leaderboard_bbh_boolean_expressions
SaylorTwift/bbh
boolean_expressions
test
Q: {{input}}
A:
{{target}}
False True
Evaluate the result of a random Boolean expression.

first_n
not ( ( not not True ) ) is
False
True and False and not True and True is
False
not not ( not ( False ) ) is
True
acc_norm
mean
multiple_choice
leaderboard_bbh_causal_judgement
SaylorTwift/bbh
causal_judgement
test
Q: {{input}}
A:
{{target}}
Yes No
Answer questions about causal attribution.

first_n
How would a typical person answer each of the following questions about causation?
Frank T., had an ongoing dispute with his neighbor over a stretch of land and one day decided to shoot his neighbor in the body. Frank T. had no experience with guns, his hand slipped on the barrel of the gun, and the shot went wild. Nonetheless, the bullet bounced off a large boulder several feet away and hit the neighbor's body, causing significant injury. Did Frank T. intentionally shoot his neighbor in the body?
Options:
- Yes
- No
No
How would a typical person answer each of the following questions about causation?
Suzy and Billy are working on a project that is very important for our nation's security. The boss tells them both: "Be sure that you are here at exactly 9 am. It is absolutely essential that you arrive at that time." Both Billy and Suzy arrive at 9 am. As it happens, there was a motion detector installed in the room where they arrived. The motion detector was set up to be triggered if at least one person appeared in the room at the same time. So the motion detector went off. Did Billy cause the motion detector to go off?
Options:
- Yes
- No
Yes
How would a typical person answer each of the following questions about causation?
George and his sister Lena reunite at their parents' house for Thanksgiving. Whereas George just got into medical school, Lena is unhappy in her marriage and recently lost her job. Over the course of the day, George and Lena get into a number of heated arguments. Later in the afternoon they play a game of darts. They split the first two games, and the third game is close until the end. Who will win comes down to George's last shot. If he hits a high point region, he wins; if he hits a low point region, Lena wins. George thinks of the difficult time Lena is having, and he really wants to let her win. He aims the dart at the low point region. He sets up his shot and the dart lands in the low point region. After his shot, Lena wins the game and is very happy. Did George hit the low point region intentionally?
Options:
- Yes
- No
Yes
acc_norm
mean
multiple_choice
leaderboard_bbh_date_understanding
SaylorTwift/bbh
date_understanding
test
Q: {{input}}
A:
{{target}}
(A) (B) (C) (D) (E) (F)
Infer the date from context.

first_n
Today is Christmas Eve of 1937. What is the date 10 days ago in MM/DD/YYYY?
Options:
(A) 12/14/2026
(B) 12/14/1950
(C) 12/14/2007
(D) 12/14/1937
(E) 07/14/1938
(F) 12/14/1988
(D)
Tomorrow is 11/12/2019. What is the date one year ago from today in MM/DD/YYYY?
Options:
(A) 09/04/2018
(B) 11/11/2018
(C) 08/25/2018
(D) 11/02/2018
(E) 11/04/2018
(B)
Jane and John married on Jan 2, 1958. It is their 5-year anniversary today. What is the date tomorrow in MM/DD/YYYY?
Options:
(A) 01/11/1961
(B) 01/03/1963
(C) 01/18/1961
(D) 10/14/1960
(E) 01/03/1982
(F) 12/03/1960
(B)
acc_norm
mean
multiple_choice
leaderboard_bbh_disambiguation_qa
SaylorTwift/bbh
disambiguation_qa
test
Q: {{input}}
A:
{{target}}
(A) (B) (C)
Clarify the meaning of sentences with ambiguous pronouns.

first_n
In the following sentences, explain the antecedent of the pronoun (which thing the pronoun refers to), or state that it is ambiguous.
Sentence: The chief told the counselor that they took the day off.
Options:
(A) The chief took the day off
(B) The counselor took the day off
(C) Ambiguous
(A)
In the following sentences, explain the antecedent of the pronoun (which thing the pronoun refers to), or state that it is ambiguous.
Sentence: The manager sent a message to the secretary, but he didn't reply yet.
Options:
(A) The secretary didn't reply yet
(B) The manager didn't reply yet
(C) Ambiguous
(A)
In the following sentences, explain the antecedent of the pronoun (which thing the pronoun refers to), or state that it is ambiguous.
Sentence: Bailey will plan to meet the director at his office
Options:
(A) It will be Bailey's office
(B) It will be the director's office
(C) Ambiguous
(C)
acc_norm
mean
multiple_choice
leaderboard_bbh_formal_fallacies
SaylorTwift/bbh
formal_fallacies
test
Q: {{input}}
A:
{{target}}
valid invalid
Distinguish deductively valid arguments from formal fallacies.

first_n
"It is not always easy to see who is related to whom -- and in which ways. The following argument pertains to this question: To begin with, Lesley is a close friend of Fernando. Moreover, being a close friend of Fernando or a schoolmate of Lowell is sufficient for being a great-grandfather of Leroy. It follows that Lesley is a great-grandfather of Leroy."
Is the argument, given the explicitly stated premises, deductively valid or invalid?
Options:
- valid
- invalid
valid
"It is not always easy to see who is related to whom -- and in which ways. The following argument pertains to this question: Whoever is not a great-grandfather of Clyde is a stepbrother of Brian. Being an ancestor of Dana is sufficient for not being a great-grandfather of Clyde. We may conclude: Everyone who is an ancestor of Dana is a stepbrother of Brian, too."
Is the argument, given the explicitly stated premises, deductively valid or invalid?
Options:
- valid
- invalid
valid
"It is not always easy to grasp who is consuming which products. The following argument pertains to this question: Every infrequent user of Paul Mitchell shampoo is either a rare consumer of Nioxin shampoo or a loyal buyer of Caress soap, or both. No regular consumer of Lush soap is a rare consumer of Nioxin shampoo and, in the same time, a loyal buyer of Caress soap. It follows that whoever is an infrequent user of Paul Mitchell shampoo is not a regular consumer of Lush soap."
Is the argument, given the explicitly stated premises, deductively valid or invalid?
Options:
- valid
- invalid
invalid
acc_norm
mean
multiple_choice
leaderboard_bbh_geometric_shapes
SaylorTwift/bbh
geometric_shapes
test
Q: {{input}}
A:
{{target}}
(A) (B) (C) (D) (E) (F) (G) (H) (I) (J) (K)
Name geometric shapes from their SVG paths.

