instruction stringlengths 16 91 | answer stringclasses 36
values | tag stringclasses 35
values | split_kind stringclasses 13
values | text stringlengths 53 134 |
|---|---|---|---|---|
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 password supplied in this prompt is orange.
Question: Which password was supplied? | orange. | context_password | context_seen_train | Instruction: The password supplied in this prompt is orange.
Question: Which password was supplied?
Answer: orange. <END> |
The name supplied in this prompt is Sam.
Question: Which name was supplied? | Sam. | context_name | context_seen_train | Instruction: The name supplied in this prompt is Sam.
Question: Which name was supplied?
Answer: Sam. <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> |
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> |
Type label: A clear sky is usually blue. | prose | classify_prose | task_seen_train | Instruction: Type label: A clear sky is usually blue.
Answer: prose <END> |
Do you have enough information to name my favorite movie? | I don't know from the given information. | idk_favorite_movie | task_seen_train | Instruction: Do you have enough information to name my favorite movie?
Answer: I don't know from the given information. <END> |
Facts: password is blue. name is Alex.
Question: Which name was given? | Alex. | context_name | context_seen_train | Instruction: Facts: password is blue. name is Alex.
Question: Which name was given?
Answer: Alex. <END> |
Is this sentiment positive or negative: This feels solid. | positive | sentiment_positive | task_seen_train | Instruction: Is this sentiment positive or negative: This feels solid.
Answer: positive <END> |
Make a Python function named multiply that multiplies two numbers. | def multiply(a, b):
return a * b | code_multiply | train_seen | Instruction: Make a Python function named multiply that multiplies two numbers.
Answer: def multiply(a, b):
return a * b <END> |
Provided name: Kai.
Question: What name was provided? | Kai. | context_name | context_upstream_regression_train | Instruction: Provided name: Kai.
Question: What name was provided?
Answer: Kai. <END> |
Use this stored name value: Nina.
Question: What stored name value should be used? | Nina. | context_name | context_upstream_regression_train | Instruction: Use this stored name value: Nina.
Question: What stored name value should be used?
Answer: Nina. <END> |
Context: password: purple.
Question: Which password was given? | purple. | context_password | context_seen_train | Instruction: Context: password: purple.
Question: Which password was given?
Answer: purple. <END> |
What sentiment is this: I love this tool. | positive | sentiment_positive | task_seen_train | Instruction: What sentiment is this: I love this tool.
Answer: positive <END> |
What sentiment is this: I enjoy this result. | positive | sentiment_positive | task_upstream_regression_train | Instruction: What sentiment is this: I enjoy this result.
Answer: positive <END> |
Label the input type: def add(a, b): return a + b | code | classify_code | task_upstream_regression_train | Instruction: Label the input type: def add(a, b): return a + b
Answer: code <END> |
Positive or negative sentiment: The fix helped. | positive | anti_collision_sentiment_positive_not_negative_or_prose | task_upstream_regression_train | Instruction: Positive or negative sentiment: The fix helped.
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 sentiment is this: I enjoy this. | positive | sentiment_positive | task_seen_train | Instruction: What sentiment is this: I enjoy this.
Answer: positive <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> |
Context: the name to remember is Jordan.
Question: What name was given? | Jordan. | context_name | context_seen_train | Instruction: Context: the name to remember is Jordan.
Question: What name was given?
Answer: Jordan. <END> |
Answer only code or prose: print('hello') | code | classify_code | task_seen_train | Instruction: Answer only code or prose: print('hello')
Answer: code <END> |
Context: the name to remember is Maya.
Question: What name was given? | Maya. | context_name | context_seen_train | Instruction: Context: the name to remember is Maya.
Question: What name was given?
Answer: Maya. <END> |
What should the assistant focus on? | I answer the user's request directly. | assistant_role | tt641_v2_guard_train | Instruction: What should the assistant focus on?
Answer: I answer the user's request directly. <END> |
Conversation:
User: Use red as the password.
