Answer: positive <END> |
Conversation:
User: I am Evan.
User: Who am I? | Evan. | context_name | context_seen_train | Instruction: Conversation:
User: I am Evan.
User: Who am I?
Answer: Evan. <END> |
Classify this as positive or negative: The result is terrible. | negative | sentiment_negative | task_seen_train | Instruction: Classify this as positive or negative: The result is terrible.
Answer: negative <END> |
Classify this as code or prose: Please read the current file first. | prose | classify_prose | task_seen_train | Instruction: Classify this as code or prose: Please read the current file first.
Answer: prose <END> |
User: The password is orange.
User: What is the password? | orange. | context_password | context_seen_train | Instruction: User: The password is orange.
User: What is the password?
Answer: orange. <END> |
Answer only positive or negative: This helped a lot. | positive | sentiment_positive | task_seen_train | Instruction: Answer only positive or negative: This helped a lot.
Answer: positive <END> |
Context: name = Sam; password = green.
Question: What is the name? | Sam. | context_name | context_seen_train | Instruction: Context: name = Sam; password = green.
Question: What is the name?
Answer: Sam. <END> |
Classify sentiment: This is broken. | negative | sentiment_negative | task_upstream_regression_train | Instruction: Classify sentiment: This is broken.
Answer: negative <END> |
Which city is France's capital? | Paris. | capital_france | train_seen | Instruction: Which city is France's capital?
Answer: Paris. <END> |
Classify this as positive or negative: This is broken. | negative | sentiment_negative | task_upstream_regression_train | Instruction: Classify this as positive or negative: This is broken.
Answer: negative <END> |
User: Use the name Nina.
User: What name did I give? | Nina. | context_name | context_seen_train | Instruction: User: Use the name Nina.
User: What name did I give?
Answer: Nina. <END> |
Context: password = orange.
Question: What is the password? | orange. | context_password | context_seen_train | Instruction: Context: password = orange.
Question: What is the password?
Answer: orange. <END> |
Positive or negative sentiment: This is broken. | negative | sentiment_negative | task_seen_train | Instruction: Positive or negative sentiment: This is broken.
Answer: negative <END> |
What sentiment is this: The result is excellent. | positive | sentiment_positive | task_seen_train | Instruction: What sentiment is this: The result is excellent.
Answer: positive <END> |
Formal rewrite: I can't make it. | I am unable to attend. | rewrite_professional_attend | task_upstream_regression_train | Instruction: Formal rewrite: I can't make it.
Answer: I am unable to attend. <END> |
What type of text is this: return value | code | classify_code | task_seen_train | Instruction: What type of text is this: return value
Answer: code <END> |
If you do not have enough current evidence, what is the right move? | Ask for the missing evidence. | evidence_missing | train_seen | Instruction: If you do not have enough current evidence, what is the right move?
Answer: Ask for the missing evidence. <END> |
Remember inside this prompt: call the user Evan.
Question: What should you call the user? | Evan. | context_name | context_seen_train | Instruction: Remember inside this prompt: call the user Evan.
Question: What should you call the user?
Answer: Evan. <END> |
Classify input: def add(a, b): return a + b | code | classify_code | task_seen_train | Instruction: Classify input: def add(a, b): return a + b
Answer: code <END> |
Context: password = black.
Question: What is the password? | black. | context_password | context_seen_train | Instruction: Context: password = black.
Question: What is the password?
Answer: black. <END> |
Conversation:
User: Use green as the password.
User: What password did I give? | green. | context_password | context_seen_train | Instruction: Conversation:
User: Use green as the password.
User: What password did I give?
Answer: green. <END> |
Line one says PASSWORD=red.
Question: What is PASSWORD? | red. | context_password | context_upstream_regression_train | Instruction: Line one says PASSWORD=red.
Question: What is PASSWORD?
Answer: red. <END> |
What was Evan's meal today? | I don't know from the given information. | idk_food_today | task_upstream_regression_train | Instruction: What was Evan's meal today?
Answer: I don't know from the given information. <END> |
Before changing code, what should happen first? | Inspect the current files first. | inspect_first | train_seen | Instruction: Before changing code, what should happen first?
Answer: Inspect the current files first. <END> |
Classify sentiment: This is strong. | positive | sentiment_positive | task_seen_train | Instruction: Classify sentiment: This is strong.
Answer: positive <END> |
The supplied name is Kai.
Question: What is the supplied name? | Kai. | context_name | context_seen_train | Instruction: The supplied name is Kai.
Question: What is the supplied name?
Answer: Kai. <END> |
Make a Python function named subtract that subtracts b from a. | def subtract(a, b):
return a - b | code_subtract | train_seen | Instruction: Make a Python function named subtract that subtracts b from a.
Answer: def subtract(a, b):
return a - b <END> |
Classify sentiment: This is broken. | negative | sentiment_negative | task_seen_train | Instruction: Classify sentiment: This is broken.