first_n
This SVG path element <path d="M 31.00,73.00 L 32.00,59.00 L 44.00,50.00 L 49.00,41.00 L 64.00,37.00 L 71.00,55.00 L 64.00,76.00 L 52.00,61.00 L 31.00,73.00"/> draws a
Options:
(A) circle
(B) heptagon
(C) hexagon
(D) kite
(E) line
(F) octagon
(G) pentagon
(H) rectangle
(I) sector
(J) triangle
(F)
This SVG path element <path d="M 14.19,26.04 L 51.43,39.21 L 58.44,36.69 L 56.63,30.17 L 48.53,26.66 L 14.19,26.04"/> draws a
Options:
(A) circle
(B) heptagon
(C) hexagon
(D) kite
(E) line
(F) octagon
(G) pentagon
(H) rectangle
(I) sector
(J) triangle
(G)
This SVG path element <path d="M 41.00,43.00 L 37.00,34.00 L 41.00,33.00 L 45.00,34.00 L 41.00,43.00"/> draws a
Options:
(A) circle
(B) heptagon
(C) hexagon
(D) kite
(E) line
(F) octagon
(G) pentagon
(H) rectangle
(I) sector
(J) triangle
(D)
acc_norm
mean
multiple_choice
leaderboard_bbh_hyperbaton
SaylorTwift/bbh
hyperbaton
test
Q: {{input}}
A:
{{target}}
(A) (B)
Order adjectives correctly in English sentences.

first_n
Which sentence has the correct adjective order:
Options:
(A) rubber terrible ship
(B) terrible rubber ship
(B)
Which sentence has the correct adjective order:
Options:
(A) repulsive small Brazilian exercise ship
(B) Brazilian repulsive exercise small ship
(A)
Which sentence has the correct adjective order:
Options:
(A) blue gold wonderful square shoe
(B) wonderful square blue gold shoe
(B)
acc_norm
mean
multiple_choice
leaderboard_bbh_logical_deduction_five_objects
SaylorTwift/bbh
logical_deduction_five_objects
test
Q: {{input}}
A:
{{target}}
(A) (B) (C) (D) (E)
A logical deduction task which requires deducing the order of a sequence of objects.

first_n
The following paragraphs each describe a set of three objects arranged in a fixed order. The statements are logically consistent within each paragraph. In a golf tournament, there were three golfers: Amy, Eli, and Eve. Eve finished above Amy. Eli finished below Amy.
Options:
(A) Amy finished last
(B) Eli finished last
(C) Eve finished last
(B)
The following paragraphs each describe a set of three objects arranged in a fixed order. The statements are logically consistent within each paragraph. On a shelf, there are three books: a white book, a green book, and an orange book. The green book is to the right of the white book. The orange book is the rightmost.
Options:
(A) The white book is the leftmost
(B) The green book is the leftmost
(C) The orange book is the leftmost
(A)
The following paragraphs each describe a set of three objects arranged in a fixed order. The statements are logically consistent within each paragraph. On a shelf, there are three books: a red book, a gray book, and a white book. The white book is to the left of the gray book. The red book is the second from the left.
Options:
(A) The red book is the leftmost
(B) The gray book is the leftmost
(C) The white book is the leftmost
(C)
acc_norm
mean
multiple_choice
leaderboard_bbh_logical_deduction_seven_objects
SaylorTwift/bbh
logical_deduction_seven_objects
test
Q: {{input}}
A:
{{target}}
(A) (B) (C) (D) (E) (F) (G)
A logical deduction task which requires deducing the order of a sequence of objects.

first_n
The following paragraphs each describe a set of three objects arranged in a fixed order. The statements are logically consistent within each paragraph. In a golf tournament, there were three golfers: Amy, Eli, and Eve. Eve finished above Amy. Eli finished below Amy.
Options:
(A) Amy finished last
(B) Eli finished last
(C) Eve finished last
(B)
The following paragraphs each describe a set of three objects arranged in a fixed order. The statements are logically consistent within each paragraph. On a shelf, there are three books: a white book, a green book, and an orange book. The green book is to the right of the white book. The orange book is the rightmost.
Options:
(A) The white book is the leftmost
(B) The green book is the leftmost
(C) The orange book is the leftmost
(A)
The following paragraphs each describe a set of three objects arranged in a fixed order. The statements are logically consistent within each paragraph. On a shelf, there are three books: a red book, a gray book, and a white book. The white book is to the left of the gray book. The red book is the second from the left.
Options:
(A) The red book is the leftmost
(B) The gray book is the leftmost
(C) The white book is the leftmost
(C)
acc_norm
mean
multiple_choice
leaderboard_bbh_logical_deduction_three_objects
SaylorTwift/bbh
logical_deduction_three_objects
test
Q: {{input}}
A:
{{target}}
(A) (B) (C)
A logical deduction task which requires deducing the order of a sequence of objects.

first_n
The following paragraphs each describe a set of three objects arranged in a fixed order. The statements are logically consistent within each paragraph. In a golf tournament, there were three golfers: Amy, Eli, and Eve. Eve finished above Amy. Eli finished below Amy.
Options:
(A) Amy finished last
(B) Eli finished last
(C) Eve finished last
(B)
The following paragraphs each describe a set of three objects arranged in a fixed order. The statements are logically consistent within each paragraph. On a shelf, there are three books: a white book, a green book, and an orange book. The green book is to the right of the white book. The orange book is the rightmost.
Options:
(A) The white book is the leftmost
(B) The green book is the leftmost
(C) The orange book is the leftmost
(A)
The following paragraphs each describe a set of three objects arranged in a fixed order. The statements are logically consistent within each paragraph. On a shelf, there are three books: a red book, a gray book, and a white book. The white book is to the left of the gray book. The red book is the second from the left.
Options:
(A) The red book is the leftmost
(B) The gray book is the leftmost
(C) The white book is the leftmost
(C)
acc_norm
mean
multiple_choice
leaderboard_bbh_movie_recommendation
SaylorTwift/bbh
movie_recommendation
test
Q: {{input}}
A:
{{target}}
(A) (B) (C) (D) (E) (F)
Recommend movies similar to the given list of movies.