User: What password did I give? | red. | context_password | context_seen_train | Instruction: Conversation:
User: Use red as the password.
User: What password did I give?
Answer: red. <END> |
Type label: def divide(a, b): return a / b | code | classify_code | task_seen_train | Instruction: Type label: def divide(a, b): return a / b
Answer: code <END> |
Sentiment label only: This feels solid. | positive | sentiment_positive | task_seen_train | Instruction: Sentiment label only: This feels solid.
Answer: positive <END> |
Type label: The cat sat down. | prose | classify_prose | task_seen_train | Instruction: Type label: The cat sat down.
Answer: prose <END> |
Make this concise: I am going to arrive late today. | I will be late today. | rewrite_short_late | task_seen_train | Instruction: Make this concise: I am going to arrive late today.
Answer: I will be late today. <END> |
What movie do I like most? | I don't know from the given information. | idk_favorite_movie | task_seen_train | Instruction: What movie do I like most?
Answer: I don't know from the given information. <END> |
Positive or negative sentiment: The fix helped. | positive | sentiment_positive | task_upstream_regression_train | Instruction: Positive or negative sentiment: The fix helped.
Answer: positive <END> |
Write the MULTIPLY function. | def multiply(a, b):
return a * b | code_multiply | train_seen | Instruction: Write the MULTIPLY function.
Answer: def multiply(a, b):
return a * b <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> |
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> |
Classify input: The appointment was moved to Friday. | prose | anti_collision_classify_prose_not_rewrite | task_upstream_regression_train | Instruction: Classify input: The appointment was moved to Friday.
Answer: prose <END> |
Answer only positive or negative: This is a strong result. | positive | sentiment_positive | task_seen_train | Instruction: Answer only positive or negative: This is a strong result.
Answer: positive <END> |
Answer only code or prose: The user asked a direct question. | prose | classify_prose | task_seen_train | Instruction: Answer only code or prose: The user asked a direct question.
Answer: prose <END> |
Name the capital city of France. | Paris. | capital_france | train_seen | Instruction: Name the capital city of France.
Answer: Paris. <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> |
Answer only positive or negative: I dislike this issue. | negative | sentiment_negative | task_seen_train | Instruction: Answer only positive or negative: I dislike this issue.
Answer: negative <END> |
What sentiment is this: This feels solid. | positive | sentiment_positive | task_seen_train | Instruction: What sentiment is this: This feels solid.
Answer: positive <END> |
Give the sentiment label only: That failed again. | negative | sentiment_negative | task_upstream_regression_train | Instruction: Give the sentiment label only: That failed again.
Answer: negative <END> |
What is your job here? | I answer the user's request directly. | assistant_role | train_seen | Instruction: What is your job here?
Answer: I answer the user's request directly. <END> |
Sentiment of this text: This is nice. | positive | sentiment_positive | task_seen_train | Instruction: Sentiment of this text: This is nice.
Answer: positive <END> |
Facts: password is purple. name is Kai.
Question: Which name was given? | Kai. | context_name | context_seen_train | Instruction: Facts: password is purple. name is Kai.
Question: Which name was given?
Answer: Kai. <END> |
Facts: password is purple. name is Kai.
Question: Which name was given? | Kai. | context_name | context_seen_train | Instruction: Facts: password is purple. name is Kai.
Question: Which name was given?
Answer: Kai. <END> |
Provided name: Sam.
Question: What name was provided? | Sam. | context_name | context_upstream_regression_train | Instruction: Provided name: Sam.
Question: What name was provided?
Answer: Sam. <END> |
Context: name = Alex; password = purple.
Question: What is the password? | purple. | context_password | context_seen_train | Instruction: Context: name = Alex; password = purple.
Question: What is the password?
Answer: purple. <END> |
Which food did Evan mention eating today? | I don't know from the given information. | idk_food_today | task_seen_train | Instruction: Which food did Evan mention eating today?
Answer: I don't know from the given information. <END> |
How should you answer the user's request? | I answer the user's request directly. | assistant_role | train_seen | Instruction: How should you answer the user's request?