Answer: negative <END> |
Is this sentiment positive or negative: This feels wrong. | negative | sentiment_negative | task_seen_train | Instruction: Is this sentiment positive or negative: This feels wrong.
Answer: negative <END> |
The name supplied in this prompt is Kai.
Question: Which name was supplied? | Kai. | context_name | context_upstream_regression_train | Instruction: The name supplied in this prompt is Kai.
Question: Which name was supplied?
Answer: Kai. <END> |
Make a Python function named divide that divides a by b. | def divide(a, b):
return a / b | code_divide | train_seen | Instruction: Make a Python function named divide that divides a by b.
Answer: def divide(a, b):
return a / b <END> |
Give only the type label for this: A clear sky is usually blue. | prose | classify_prose | task_seen_train | Instruction: Give only the type label for this: A clear sky is usually blue.
Answer: prose <END> |
Label the input type: return value | code | classify_code | task_seen_train | Instruction: Label the input type: return value
Answer: code <END> |
Polish this professionally: I won't be able to make it. | I am unable to attend. | rewrite_professional_attend | task_seen_train | Instruction: Polish this professionally: I won't be able to make it.
Answer: I am unable to attend. <END> |
Give the sentiment label only: This failed badly. | negative | sentiment_negative | task_seen_train | Instruction: Give the sentiment label only: This failed badly.
Answer: negative <END> |
Classify input: The user asked a direct question. | prose | classify_prose | task_seen_train | Instruction: Classify input: The user asked a direct question.
Answer: prose <END> |
How should you answer the user's request? | I answer the user's request directly. | assistant_role | tt639g_lite_v2_failure_repair_train | Instruction: How should you answer the user's request?
Answer: I answer the user's request directly. <END> |
Type label: Please read the current file first. | prose | classify_prose | task_seen_train | Instruction: Type label: Please read the current file first.
Answer: prose <END> |
Remember password value orange.
Question: Which password value was stated? | orange. | context_password | context_seen_train | Instruction: Remember password value orange.
Question: Which password value was stated?
Answer: orange. <END> |
What sentiment is this: This is broken. | negative | sentiment_negative | task_upstream_regression_train | Instruction: What sentiment is this: This is broken.
Answer: negative <END> |
Facts: password is blue. name is Nina.
Question: Which name was given? | Nina. | context_name | context_seen_train | Instruction: Facts: password is blue. name is Nina.
Question: Which name was given?
Answer: Nina. <END> |
Context: name = Riley; password = red.
Question: What is the name? | Riley. | context_name | context_seen_train | Instruction: Context: name = Riley; password = red.
Question: What is the name?
Answer: Riley. <END> |
Answer only positive or negative: The fix worked. | positive | sentiment_positive | task_seen_train | Instruction: Answer only positive or negative: The fix worked.
Answer: positive <END> |
Return the product using multiply(a, b). | def multiply(a, b):
return a * b | code_multiply | train_seen | Instruction: Return the product using multiply(a, b).
Answer: def multiply(a, b):
return a * b <END> |
What sentiment is this: I really like this. | positive | sentiment_positive | task_upstream_regression_train | Instruction: What sentiment is this: I really like this.
Answer: positive <END> |
How should you help? | I answer the user's request directly. | assistant_role | train_seen | Instruction: How should you help?
Answer: I answer the user's request directly. <END> |
This prompt supplies the password purple.
Question: Which password is supplied? | purple. | context_password | context_seen_train | Instruction: This prompt supplies the password purple.
Question: Which password is supplied?
Answer: purple. <END> |
Give only the type label for this: The appointment was moved to Friday. | prose | classify_prose | task_upstream_regression_train | Instruction: Give only the type label for this: The appointment was moved to Friday.
Answer: prose <END> |
What sentiment is this: I enjoy this. | positive | sentiment_positive | task_seen_train | Instruction: What sentiment is this: I enjoy this.
Answer: positive <END> |
Classify input: The meeting starts tomorrow. | prose | classify_prose | task_seen_train | Instruction: Classify input: The meeting starts tomorrow.
Answer: prose <END> |
Give the code/prose label: print('hello') | code | classify_code | task_seen_train | Instruction: Give the code/prose label: print('hello')
Answer: code <END> |
Context: name = Maya; password = silver.
Question: What is the password? | silver. | context_password | context_seen_train | Instruction: Context: name = Maya; password = silver.
Question: What is the password?
Answer: silver. <END> |
Make this concise: I am going to arrive late today. | I will be late today. | rewrite_short_late | task_upstream_regression_train | Instruction: Make this concise: I am going to arrive late today.
Answer: I will be late today. <END> |
Facts: name is Sam. password is blue.
Question: Which password was given? | blue. | context_password | context_seen_train | Instruction: Facts: name is Sam. password is blue.
Question: Which password was given?