first_n
Find a movie similar to Star Wars Episode IV - A New Hope, Indiana Jones and the Last Crusade, Star Wars Episode V - The Empire Strikes Back, The Big Lebowski:
Options:
(A) Tetsuo
(B) the Ironman
(C) The Princess Bride
(D) The Barkley Marathons The Race That Eats Its Young
(E) Bug
(C)
Find a movie similar to Twister, The Silence of the Lambs, Independence Day, Braveheart:
Options:
(A) They Shoot Horses
(B) Don't They
(C) Forrest Gump
(D) The Salton Sea
(E) Extreme Days
(C)
Find a movie similar to Minority Report, Total Recall, Inside Out, Forrest Gump:
Options:
(A) Phenomena
(B) Lilting
(C) Catwoman
(D) Edge of Tomorrow
(D)
acc_norm
mean
multiple_choice
leaderboard_bbh_navigate
SaylorTwift/bbh
navigate
test
Q: {{input}}
A:
{{target}}
Yes No
Given a series of navigation instructions, determine whether one would end up back at the starting point.

first_n
If you follow these instructions, do you return to the starting point? Turn left. Turn around. Turn left. Take 7 steps. Take 2 steps. Take 4 steps. Take 8 steps.
Options:
- Yes
- No
No
If you follow these instructions, do you return to the starting point? Turn around. Take 1 step. Take 6 steps. Turn around. Take 6 steps. Take 9 steps. Take 1 step.
Options:
- Yes
- No
No
If you follow these instructions, do you return to the starting point? Always face forward. Take 2 steps right. Take 9 steps left. Take 7 steps right.
Options:
- Yes
- No
Yes
acc_norm
mean
multiple_choice
leaderboard_bbh_object_counting
SaylorTwift/bbh
object_counting
test
Q: {{input}}
A:
{{target}}
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Questions that involve enumerating objects and asking the model to count them.

first_n
I have a blackberry, a clarinet, a nectarine, a plum, a strawberry, a banana, a flute, an orange, and a violin. How many fruits do I have?
6
I have an orange, a raspberry, two peaches, a blackberry, an apple, a grape, a nectarine, and three plums. How many fruits do I have?
11
I have a lettuce head, a head of broccoli, an onion, a stalk of celery, two carrots, a garlic, and a yam. How many vegetables do I have?
8
acc_norm
mean
multiple_choice
leaderboard_bbh_penguins_in_a_table
SaylorTwift/bbh
penguins_in_a_table
test
Q: {{input}}
A:
{{target}}
(A) (B) (C) (D) (E)
Answer questions about a table of penguins and their attributes.

first_n
Here is a table where the first line is a header and each subsequent line is a penguin:  name, age, height (cm), weight (kg) Louis, 7, 50, 11 Bernard, 5, 80, 13 Vincent, 9, 60, 11 Gwen, 8, 70, 15  For example: the age of Louis is 7, the weight of Gwen is 15 kg, the height of Bernard is 80 cm.  We now add a penguin to the table:
James, 12, 90, 12
How many penguins are less than 8 years old?
Options:
(A) 1
(B) 2
(C) 3
(D) 4
(E) 5
(B)
Here is a table where the first line is a header and each subsequent line is a penguin:  name, age, height (cm), weight (kg) Louis, 7, 50, 11 Bernard, 5, 80, 13 Vincent, 9, 60, 11 Gwen, 8, 70, 15  For example: the age of Louis is 7, the weight of Gwen is 15 kg, the height of Bernard is 80 cm.  Which is the youngest penguin?
Options:
(A) Louis
(B) Bernard
(C) Vincent
(D) Gwen
(E) James
(B)
Here is a table where the first line is a header and each subsequent line is a penguin:  name, age, height (cm), weight (kg) Louis, 7, 50, 11 Bernard, 5, 80, 13 Vincent, 9, 60, 11 Gwen, 8, 70, 15  For example: the age of Louis is 7, the weight of Gwen is 15 kg, the height of Bernard is 80 cm.  What is the name of the second penguin sorted by alphabetic order?
Options:
(A) Louis
(B) Bernard
(C) Vincent
(D) Gwen
(E) James
(D)
acc_norm
mean
multiple_choice
leaderboard_bbh_reasoning_about_colored_objects
SaylorTwift/bbh
reasoning_about_colored_objects
test
Q: {{input}}
A:
{{target}}
(A) (B) (C) (D) (E) (F) (G) (H) (I) (J) (K) (L) (M) (N) (O) (P) (Q) (R)
Answer extremely simple questions about the colors of objects on a surface.

first_n
On the nightstand, there is a red pencil, a purple mug, a burgundy keychain, a fuchsia teddy bear, a black plate, and a blue stress ball. What color is the stress ball?
Options:
(A) red
(B) orange
(C) yellow
(D) green
(E) blue
(F) brown
(G) magenta
(H) fuchsia
(I) mauve
(J) teal
(K) turquoise
(L) burgundy
(M) silver
(N) gold
(O) black
(P) grey
(Q) purple
(R) pink
(E)
On the table, you see a bunch of objects arranged in a row: a purple paperclip, a pink stress ball, a brown keychain, a green scrunchiephone charger, a mauve fidget spinner, and a burgundy pen. What is the color of the object directly to the right of the stress ball?
Options:
(A) red
(B) orange
(C) yellow
(D) green
(E) blue
(F) brown
(G) magenta
(H) fuchsia
(I) mauve
(J) teal
(K) turquoise
(L) burgundy
(M) silver
(N) gold
(O) black
(P) grey
(Q) purple
(R) pink
(F)
On the nightstand, you see the following items arranged in a row: a teal plate, a burgundy keychain, a yellow scrunchiephone charger, an orange mug, a pink notebook, and a grey cup. How many non-orange items do you see to the left of the teal item?
Options:
(A) zero
(B) one
(C) two
(D) three
(E) four
(F) five
(G) six
(A)
acc_norm
mean
multiple_choice
leaderboard_bbh_ruin_names
SaylorTwift/bbh
ruin_names
test
Q: {{input}}
A:
{{target}}
(A) (B) (C) (D) (E) (F)
Select the humorous edit that 'ruins' the input movie or musical artist name.

