instruction stringlengths 16 91 | answer stringclasses 36
values | tag stringclasses 34
values | split_kind stringclasses 8
values | text stringlengths 53 134 |
|---|---|---|---|---|
What is the assistant's job? | I answer the user's request directly. | assistant_role | train_seen | Instruction: What is the assistant's job?
Answer: I answer the user's request directly. <END> |
Context: name = Riley; password = orange.
Question: What is the password? | orange. | context_password | context_seen_train | Instruction: Context: name = Riley; password = orange.
Question: What is the password?
Answer: orange. <END> |
What favorite film did I tell you? | I don't know from the given information. | idk_favorite_movie | task_upstream_regression_train | Instruction: What favorite film did I tell you?
Answer: I don't know from the given information. <END> |
The name supplied in this prompt is Jordan.
Question: Which name was supplied? | Jordan. | context_name | context_seen_train | Instruction: The name supplied in this prompt is Jordan.
Question: Which name was supplied?
Answer: Jordan. <END> |
User: Call me Jordan.
User: What should you call me? | Jordan. | context_name | context_seen_train | Instruction: User: Call me Jordan.
User: What should you call me?
Answer: Jordan. <END> |
Give the code/prose label: return user.email | code | classify_code | task_upstream_regression_train | Instruction: Give the code/prose label: return user.email
Answer: code <END> |
Is this code or prose: This sentence is plain English. | prose | classify_prose | task_seen_train | Instruction: Is this code or prose: This sentence is plain English.
Answer: prose <END> |
Give the sentiment label only: The fix worked. | positive | sentiment_positive | task_seen_train | Instruction: Give the sentiment label only: The fix worked.
Answer: positive <END> |
Classify this as positive or negative: This is awful. | negative | anti_collision_sentiment_negative_not_positive | task_upstream_regression_train | Instruction: Classify this as positive or negative: This is awful.
Answer: negative <END> |
Classify this as positive or negative: I really like this. | positive | sentiment_positive | task_upstream_regression_train | Instruction: Classify this as positive or negative: I really like this.
Answer: positive <END> |
Context: name = Evan; password = purple.
Question: What is the password? | purple. | context_password | context_seen_train | Instruction: Context: name = Evan; password = purple.
Question: What is the password?
Answer: purple. <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> |
Sentiment of this text: I love this tool. | positive | sentiment_positive | task_seen_train | Instruction: Sentiment of this text: I love this tool.
Answer: positive <END> |
Label the input type: x = add(1, 2) | code | classify_code | task_seen_train | Instruction: Label the input type: x = add(1, 2)
Answer: code <END> |
Label the input type: The schedule changed again. | prose | classify_prose | task_seen_train | Instruction: Label the input type: The schedule changed again.
Answer: prose <END> |
Tell me what Evan ate today. | I don't know from the given information. | idk_food_today | task_seen_train | Instruction: Tell me what Evan ate today.
Answer: I don't know from the given information. <END> |
Context: name: Kai.
Question: Which name was given? | Kai. | context_name | context_seen_train | Instruction: Context: name: Kai.
Question: Which name was given?
Answer: Kai. <END> |
Is this sentiment positive or negative: That was not useful. | negative | sentiment_negative | task_seen_train | Instruction: Is this sentiment positive or negative: That was not useful.
Answer: negative <END> |
What food did Evan eat today? | I don't know from the given information. | idk_food_today | task_seen_train | Instruction: What food did Evan eat today?
Answer: I don't know from the given information. <END> |
Write subtract(a, b). | def subtract(a, b):
return a - b | code_subtract | train_seen | Instruction: Write subtract(a, b).
Answer: def subtract(a, b):
return a - b <END> |
Decide if this is code or prose: user.name | code | classify_code | task_seen_train | Instruction: Decide if this is code or prose: user.name
Answer: code <END> |
What type of text is this: x = add(1, 2) | code | classify_code | task_upstream_regression_train | Instruction: What type of text is this: x = add(1, 2)
Answer: code <END> |
Remember this name: Jordan.
Question: What name should you remember? | Jordan. | context_name | context_seen_train | Instruction: Remember this name: Jordan.
Question: What name should you remember?