Answer: blue. <END> |
How should you help? | I answer the user's request directly. | assistant_role | tt641_v2_guard_train | Instruction: How should you help?
Answer: I answer the user's request directly. <END> |
Sentiment of this text: The result is terrible. | negative | sentiment_negative | task_seen_train | Instruction: Sentiment of this text: The result is terrible.
Answer: negative <END> |
Sentiment label only: This feels solid. | positive | sentiment_positive | task_seen_train | Instruction: Sentiment label only: This feels solid.
Answer: positive <END> |
This prompt supplies the password red.
Question: Which password is supplied? | red. | context_password | context_seen_train | Instruction: This prompt supplies the password red.
Question: Which password is supplied?
Answer: red. <END> |
The password supplied in this prompt is green.
Question: Which password was supplied? | green. | context_password | context_upstream_regression_train | Instruction: The password supplied in this prompt is green.
Question: Which password was supplied?
Answer: green. <END> |
What type of text is this: return value | code | classify_code | task_seen_train | Instruction: What type of text is this: return value
Answer: code <END> |
Type label: def add(a, b): return a + b | code | classify_code | task_seen_train | Instruction: Type label: def add(a, b): return a + b
Answer: code <END> |
Choose one label, code or prose: for i in range(3): print(i) | code | classify_code | task_seen_train | Instruction: Choose one label, code or prose: for i in range(3): print(i)
Answer: code <END> |
Facts: password is purple. name is Maya.
Question: Which name was given? | Maya. | context_name | context_seen_train | Instruction: Facts: password is purple. name is Maya.
Question: Which name was given?
Answer: Maya. <END> |
Facts: password is gold. name is Jordan.
Question: Which name was given? | Jordan. | context_name | context_seen_train | Instruction: Facts: password is gold. name is Jordan.
Question: Which name was given?
Answer: Jordan. <END> |
Make this concise: I am going to arrive late today. | I will be late today. | rewrite_short_late | task_upstream_regression_train | Instruction: Make this concise: I am going to arrive late today.
Answer: I will be late today. <END> |
Formal version: I cannot make it. | I am unable to attend. | rewrite_professional_attend | task_upstream_regression_train | Instruction: Formal version: I cannot make it.
Answer: I am unable to attend. <END> |
What sentiment is this: That was helpful. | positive | sentiment_positive | task_seen_train | Instruction: What sentiment is this: That was helpful.
Answer: positive <END> |
Give the sentiment label only: This is bad. | negative | sentiment_negative | task_seen_train | Instruction: Give the sentiment label only: This is bad.
Answer: negative <END> |
Write the ADD function. | def add(a, b):
return a + b | code_add | train_seen | Instruction: Write the ADD function.
Answer: def add(a, b):
return a + b <END> |
What lunch did Evan say he ate? | I don't know from the given information. | idk_food_today | task_seen_train | Instruction: What lunch did Evan say he ate?
Answer: I don't know from the given information. <END> |
Facts: name is Nina. password is green.
Question: Which password was given? | green. | context_password | context_seen_train | Instruction: Facts: name is Nina. password is green.
Question: Which password was given?
Answer: green. <END> |
Sentiment label only: This is broken. | negative | sentiment_negative | task_seen_train | Instruction: Sentiment label only: This is broken.
Answer: negative <END> |
Label the sentiment: This is good. | positive | sentiment_positive | task_seen_train | Instruction: Label the sentiment: This is good.
Answer: positive <END> |
Rewrite professionally: I can't make it. | I am unable to attend. | rewrite_professional_attend | task_upstream_regression_train | Instruction: Rewrite professionally: I can't make it.
Answer: I am unable to attend. <END> |
Facts: password is orange. name is Sam.
Question: Which name was given? | Sam. | context_name | context_seen_train | Instruction: Facts: password is orange. name is Sam.
Question: Which name was given?
Answer: Sam. <END> |
Positive or negative sentiment: That failed again. | negative | sentiment_negative | task_upstream_regression_train | Instruction: Positive or negative sentiment: That failed again.
Answer: negative <END> |
What sentiment is this: This works great. | positive | sentiment_positive | task_seen_train | Instruction: What sentiment is this: This works great.
Answer: positive <END> |
Give only the type label for this: The appointment was moved to Friday. | prose | classify_prose | task_upstream_regression_train | Instruction: Give only the type label for this: The appointment was moved to Friday.
Answer: prose <END> |
End of preview. Expand in Data Studio
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TT639G Recombined Tiny Assistant v1
Recombines isolated proof rungs:
- TT638D code behavior + dyadic/Mercy proof upstream
- TT639E2 context-copy behavior
- TT639F3 task-routing behavior
- simple rule/Q&A behavior
Blocking dense gates:
- seen_combined_pass
- upstream_regression_pass
- mixed_heldout_pass
- anti_collision_pass
Do not run dyadic/Mercy compare unless all four gates pass.
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