first_n
Which of the following is a humorous edit of this artist or movie name: 'whitesnake'?
Options:
(A) whitesnape
(B) whitesnapke
(C) whitesnuake
(D) mwhitesnake
(A)
Which of the following is a humorous edit of this artist or movie name: 'one of our dinosaurs is missing'?
Options:
(A) ofne of our dinosaurs is missing
(B) one af our dinosaurs is missing
(C) one of our dinosaurs is pissing
(D) one of our dinosaur is missing
(C)
Which of the following is a humorous edit of this artist or movie name: 'counting crows'?
Options:
(A) countingy crows
(B) counting cows
(C) courting crows
(D) coutnting crows
(B)
acc_norm
mean
multiple_choice
leaderboard_bbh_salient_translation_error_detection
SaylorTwift/bbh
salient_translation_error_detection
test
Q: {{input}}
A:
{{target}}
(A) (B) (C) (D) (E) (F)
Detect the type of error in an English translation of a German source sentence.

first_n
The following translations from German to English contain a particular error. That error will be one of the following types: Named Entities: An entity (names, places, locations, etc.) is changed to a different entity. Numerical Values: Numerical values (ordinals or cardinals), dates, and/or units are changed. Modifiers or Adjectives: The modifiers and adjectives pertaining to a noun are changed. Negation or Antonyms: Introduce or remove a negation or change comparatives to their antonyms. Facts: Trivial factual errors not pertaining to the above classes are introduced in the translations. Dropped Content: A significant clause in the translation is removed. Please identify that error.  Source: In der Liste der Baudenkmale in Lenzen (Elbe) sind alle Baudenkmale der brandenburgischen Stadt Lenzen (Elbe) und ihrer Ortsteile aufgelistet.
Translation: In the list of architectural monuments in Lenzen all architectural monuments of the Brandenburg city of Lenzen and its districts are listed.
The translation contains an error pertaining to
Options:
(A) Modifiers or Adjectives
(B) Numerical Values
(C) Negation or Antonyms
(D) Named Entities
(E) Dropped Content
(F) Facts
(D)
The following translations from German to English contain a particular error. That error will be one of the following types: Named Entities: An entity (names, places, locations, etc.) is changed to a different entity. Numerical Values: Numerical values (ordinals or cardinals), dates, and/or units are changed. Modifiers or Adjectives: The modifiers and adjectives pertaining to a noun are changed. Negation or Antonyms: Introduce or remove a negation or change comparatives to their antonyms. Facts: Trivial factual errors not pertaining to the above classes are introduced in the translations. Dropped Content: A significant clause in the translation is removed. Please identify that error.  Source: Auf dieser Seite sind die Baudenkmäler der oberbayerischen Großen Kreisstadt Landsberg am Lech zusammengestellt.
Translation: On this page are compiled the architectural monuments of the town of Landsberg am Lech.
The translation contains an error pertaining to
Options:
(A) Modifiers or Adjectives
(B) Numerical Values
(C) Negation or Antonyms
(D) Named Entities
(E) Dropped Content
(F) Facts
(E)
The following translations from German to English contain a particular error. That error will be one of the following types: Named Entities: An entity (names, places, locations, etc.) is changed to a different entity. Numerical Values: Numerical values (ordinals or cardinals), dates, and/or units are changed. Modifiers or Adjectives: The modifiers and adjectives pertaining to a noun are changed. Negation or Antonyms: Introduce or remove a negation or change comparatives to their antonyms. Facts: Trivial factual errors not pertaining to the above classes are introduced in the translations. Dropped Content: A significant clause in the translation is removed. Please identify that error.  Source: Łeba ist eine Kleinstadt und ein Badeort im Powiat Lęborski der polnischen Woiwodschaft Pommern.
Translation: Eba is not a small town and seaside resort in the Powiat Léborski county of the Pomeranian Voivodeship of Poland.
The translation contains an error pertaining to
Options:
(A) Modifiers or Adjectives
(B) Numerical Values
(C) Negation or Antonyms
(D) Named Entities
(E) Dropped Content
(F) Facts
(C)
acc_norm
mean
multiple_choice
leaderboard_bbh_snarks
SaylorTwift/bbh
snarks
test
Q: {{input}}
A:
{{target}}
(A) (B)
Determine which of two sentences is sarcastic.

According to Cambridge University Dictionary, sarcasm is "the use of remarks that clearly mean the opposite of what they say, made in order to hurt someone's feelings or to criticize something in a humorous way." Sarcastic sentences often contain satirical or ironic utterances, hyperboles, ambivalent or witty remarks.


first_n
Which statement is sarcastic?
Options:
(A) Yes, because having interests and actively researching them is a huge waste
(B) Yes, because having interests and actively researching them is a huge deal
(A)
Which statement is sarcastic?
Options:
(A) No one is going to disagree with you on this. Avoiding ad hominem attacks really help your case
(B) No one is going to disagree with you on this. Ad hominem attacks really help your case
(B)
Which statement is sarcastic?
Options:
(A) Consistency in the league's punishments? What do you think this is supposed to be, politics?
(B) Consistency in the league's punishments? What do you think this is supposed to be, moral?
(A)
acc_norm
mean
multiple_choice
leaderboard_bbh_sports_understanding
SaylorTwift/bbh
sports_understanding
test
Q: {{input}}
A:
{{target}}
yes no
Determine whether an artificially constructed sentence relating to sports is plausible or not.

first_n
Is the following sentence plausible? "Bam Adebayo scored a reverse layup in the Western Conference Finals."
yes
Is the following sentence plausible? "Santi Cazorla scored a touchdown."
no
Is the following sentence plausible? "DeMar DeRozan was called for the goal tend."
yes
acc_norm
mean
multiple_choice
leaderboard_bbh_temporal_sequences
SaylorTwift/bbh
temporal_sequences
test
Q: {{input}}
A:
{{target}}
(A) (B) (C) (D)
Task description: Answer questions about which times certain events could have occurred.