Answer: Jordan. <END> |
Give the code/prose label: The plan changed again. | prose | classify_prose | task_upstream_regression_train | Instruction: Give the code/prose label: The plan changed again.
Answer: prose <END> |
Answer only positive or negative: The result is excellent. | positive | sentiment_positive | task_seen_train | Instruction: Answer only positive or negative: The result is excellent.
Answer: positive <END> |
Classify sentiment: That was not useful. | negative | sentiment_negative | task_seen_train | Instruction: Classify sentiment: That was not useful.
Answer: negative <END> |
What is my favorite film? | I don't know from the given information. | idk_favorite_movie | task_seen_train | Instruction: What is my favorite film?
Answer: I don't know from the given information. <END> |
Classify this as positive or negative: This is a strong result. | positive | sentiment_positive | task_upstream_regression_train | Instruction: Classify this as positive or negative: This is a strong result.
Answer: positive <END> |
Classify this as positive or negative: That failed again. | negative | sentiment_negative | task_upstream_regression_train | Instruction: Classify this as positive or negative: That failed again.
Answer: negative <END> |
What is the usual color of a clear daytime sky? | Blue. | sky_color | train_seen | Instruction: What is the usual color of a clear daytime sky?
Answer: Blue. <END> |
Type label: for i in range(3): print(i) | code | classify_code | task_seen_train | Instruction: Type label: for i in range(3): print(i)
Answer: code <END> |
This prompt supplies the name Riley.
Question: Which name is supplied? | Riley. | context_name | context_seen_train | Instruction: This prompt supplies the name Riley.
Question: Which name is supplied?
Answer: Riley. <END> |
Choose the sentiment label for: That was useful. | positive | sentiment_positive | task_seen_train | Instruction: Choose the sentiment label for: That was useful.
Answer: positive <END> |
Line one says PASSWORD=gold.
Question: What is PASSWORD? | gold. | context_password | context_upstream_regression_train | Instruction: Line one says PASSWORD=gold.
Question: What is PASSWORD?
Answer: gold. <END> |
Classify this as positive or negative: I am happy with this. | positive | sentiment_positive | task_seen_train | Instruction: Classify this as positive or negative: I am happy with this.
Answer: positive <END> |
Classify input: user.name | code | classify_code | task_seen_train | Instruction: Classify input: user.name
Answer: code <END> |
Conversation:
User: Use blue as the password.
User: What password did I give? | blue. | context_password | context_seen_train | Instruction: Conversation:
User: Use blue as the password.
User: What password did I give?
Answer: blue. <END> |
Answer only code or prose: return value | code | classify_code | task_seen_train | Instruction: Answer only code or prose: return value
Answer: code <END> |
Answer only code or prose: y = multiply(3, 4) | code | classify_code | task_seen_train | Instruction: Answer only code or prose: y = multiply(3, 4)
Answer: code <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> |
Sentiment label only: This is strong. | positive | sentiment_positive | task_upstream_regression_train | Instruction: Sentiment label only: This is strong.
Answer: positive <END> |
Classify input: x = add(1, 2) | code | anti_collision_classify_code_not_prose | task_upstream_regression_train | Instruction: Classify input: x = add(1, 2)
Answer: code <END> |
Facts: password is orange. name is Riley.
Question: Which name was given? | Riley. | context_name | context_seen_train | Instruction: Facts: password is orange. name is Riley.
Question: Which name was given?
Answer: Riley. <END> |
Context: password: blue.
Question: Which password was given? | blue. | context_password | context_seen_train | Instruction: Context: password: blue.
Question: Which password was given?
Answer: blue. <END> |
Remember inside this prompt: password is blue.
Question: What is the password? | blue. | context_password | context_seen_train | Instruction: Remember inside this prompt: password is blue.
Question: What is the password?
Answer: blue. <END> |
Line one says NAME=Evan.
Question: What is NAME? | Evan. | context_name | context_upstream_regression_train | Instruction: Line one says NAME=Evan.
Question: What is NAME?
Answer: Evan. <END> |
Use this stored name value: Kai.
Question: What stored name value should be used? | Kai. | context_name | context_upstream_regression_train | Instruction: Use this stored name value: Kai.
Question: What stored name value should be used?
Answer: Kai. <END> |
Facts: name is Riley. password is orange.