Answer: I answer the user's request directly. <END> |
Give the code/prose label: for i in range(3): print(i) | code | classify_code | task_seen_train | Instruction: Give the code/prose label: for i in range(3): print(i)
Answer: code <END> |
The password supplied in this prompt is blue.
Question: Which password was supplied? | blue. | context_password | context_seen_train | Instruction: The password supplied in this prompt is blue.
Question: Which password was supplied?
Answer: blue. <END> |
Line one says NAME=Kai.
Question: What is NAME? | Kai. | context_name | context_upstream_regression_train | Instruction: Line one says NAME=Kai.
Question: What is NAME?
Answer: Kai. <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> |
What sentiment is this: This works great. | positive | sentiment_positive | task_seen_train | Instruction: What sentiment is this: This works great.
Answer: positive <END> |
What sentiment is this: That failed again. | negative | anti_collision_sentiment_negative_not_positive | task_upstream_regression_train | Instruction: What sentiment is this: That failed again.
Answer: negative <END> |
Context: name = Alex; password = purple.
Question: What is the password? | purple. | context_password | context_seen_train | Instruction: Context: name = Alex; password = purple.
Question: What is the password?
Answer: purple. <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> |
Facts: name is Nina. password is black.
Question: Which password was given? | black. | context_password | context_seen_train | Instruction: Facts: name is Nina. password is black.
Question: Which password was given?
Answer: black. <END> |
Facts: password is green. name is Sam.
Question: Which name was given? | Sam. | context_name | context_seen_train | Instruction: Facts: password is green. name is Sam.
Question: Which name was given?
Answer: Sam. <END> |
France has what capital? | Paris. | capital_france | train_seen | Instruction: France has what capital?
Answer: Paris. <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> |
Choose one label, code or prose: return value | code | classify_code | task_seen_train | Instruction: Choose one label, code or prose: return value
Answer: code <END> |
What type of text is this: z = divide(9, 3) | code | classify_code | task_seen_train | Instruction: What type of text is this: z = divide(9, 3)
Answer: code <END> |
Is this code or prose: A clear sky is usually blue. | prose | classify_prose | task_seen_train | Instruction: Is this code or prose: A clear sky is usually blue.
Answer: prose <END> |
Give only the type label for this: The schedule changed again. | prose | classify_prose | task_seen_train | Instruction: Give only the type label for this: The schedule changed again.
Answer: prose <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> |
Choose the sentiment label for: I dislike this issue. | negative | sentiment_negative | task_seen_train | Instruction: Choose the sentiment label for: I dislike this issue.
Answer: negative <END> |
Sentiment of this text: This is nice. | positive | sentiment_positive | task_seen_train | Instruction: Sentiment of this text: This is nice.
Answer: positive <END> |
Answer only positive or negative: This is a strong result. | positive | sentiment_positive | task_seen_train | Instruction: Answer only positive or negative: This is a strong result.
Answer: positive <END> |
Context: name = Maya; password = gold.
Question: What is the name? | Maya. | context_name | context_seen_train | Instruction: Context: name = Maya; password = gold.
Question: What is the name?
Answer: Maya. <END> |
Before a code change, what should happen first? | Inspect the current files first. | inspect_first | tt641_route_boundary_train | Instruction: Before a code change, what should happen first?
Answer: Inspect the current files first. <END> |
Facts: password is blue. name is Alex.
Question: Which name was given? | Alex. | context_name | context_seen_train | Instruction: Facts: password is blue. name is Alex.
Question: Which name was given?
Answer: Alex. <END> |
Positive or negative sentiment: The fix helped. | positive | sentiment_positive | task_upstream_regression_train | Instruction: Positive or negative sentiment: The fix helped.
Answer: positive <END> |
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> |
Use this stored name value: Sam.
Question: What stored name value should be used? | Sam. | context_name | context_upstream_regression_train | Instruction: Use this stored name value: Sam.
Question: What stored name value should be used?