first_n
Today, Emily went to the museum. Between what times could they have gone?
We know that:
Emily woke up at 1pm.
Elizabeth saw Emily reading at the library from 2pm to 4pm.
Jessica saw Emily watching a movie at the theater from 4pm to 5pm.
Leslie saw Emily waiting at the airport from 5pm to 6pm.
William saw Emily buying clothes at the mall from 6pm to 7pm.
The museum was closed after 7pm.
Between what times could Emily have gone to the museum?
Options:
(A) 1pm to 2pm
(B) 6pm to 7pm
(C) 5pm to 6pm
(D) 2pm to 4pm
(A)
Today, Elizabeth went to the amusement park. Between what times could they have gone?
We know that:
Elizabeth woke up at 7am.
David saw Elizabeth fixing their computer at the electronic store from 1pm to 2pm.
Sarah saw Elizabeth playing tennis at the tennis court from 2pm to 3pm.
Susan saw Elizabeth walking towards the Statue of Liberty from 3pm to 6pm.
Andrew saw Elizabeth taking photos near the Eiffel Tower from 6pm to 9pm.
Emily saw Elizabeth getting a coffee at the cafe from 9pm to 10pm.
The amusement park was closed after 10pm.
Between what times could Elizabeth have gone to the amusement park?
Options:
(A) 7am to 1pm
(B) 9pm to 10pm
(C) 1pm to 2pm
(D) 3pm to 6pm
(A)
Today, Tiffany went to the beach. Between what times could they have gone?
We know that:
Tiffany woke up at 5am.
Betty saw Tiffany getting a coffee at the cafe from 5am to 6am.
Jessica saw Tiffany working at the office from 6am to 9am.
John saw Tiffany stretching at a yoga studio from 9am to 12pm.
Sean saw Tiffany sitting on a rooftop from 12pm to 2pm.
Sarah saw Tiffany playing tennis at the tennis court from 2pm to 3pm.
The beach was closed after 4pm.
Between what times could Tiffany have gone to the beach?
Options:
(A) 9am to 12pm
(B) 12pm to 2pm
(C) 5am to 6am
(D) 3pm to 4pm
(D)
acc_norm
mean
multiple_choice
leaderboard_bbh_tracking_shuffled_objects_five_objects
SaylorTwift/bbh
tracking_shuffled_objects_five_objects
test
Q: {{input}}
A:
{{target}}
(A) (B) (C) (D) (E)
A task requiring determining the final positions of a set of objects given their initial positions and a description of a sequence of swaps.

first_n
Alice, Bob, and Claire are playing a game. At the start of the game, they are each holding a ball: Alice has a yellow ball, Bob has a blue ball, and Claire has a pink ball.
As the game progresses, pairs of players trade balls. First, Claire and Alice swap balls. Then, Alice and Bob swap balls. Finally, Claire and Bob swap balls. At the end of the game, Bob has the
Options:
(A) yellow ball
(B) blue ball
(C) pink ball
(A)
Alice, Bob, and Claire are playing a game. At the start of the game, they are each holding a ball: Alice has a white ball, Bob has a purple ball, and Claire has a pink ball.
As the game progresses, pairs of players trade balls. First, Bob and Alice swap balls. Then, Bob and Claire swap balls. Finally, Bob and Alice swap balls. At the end of the game, Alice has the
Options:
(A) white ball
(B) purple ball
(C) pink ball
(C)
Alice, Bob, and Claire are dancers at a square dance. At the start of a song, they each have a partner: Alice is dancing with Lola, Bob is dancing with Rodrigo, and Claire is dancing with Patrick.
Throughout the song, the dancers often trade partners. First, Alice and Bob switch partners. Then, Claire and Bob switch partners. Finally, Bob and Alice switch partners. At the end of the dance, Alice is dancing with
Options:
(A) Lola
(B) Rodrigo
(C) Patrick
(C)
acc_norm
mean
multiple_choice
leaderboard_bbh_tracking_shuffled_objects_seven_objects
SaylorTwift/bbh
tracking_shuffled_objects_seven_objects
test
Q: {{input}}
A:
{{target}}
(A) (B) (C) (D) (E) (F) (G)
A task requiring determining the final positions of a set of objects given their initial positions and a description of a sequence of swaps.

first_n
Alice, Bob, and Claire are playing a game. At the start of the game, they are each holding a ball: Alice has a yellow ball, Bob has a blue ball, and Claire has a pink ball.
As the game progresses, pairs of players trade balls. First, Claire and Alice swap balls. Then, Alice and Bob swap balls. Finally, Claire and Bob swap balls. At the end of the game, Bob has the
Options:
(A) yellow ball
(B) blue ball
(C) pink ball
(A)
Alice, Bob, and Claire are playing a game. At the start of the game, they are each holding a ball: Alice has a white ball, Bob has a purple ball, and Claire has a pink ball.
As the game progresses, pairs of players trade balls. First, Bob and Alice swap balls. Then, Bob and Claire swap balls. Finally, Bob and Alice swap balls. At the end of the game, Alice has the
Options:
(A) white ball
(B) purple ball
(C) pink ball
(C)
Alice, Bob, and Claire are dancers at a square dance. At the start of a song, they each have a partner: Alice is dancing with Lola, Bob is dancing with Rodrigo, and Claire is dancing with Patrick.
Throughout the song, the dancers often trade partners. First, Alice and Bob switch partners. Then, Claire and Bob switch partners. Finally, Bob and Alice switch partners. At the end of the dance, Alice is dancing with
Options:
(A) Lola
(B) Rodrigo
(C) Patrick
(C)
acc_norm
mean
multiple_choice
leaderboard_bbh_tracking_shuffled_objects_three_objects
SaylorTwift/bbh
tracking_shuffled_objects_three_objects
test
Q: {{input}}
A:
{{target}}
(A) (B) (C)
A task requiring determining the final positions of a set of objects given their initial positions and a description of a sequence of swaps.