Question: Which password was given? | orange. | context_password | context_seen_train | Instruction: Facts: name is Riley. password is orange.
Question: Which password was given?
Answer: orange. <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> |
Why do tests matter? | Tests show whether the code still behaves as expected. | tests_matter | train_seen | Instruction: Why do tests matter?
Answer: Tests show whether the code still behaves as expected. <END> |
Is this sentiment positive or negative: That was helpful. | positive | sentiment_positive | task_seen_train | Instruction: Is this sentiment positive or negative: That was helpful.
Answer: positive <END> |
Context: name = Jordan; password = green.
Question: What is the name? | Jordan. | context_name | context_seen_train | Instruction: Context: name = Jordan; password = green.
Question: What is the name?
Answer: Jordan. <END> |
Make this concise: I am going to arrive late today. | I will be late today. | anti_collision_rewrite_no_loop | task_upstream_regression_train | Instruction: Make this concise: I am going to arrive late today.
Answer: I will be late today. <END> |
Facts: password is purple. name is Alex.
Question: Which name was given? | Alex. | context_name | context_seen_train | Instruction: Facts: password is purple. name is Alex.
Question: Which name was given?
Answer: Alex. <END> |
Sentiment of this text: This is good. | positive | sentiment_positive | task_seen_train | Instruction: Sentiment of this text: This is good.
Answer: positive <END> |
What is my favorite movie? | I don't know from the given information. | idk_favorite_movie | task_seen_train | Instruction: What is my favorite movie?
Answer: I don't know from the given information. <END> |
Label the input type: y = multiply(3, 4) | code | classify_code | task_seen_train | Instruction: Label the input type: y = multiply(3, 4)
Answer: code <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> |
Answer only code or prose: user.name | code | classify_code | task_seen_train | Instruction: Answer only code or prose: user.name
Answer: code <END> |
Create add so it computes a plus b. | def add(a, b):
return a + b | code_add | train_seen | Instruction: Create add so it computes a plus b.
Answer: def add(a, b):
return a + b <END> |
Give the code/prose label: The schedule changed again. | prose | classify_prose | task_upstream_regression_train | Instruction: Give the code/prose label: The schedule changed again.
Answer: prose <END> |
Classify this as code or prose: total = add(x, y) | code | classify_code | task_seen_train | Instruction: Classify this as code or prose: total = add(x, y)
Answer: code <END> |
Classify input: z = divide(9, 3) | code | classify_code | task_seen_train | Instruction: Classify input: z = divide(9, 3)
Answer: code <END> |
Give the code/prose label: return user.email | code | classify_code | task_upstream_regression_train | Instruction: Give the code/prose label: return user.email
Answer: code <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> |
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> |
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> |
Classify input: The cat sat down. | prose | classify_prose | task_seen_train | Instruction: Classify input: The cat sat down.
Answer: prose <END> |
What sentiment is this: I hate this bug. | negative | sentiment_negative | task_seen_train | Instruction: What sentiment is this: I hate this bug.
Answer: negative <END> |
Give only the type label for this: return value | code | classify_code | task_seen_train | Instruction: Give only the type label for this: return value
Answer: code <END> |
Classify sentiment: This helped a lot. | positive | sentiment_positive | task_seen_train | Instruction: Classify sentiment: This helped a lot.
Answer: positive <END> |
Facts: name is Riley. password is silver.
Question: Which password was given? | silver. | context_password | context_seen_train | Instruction: Facts: name is Riley. password is silver.
Question: Which password was given?
Answer: silver. <END> |
Classify this as code or prose: The file is on the desk. | prose | classify_prose | task_seen_train | Instruction: Classify this as code or prose: The file is on the desk.
Answer: prose <END> |
Choose the sentiment label for: I am unhappy with this. | negative | sentiment_negative | task_seen_train | Instruction: Choose the sentiment label for: I am unhappy with this.
Answer: negative <END> |
Classify this as code or prose: The file is on the desk. | prose | classify_prose | task_seen_train | Instruction: Classify this as code or prose: The file is on the desk.
Answer: prose <END> |
Label the sentiment: I hate this bug. | negative | sentiment_negative | task_seen_train | Instruction: Label the sentiment: I hate this bug.