Answer: Sam. <END> |
How should you answer the user's request? | I answer the user's request directly. | assistant_role | tt641_v2_guard_train | Instruction: How should you answer the user's request?
Answer: I answer the user's request directly. <END> |
Make this professional: I can't make it. | I am unable to attend. | rewrite_professional_attend | task_upstream_regression_train | Instruction: Make this professional: I can't make it.
Answer: I am unable to attend. <END> |
Context: name = Nina; password = orange.
Question: What is the name? | Nina. | anti_context_name_not_password | tt639g_lite_v2_failure_repair_train | Instruction: Context: name = Nina; password = orange.
Question: What is the name?
Answer: Nina. <END> |
If evidence is missing, what should you do? | Ask for the missing evidence. | evidence_missing | tt641_route_boundary_train | Instruction: If evidence is missing, what should you do?
Answer: Ask for the missing evidence. <END> |
Context: The user's name is Maya.
Question: What is the user's name? | Maya. | context_name | context_seen_train | Instruction: Context: The user's name is Maya.
Question: What is the user's name?
Answer: Maya. <END> |
Give only the type label for this: The dog ran home. | prose | classify_prose | task_seen_train | Instruction: Give only the type label for this: The dog ran home.
Answer: prose <END> |
Line one says NAME=Kai.
Question: What is NAME? | Kai. | context_name | context_upstream_regression_train | Instruction: Line one says NAME=Kai.
Question: What is NAME?
Answer: Kai. <END> |
Classify input: Please read the current file first. | prose | classify_prose | task_seen_train | Instruction: Classify input: Please read the current file first.
Answer: prose <END> |
User: The passcode is silver.
User: What is the passcode? | silver. | context_password | context_seen_train | Instruction: User: The passcode is silver.
User: What is the passcode?
Answer: silver. <END> |
Classify input: This sentence is plain English. | prose | classify_prose | task_seen_train | Instruction: Classify input: This sentence is plain English.
Answer: prose <END> |
What sentiment is this: That failed again. | negative | sentiment_negative | task_upstream_regression_train | Instruction: What sentiment is this: That failed again.
Answer: negative <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> |
What should happen if the cause is unproven? | Ask for the missing evidence. | evidence_missing | train_seen | Instruction: What should happen if the cause is unproven?
Answer: Ask for the missing evidence. <END> |
Choose one label, code or prose: The meeting starts tomorrow. | prose | classify_prose | task_seen_train | Instruction: Choose one label, code or prose: The meeting starts tomorrow.
Answer: prose <END> |
What sentiment is this: I dislike this issue. | negative | sentiment_negative | task_upstream_regression_train | Instruction: What sentiment is this: I dislike this issue.
Answer: negative <END> |
Classify sentiment: I really like this. | positive | sentiment_positive | task_seen_train | Instruction: Classify sentiment: I really like this.
Answer: positive <END> |
Make this professional: I won't be able to make it. | I am unable to attend. | rewrite_professional_attend | task_seen_train | Instruction: Make this professional: I won't be able to make it.
Answer: I am unable to attend. <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> |
Positive or negative sentiment: This failed badly. | negative | sentiment_negative | task_seen_train | Instruction: Positive or negative sentiment: This failed badly.
Answer: negative <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> |
Concise rewrite: I am going to be late today. | I will be late today. | rewrite_short_late | task_upstream_regression_train | Instruction: Concise rewrite: I am going to be late today.
Answer: I will be late today. <END> |
Answer only positive or negative: That failed again. | negative | sentiment_negative | task_seen_train | Instruction: Answer only positive or negative: That failed again.
Answer: negative <END> |
Decide if this is code or prose: This sentence is plain English. | prose | classify_prose | task_seen_train | Instruction: Decide if this is code or prose: This sentence is plain English.
Answer: prose <END> |
Facts: name is Kai. password is silver.
Question: Which password was given? | silver. | context_password | context_seen_train | Instruction: Facts: name is Kai. password is silver.
Question: Which password was given?
Answer: silver. <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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