first_n
Alice, Bob, and Claire are playing a game. At the start of the game, they are each holding a ball: Alice has a yellow ball, Bob has a blue ball, and Claire has a pink ball.
As the game progresses, pairs of players trade balls. First, Claire and Alice swap balls. Then, Alice and Bob swap balls. Finally, Claire and Bob swap balls. At the end of the game, Bob has the
Options:
(A) yellow ball
(B) blue ball
(C) pink ball
(A)
Alice, Bob, and Claire are playing a game. At the start of the game, they are each holding a ball: Alice has a white ball, Bob has a purple ball, and Claire has a pink ball.
As the game progresses, pairs of players trade balls. First, Bob and Alice swap balls. Then, Bob and Claire swap balls. Finally, Bob and Alice swap balls. At the end of the game, Alice has the
Options:
(A) white ball
(B) purple ball
(C) pink ball
(C)
Alice, Bob, and Claire are dancers at a square dance. At the start of a song, they each have a partner: Alice is dancing with Lola, Bob is dancing with Rodrigo, and Claire is dancing with Patrick.
Throughout the song, the dancers often trade partners. First, Alice and Bob switch partners. Then, Claire and Bob switch partners. Finally, Bob and Alice switch partners. At the end of the dance, Alice is dancing with
Options:
(A) Lola
(B) Rodrigo
(C) Patrick
(C)
acc_norm
mean
multiple_choice
leaderboard_bbh_web_of_lies
SaylorTwift/bbh
web_of_lies
test
Q: {{input}}
A:
{{target}}
Yes No
Evaluate a random boolean function expressed as a word problem.

first_n
Question: Fidel tells the truth. Jerry says Fidel tells the truth. Vina says Jerry tells the truth. Millicent says Vina lies. Raymond says Millicent lies. Does Raymond tell the truth?
Yes
Question: Kristian lies. Millie says Kristian lies. Maybelle says Millie tells the truth. Fidel says Maybelle lies. Leda says Fidel lies. Does Leda tell the truth?
Yes
Question: Kristian tells the truth. Michaela says Kristian lies. Raymond says Michaela tells the truth. Osvaldo says Raymond tells the truth. Jamey says Osvaldo tells the truth. Does Jamey tell the truth?
No
acc_norm
mean
multiple_choice
leaderboard_gpqa_diamond
Idavidrein/gpqa
gpqa_diamond
train
train
def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:
    def _process_doc(doc):
        choices = [
            preprocess(doc["Incorrect Answer 1"]),
            preprocess(doc["Incorrect Answer 2"]),
            preprocess(doc["Incorrect Answer 3"]),
            preprocess(doc["Correct Answer"]),
        ]

        random.shuffle(choices)
        correct_answer_index = choices.index(preprocess(doc["Correct Answer"]))

        out_doc = {
            "choice1": choices[0],
            "choice2": choices[1],
            "choice3": choices[2],
            "choice4": choices[3],
            "answer": f"({chr(65 + correct_answer_index)})",
        }
        return out_doc

    return dataset.map(_process_doc)

What is the correct answer to this question:{{Question}}
Choices:
(A) {{choice1}}
(B) {{choice2}}
(C) {{choice3}}
(D) {{choice4}}
Answer: 
answer
(A) (B) (C) (D)
first_n
acc_norm
mean
multiple_choice
leaderboard_gpqa_extended
Idavidrein/gpqa
gpqa_extended
train
train
def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:
    def _process_doc(doc):
        choices = [
            preprocess(doc["Incorrect Answer 1"]),
            preprocess(doc["Incorrect Answer 2"]),
            preprocess(doc["Incorrect Answer 3"]),
            preprocess(doc["Correct Answer"]),
        ]

        random.shuffle(choices)
        correct_answer_index = choices.index(preprocess(doc["Correct Answer"]))

        out_doc = {
            "choice1": choices[0],
            "choice2": choices[1],
            "choice3": choices[2],
            "choice4": choices[3],
            "answer": f"({chr(65 + correct_answer_index)})",
        }
        return out_doc

    return dataset.map(_process_doc)

What is the correct answer to this question:{{Question}}
Choices:
(A) {{choice1}}
(B) {{choice2}}
(C) {{choice3}}
(D) {{choice4}}
Answer: 
answer
(A) (B) (C) (D)
first_n
acc_norm
mean
multiple_choice
leaderboard_gpqa_main
Idavidrein/gpqa
gpqa_main
train
train
def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:
    def _process_doc(doc):
        choices = [
            preprocess(doc["Incorrect Answer 1"]),
            preprocess(doc["Incorrect Answer 2"]),
            preprocess(doc["Incorrect Answer 3"]),
            preprocess(doc["Correct Answer"]),
        ]

        random.shuffle(choices)
        correct_answer_index = choices.index(preprocess(doc["Correct Answer"]))

        out_doc = {
            "choice1": choices[0],
            "choice2": choices[1],
            "choice3": choices[2],
            "choice4": choices[3],
            "answer": f"({chr(65 + correct_answer_index)})",
        }
        return out_doc

    return dataset.map(_process_doc)

What is the correct answer to this question:{{Question}}
Choices:
(A) {{choice1}}
(B) {{choice2}}
(C) {{choice3}}
(D) {{choice4}}
Answer: 
answer
(A) (B) (C) (D)
first_n
acc_norm
mean
multiple_choice
leaderboard_ifeval
wis-k/instruction-following-eval
train
prompt
def process_results(doc, results):
    inp = InputExample(
        key=doc["key"],
        instruction_id_list=doc["instruction_id_list"],
        prompt=doc["prompt"],
        kwargs=doc["kwargs"],
    )
    response = results[0]

    out_strict = test_instruction_following_strict(inp, response)
    out_loose = test_instruction_following_loose(inp, response)

    return {
        "prompt_level_strict_acc": out_strict.follow_all_instructions,
        "inst_level_strict_acc": out_strict.follow_instruction_list,
        "prompt_level_loose_acc": out_loose.follow_all_instructions,
        "inst_level_loose_acc": out_loose.follow_instruction_list,
    }

first_n
prompt_level_strict_acc
mean
inst_level_strict_acc
def agg_inst_level_acc(items):
    flat_items = [item for sublist in items for item in sublist]
    inst_level_acc = sum(flat_items) / len(flat_items)
    return inst_level_acc

prompt_level_loose_acc
mean
inst_level_loose_acc
def agg_inst_level_acc(items):
    flat_items = [item for sublist in items for item in sublist]
    inst_level_acc = sum(flat_items) / len(flat_items)
    return inst_level_acc

generate_until
leaderboard_math_algebra_hard
lighteval/MATH-Hard
algebra
train
test
def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:
    def _process_doc(doc: dict) -> dict:
        out_doc = {
            "problem": doc["problem"],
            "solution": doc["solution"],
            "answer": normalize_final_answer(
                remove_boxed(last_boxed_only_string(doc["solution"]))
            ),
        }
        if getattr(doc, "few_shot", None) is not None:
            out_doc["few_shot"] = True
        return out_doc

    return dataset.map(_process_doc)

def doc_to_text(doc: dict) -> str:
    return "Problem:" + "\n" + doc["problem"] + "\n\n" + "Solution:"