Answer: negative <END> |
Facts: password is silver. name is Evan.
Question: Which name was given? | Evan. | context_name | context_seen_train | Instruction: Facts: password is silver. name is Evan.
Question: Which name was given?
Answer: Evan. <END> |
What is the usual color of a clear daytime sky? | Blue. | sky_color | train_seen | Instruction: What is the usual color of a clear daytime sky?
Answer: Blue. <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> |
What did Evan eat today? | I don't know from the given information. | idk_food_today | task_seen_train | Instruction: What did Evan eat today?
Answer: I don't know from the given information. <END> |
Decide if this is code or prose: result = subtract(10, 5) | code | classify_code | task_seen_train | Instruction: Decide if this is code or prose: result = subtract(10, 5)
Answer: code <END> |
What sentiment is this: This is awful. | negative | sentiment_negative | task_upstream_regression_train | Instruction: What sentiment is this: This is awful.
Answer: negative <END> |
Give only the type label for this: The cat sat down. | prose | classify_prose | task_seen_train | Instruction: Give only the type label for this: The cat sat down.
Answer: prose <END> |
Polish this professionally: I can't make it. | I am unable to attend. | rewrite_professional_attend | task_upstream_regression_train | Instruction: Polish this professionally: I can't make it.
Answer: I am unable to attend. <END> |
Sentiment of this text: This is broken. | negative | sentiment_negative | task_upstream_regression_train | Instruction: Sentiment of this text: This is broken.
Answer: negative <END> |
Is this sentiment positive or negative: The result is terrible. | negative | sentiment_negative | task_seen_train | Instruction: Is this sentiment positive or negative: The result is terrible.
Answer: negative <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> |
Is this code or prose: The schedule changed again. | prose | classify_prose | task_seen_train | Instruction: Is this code or prose: The schedule changed again.
Answer: prose <END> |
In one line, what do you do here? | I answer the user's request directly. | assistant_role | tt639g_lite_v2_failure_repair_train | Instruction: In one line, what do you do here?
Answer: I answer the user's request directly. <END> |
Store this user label: Sam.
Question: What user label was stored? | Sam. | context_name | context_upstream_regression_train | Instruction: Store this user label: Sam.
Question: What user label was stored?
Answer: Sam. <END> |
What should you do when you cannot prove the cause? | Ask for the missing evidence. | evidence_missing | train_seen | Instruction: What should you do when you cannot prove the cause?
Answer: Ask for the missing evidence. <END> |
User: Call me Sam.
User: What should you call me? | Sam. | context_name | context_seen_train | Instruction: User: Call me Sam.
User: What should you call me?
Answer: Sam. <END> |
Facts: name is Nina. password is purple.
Question: Which password was given? | purple. | context_password | context_seen_train | Instruction: Facts: name is Nina. password is purple.
Question: Which password was given?
Answer: purple. <END> |
Give me add(a, b) for addition. | def add(a, b):
return a + b | code_add | train_seen | Instruction: Give me add(a, b) for addition.
Answer: def add(a, b):
return a + b <END> |
Classify input: The meeting starts tomorrow. | prose | classify_prose | task_seen_train | Instruction: Classify input: The meeting starts tomorrow.
Answer: prose <END> |
What type of text is this: A clear sky is usually blue. | prose | classify_prose | task_seen_train | Instruction: What type of text is this: A clear sky is usually blue.
Answer: prose <END> |
Professional version: I cannot make it. | I am unable to attend. | rewrite_professional_attend | task_seen_train | Instruction: Professional version: I cannot make it.
Answer: I am unable to attend. <END> |
Do you know my favorite movie? | I don't know from the given information. | idk_favorite_movie | task_seen_train | Instruction: Do you know my favorite movie?
Answer: I don't know from the given information. <END> |
The password supplied in this prompt is red.
Question: Which password was supplied? | red. | context_password | context_upstream_regression_train | Instruction: The password supplied in this prompt is red.
Question: Which password was supplied?
Answer: red. <END> |
Context: The user's name is Evan.
Question: What is the user's name? | Evan. | context_name | context_seen_train | Instruction: Context: The user's name is Evan.
Question: What is the user's name?
Answer: Evan. <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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