{{answer if few_shot is undefined else solution}}
def process_results(doc: dict, results: List[str]) -> Dict[str, int]:
    candidates = results[0]

    unnormalized_answer = get_unnormalized_answer(candidates)
    answer = normalize_final_answer(unnormalized_answer)

    if answer == INVALID_ANSWER:
        return {"exact_match": 0}

    if answer.strip() == doc["answer"].strip() or is_equiv(answer, doc["answer"]):
        retval = 1
    else:
        retval = 0

    results = {
        "exact_match": retval,
    }
    return results

first_n
<function list_fewshot_samples at 0x7f6a85d980d0>
exact_match
mean
generate_until
Problem:
leaderboard_math_counting_and_prob_hard
lighteval/MATH-Hard
counting_and_probability
train
test
def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:
    def _process_doc(doc: dict) -> dict:
        out_doc = {
            "problem": doc["problem"],
            "solution": doc["solution"],
            "answer": normalize_final_answer(
                remove_boxed(last_boxed_only_string(doc["solution"]))
            ),
        }
        if getattr(doc, "few_shot", None) is not None:
            out_doc["few_shot"] = True
        return out_doc

    return dataset.map(_process_doc)

def doc_to_text(doc: dict) -> str:
    return "Problem:" + "\n" + doc["problem"] + "\n\n" + "Solution:"

{{answer if few_shot is undefined else solution}}
def process_results(doc: dict, results: List[str]) -> Dict[str, int]:
    candidates = results[0]

    unnormalized_answer = get_unnormalized_answer(candidates)
    answer = normalize_final_answer(unnormalized_answer)

    if answer == INVALID_ANSWER:
        return {"exact_match": 0}

    if answer.strip() == doc["answer"].strip() or is_equiv(answer, doc["answer"]):
        retval = 1
    else:
        retval = 0

    results = {
        "exact_match": retval,
    }
    return results

first_n
<function list_fewshot_samples at 0x7f6a85d6fb50>
exact_match
mean
generate_until
Problem:
leaderboard_math_geometry_hard
lighteval/MATH-Hard
geometry
train
test
def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:
    def _process_doc(doc: dict) -> dict:
        out_doc = {
            "problem": doc["problem"],
            "solution": doc["solution"],
            "answer": normalize_final_answer(
                remove_boxed(last_boxed_only_string(doc["solution"]))
            ),
        }
        if getattr(doc, "few_shot", None) is not None:
            out_doc["few_shot"] = True
        return out_doc

    return dataset.map(_process_doc)

def doc_to_text(doc: dict) -> str:
    return "Problem:" + "\n" + doc["problem"] + "\n\n" + "Solution:"

{{answer if few_shot is undefined else solution}}
def process_results(doc: dict, results: List[str]) -> Dict[str, int]:
    candidates = results[0]

    unnormalized_answer = get_unnormalized_answer(candidates)
    answer = normalize_final_answer(unnormalized_answer)

    if answer == INVALID_ANSWER:
        return {"exact_match": 0}

    if answer.strip() == doc["answer"].strip() or is_equiv(answer, doc["answer"]):
        retval = 1
    else:
        retval = 0

    results = {
        "exact_match": retval,
    }
    return results

first_n
<function list_fewshot_samples at 0x7f6a85ee9c60>
exact_match
mean
generate_until
Problem:
leaderboard_math_intermediate_algebra_hard
lighteval/MATH-Hard
intermediate_algebra
train
test
def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:
    def _process_doc(doc: dict) -> dict:
        out_doc = {
            "problem": doc["problem"],
            "solution": doc["solution"],
            "answer": normalize_final_answer(
                remove_boxed(last_boxed_only_string(doc["solution"]))
            ),
        }
        if getattr(doc, "few_shot", None) is not None:
            out_doc["few_shot"] = True
        return out_doc

    return dataset.map(_process_doc)

def doc_to_text(doc: dict) -> str:
    return "Problem:" + "\n" + doc["problem"] + "\n\n" + "Solution:"

{{answer if few_shot is undefined else solution}}
def process_results(doc: dict, results: List[str]) -> Dict[str, int]:
    candidates = results[0]

    unnormalized_answer = get_unnormalized_answer(candidates)
    answer = normalize_final_answer(unnormalized_answer)

    if answer == INVALID_ANSWER:
        return {"exact_match": 0}

    if answer.strip() == doc["answer"].strip() or is_equiv(answer, doc["answer"]):
        retval = 1
    else:
        retval = 0

    results = {
        "exact_match": retval,
    }
    return results

first_n
<function list_fewshot_samples at 0x7f6a85d6d480>
exact_match
mean
generate_until
Problem:
leaderboard_math_num_theory_hard
lighteval/MATH-Hard
number_theory
train
test
def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:
    def _process_doc(doc: dict) -> dict:
        out_doc = {
            "problem": doc["problem"],
            "solution": doc["solution"],
            "answer": normalize_final_answer(
                remove_boxed(last_boxed_only_string(doc["solution"]))
            ),
        }
        if getattr(doc, "few_shot", None) is not None:
            out_doc["few_shot"] = True
        return out_doc

    return dataset.map(_process_doc)

def doc_to_text(doc: dict) -> str:
    return "Problem:" + "\n" + doc["problem"] + "\n\n" + "Solution:"

{{answer if few_shot is undefined else solution}}
def process_results(doc: dict, results: List[str]) -> Dict[str, int]:
    candidates = results[0]

    unnormalized_answer = get_unnormalized_answer(candidates)
    answer = normalize_final_answer(unnormalized_answer)

    if answer == INVALID_ANSWER:
        return {"exact_match": 0}

    if answer.strip() == doc["answer"].strip() or is_equiv(answer, doc["answer"]):
        retval = 1
    else:
        retval = 0

    results = {
        "exact_match": retval,
    }
    return results

first_n
<function list_fewshot_samples at 0x7f6a85d3ad40>
exact_match
mean
generate_until
Problem:
leaderboard_math_prealgebra_hard
lighteval/MATH-Hard
prealgebra
train
test
def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:
    def _process_doc(doc: dict) -> dict:
        out_doc = {
            "problem": doc["problem"],
            "solution": doc["solution"],
            "answer": normalize_final_answer(
                remove_boxed(last_boxed_only_string(doc["solution"]))
            ),
        }
        if getattr(doc, "few_shot", None) is not None:
            out_doc["few_shot"] = True
        return out_doc

    return dataset.map(_process_doc)

def doc_to_text(doc: dict) -> str:
    return "Problem:" + "\n" + doc["problem"] + "\n\n" + "Solution:"

{{answer if few_shot is undefined else solution}}
def process_results(doc: dict, results: List[str]) -> Dict[str, int]:
    candidates = results[0]

    unnormalized_answer = get_unnormalized_answer(candidates)
    answer = normalize_final_answer(unnormalized_answer)

    if answer == INVALID_ANSWER:
        return {"exact_match": 0}

    if answer.strip() == doc["answer"].strip() or is_equiv(answer, doc["answer"]):
        retval = 1
    else:
        retval = 0

    results = {
        "exact_match": retval,
    }
    return results

first_n
<function list_fewshot_samples at 0x7f6a85d38550>
exact_match
mean
generate_until
Problem:
leaderboard_math_precalculus_hard
lighteval/MATH-Hard
precalculus
train
test
def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:
    def _process_doc(doc: dict) -> dict:
        out_doc = {
            "problem": doc["problem"],
            "solution": doc["solution"],
            "answer": normalize_final_answer(
                remove_boxed(last_boxed_only_string(doc["solution"]))
            ),
        }
        if getattr(doc, "few_shot", None) is not None:
            out_doc["few_shot"] = True
        return out_doc

    return dataset.map(_process_doc)

def doc_to_text(doc: dict) -> str:
    return "Problem:" + "\n" + doc["problem"] + "\n\n" + "Solution:"

{{answer if few_shot is undefined else solution}}
def process_results(doc: dict, results: List[str]) -> Dict[str, int]:
    candidates = results[0]

    unnormalized_answer = get_unnormalized_answer(candidates)
    answer = normalize_final_answer(unnormalized_answer)

    if answer == INVALID_ANSWER:
        return {"exact_match": 0}

    if answer.strip() == doc["answer"].strip() or is_equiv(answer, doc["answer"]):
        retval = 1
    else:
        retval = 0

    results = {
        "exact_match": retval,
    }
    return results

first_n
<function list_fewshot_samples at 0x7f6a85ee8d30>
exact_match
mean
generate_until
Problem:
leaderboard_mmlu_pro
TIGER-Lab/MMLU-Pro
test
validation
def doc_to_text(doc):
    doc_to_text = f"{doc['question']}\n"

    for i in range(len(doc["options"])):
        doc_to_text += f"{string.ascii_uppercase[i]}. {doc['options'][i]}\n"

    doc_to_text += "Answer:"
    return doc_to_text

answer
def doc_to_choice(doc):
    return [string.ascii_uppercase[i] for i in range(len(doc["options"]))]

first_n
acc
mean
multiple_choice
leaderboard_musr_murder_mysteries
TAUR-Lab/MuSR
murder_mysteries
def doc_to_text(doc):
    """
    Convert a doc to text.
    """
    choices = ""
    for i, choice in enumerate(ast.literal_eval(doc["choices"])):
        choices += f"{i+1} - {choice}\n"

    text = DOC_TO_TEXT.format(
        narrative=doc["narrative"], question=doc["question"], choices=choices
    )

    return text

{{answer_choice}}
{{choices}}
acc_norm
mean
multiple_choice
leaderboard_musr_object_placements
TAUR-Lab/MuSR
object_placements
def doc_to_text(doc):
    """
    Convert a doc to text.
    """
    choices = ""
    for i, choice in enumerate(ast.literal_eval(doc["choices"])):
        choices += f"{i+1} - {choice}\n"

    text = DOC_TO_TEXT.format(
        narrative=doc["narrative"], question=doc["question"], choices=choices
    )

    return text

{{answer_choice}}
{{choices}}
acc_norm
mean
multiple_choice
leaderboard_musr_team_allocation
TAUR-Lab/MuSR
team_allocation
def doc_to_text(doc):
    """
    Convert a doc to text.
    """
    choices = ""
    for i, choice in enumerate(ast.literal_eval(doc["choices"])):
        choices += f"{i+1} - {choice}\n"

    text = DOC_TO_TEXT.format(
        narrative=doc["narrative"], question=doc["question"], choices=choices
    )

    return text

{{answer_choice}}
{{choices}}
acc_norm
mean
multiple_choice
PyTorch version: 2.3.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 10.5.0-1ubuntu1~20.04) 10.5.0
Clang version: Could not collect
CMake version: version 3.27.7
Libc version: glibc-2.31

Python version: 3.10.14 (main, May  6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-1048-aws-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 12.1.105
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA H100 80GB HBM3
Nvidia driver version: 535.104.12
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Byte Order:                         Little Endian
Address sizes:                      48 bits physical, 48 bits virtual
CPU(s):                             96
On-line CPU(s) list:                0-95
Thread(s) per core:                 1
Core(s) per socket:                 48
Socket(s):                          2
NUMA node(s):                       2
Vendor ID:                          AuthenticAMD
CPU family:                         25
Model:                              1
Model name:                         AMD EPYC 7R13 Processor
Stepping:                           1
CPU MHz:                            2650.000
BogoMIPS:                           5300.00
Hypervisor vendor:                  KVM
Virtualization type:                full
L1d cache:                          3 MiB
L1i cache:                          3 MiB
L2 cache:                           48 MiB
L3 cache:                           384 MiB
NUMA node0 CPU(s):                  0-47
NUMA node1 CPU(s):                  48-95
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Mitigation; safe RET
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext perfctr_core invpcid_single ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid

Versions of relevant libraries:
[pip3] mypy==1.13.0
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] onnx==1.14.1
[pip3] onnxruntime==1.19.2
[pip3] torch==2.3.1
[pip3] triton==2.3.1
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] torch                     2.3.1                    pypi_0    pypi
[conda] triton                    2.3.1                    pypi_0    pypi