DataHammer/scimrc huggingface dataset:
Question: When models are trained on datasets of picture description tasks augmented with a conversational speech of healthy speakers learn decision, are there any benefits?
Evidence: 
Alzheimer’s disease (AD) is a neurodegenerative disease affecting over 40 million people worldwide with high costs of acute and long-term care BIBREF0 . Recruitment of participants with cognitive impairment has historically been a bottleneck in clinical trials BIBREF1 , making AD datasets relatively small. Additionally, though cognitive assessments test domains of cognition through multiple tasks, most available datasets of pathological speech are restricted to participants performing a single task. Picture description using an image to elicit narrative discourse samples is one such task that has proved to be successful in detecting AD BIBREF2 . However, it is important to develop ML models of high performance that would produce results generalizable across different tasks.
Several studies have used natural language processing and machine learning to distinguish between healthy and cognitively impaired speech of participants describing a picture. Fraser et al. BIBREF3 used linguistic and acoustic features to classify healthy and pathological speech transcripts with an accuracy of INLINEFORM0 . Similarly, Karlekar et al. BIBREF4 classified utterances of speakers as AD or healthy (HC) with an accuracy of INLINEFORM1 using an enlarged, utterance-level view of transcripts of picture descriptions. In line with previous research, we use linguistic and acoustic features of speech as input to our ML model. Furthermore, we extend the model to using data from several different tasks.
Noorian et al. BIBREF5 demonstrated that using within-task data of healthy participants describing a picture improved AD detection performance by up to 13%. In this paper, we evaluate if model performance improves with the addition of data from healthy participants, with varying ages, performing either the same or different tasks. We find that models trained on datasets of picture description tasks augmented with conversational speech of healthy speakers learn decision boundaries that are more generalizable across activities with lower out-of-task errors. We observe a 9% increase in AD detection performance when normative data from different tasks are utilized. We also analyze if each task provides domain-specific inductive bias for other tasks to obtain a model setting capable of detecting AD from any sample of speech using high-precision model-agnostic explanations proposed by Ribeiro et al. BIBREF6 and computation of various error metrics related to classification.

We find that models trained on datasets of picture description tasks augmented with conversational speech of healthy speakers learn decision boundaries that are more generalizable across activities with lower out-of-task errors.
Answer: They are more generalizable across activities with lower out-of-task errors.
Question: Classify the countries based on the continent they belong to - North America or South America with the former as 'north' and the latter as 'south'. United States, Mexico, Canada, Brazil, Columbia, Argentina, Cuba, Jamaica, Chile, Guyana, Peru.
Answer: 'north', 'north', 'north', 'south', 'south', 'south', 'north', 'north', 'south', 'south', 'south'.
Question: What are the best books of Dostoyevsky?
Answer: 1. 'Crime and Punishment' 
2. 'The Brothers Karamazov' 
3. 'The Idiot' 
4. 'Demons' 
5. ‘Notes from the Underground’
Question: Does the model proposed by the authors perform better than several state-of-the-art GAN baselines?
Evidence: We propose a novel adversarial training framework to deal with the instability problem of current GANs for text generation. To address the instability issue caused by policy gradient, we incorporate RAML into the advesarial training paradigm to make our generator acquire stable rewards. Experiments show that our model performs better than several state-of-the-art GAN baselines with lower training variance, yet producing better performance on three text generation tasks.
We propose a novel adversarial training framework to deal with the instability problem of current GANs for text generation. To address the instability issue caused by policy gradient, we incorporate
Answer: Yes, it does.
Question: Give me a list of the best rappers of all time
Answer: Eminem
Andre 3000
Tupac
Jay-Z
Kanye West
Kendrick Lamar
Lauryn Hill
Nas
Question: What is the Spengler Cup
Answer: The Spengler Cup is an annual invitational ice hockey tournament held in Davos, Switzerland. First held in 1923, the Spengler Cup is often cited as the oldest invitational ice hockey tournament in the world. The event is hosted by the Swiss team HC Davos and played each year in Davos from 26 December to 31 inclusively. Currently, all games are held at the Eisstadion Davos.

It was originally devised by Dr. Carl Spengler as a means to promote teams from German-speaking Europe, who might have suffered ostracism in the aftermath of World War I. Eventually, the tournament grew well beyond expectations. Many of Europe's most prestigious clubs and national programs have appeared, including Soviet, Czechoslovak, Swedish, German, and Finnish powerhouses. Through its history, club or national teams from 13 countries have won the tournament, with Team Canada winning the most cups as a club (16) while various teams from Switzerland have won the most cups for one country (20).

Among non-European organizations, Team Canada, Team USA, nationally ranked U.S. collegiate teams, reigning AHL Calder Cup and Ontario Hockey Association champions, and even Team Japan (in 1971, building international experience before playing as hosts of the 1972 Sapporo Winter Olympics) have competed for the Spengler Cup. Since at least 1990, Team Canada has been the only participant from North America, with the exception of the AHL's Rochester Americans in 1996 and 2013 (see AHL participation at the Spengler Cup).

In the 2018 tournament, Finnish club team KalPa from the city of Kuopio defeated Team Canada 2–1 in the final. The game was decided in the 8th round of a shootout, the first series of game-winning shots in tournament history that determined the winner of the Spengler Cup.

The Spengler Cup tournament was not played in 2020 or 2021 due to the COVID-19 pandemic. In the 2022 tournament, Swiss club team HC Ambrì-Piotta defeated Sparta Prague 3–2 in the final; with the game decided in the 5th round of a shootout.
Question: What are the most expensive cities in the world ?
Answer: This is an exhaustive list of the most expensive cities : Singapore, Tel Aviv,  New York, Hong Kong, Los Angeles,  Zurich, Geneva, San Francisco, Paris and Sydney
Question: Robin Williams dressed in drag for which 1993 film
Answer: Mrs Doubtfire
Question: What experiments are presented?
Evidence: Experiments were carried out by the method proposed in BIBREF27 . The first part is described as Task A, the purpose of which is to identify the boundaries of timexes and assign them to one of the following classes: date, time, duration, set.
We chose the best 3 results from each word embeddings group (EE, EP, EC) from Table TABREF19 presenting F1-scores for all models. Then we evaluated these results using more detailed measures for timexes, presented in BIBREF27 . The following measures were used to evaluate the quality of boundaries and class recognition, so-called strict match: strict precision (Str.P), strict recall (Str.R) and strict F1-score (Str.F1). A relaxed match (Rel.P, Rel.R, Rel.F1) evaluation has also been carried out to determine whether there is an overlap between the system entity and gold entity, e.g. [Sunday] and [Sunday morning] BIBREF27 . If there was an overlap, a relaxed type F1-score (Type.F1) was calculated BIBREF27 . The results are presented in Table TABREF20 .
The first part is described as Task A, the purpose of which is to identify the boundaries of timexes and assign them to one of the following classes: date, time, duration, set.
We chose the best 3 results from each word embeddings group (EE, EP, EC) from Table TABREF19 presenting F1-scores for all models. Then we evaluated these results using more detailed measures for timexes, presented in BIBREF27 .
Answer: The answers are shown as follows:
* identify the boundaries of timexes and assign them to one of the following classes: date, time, duration, set
*  Then we evaluated these results using more detailed measures for timexes
Question: Discuss the pros and cons of a day trip to Vancouver versus a day trip to Portland from Seattle.
Answer: Both Vancouver, Canada and Portland, Oregon can be a fun day trip from Seattle. The pros for both are that the cities are fairly close to Seattle, so a day trip is very reasonable. In terms of time to get there, Portland is a bit closer and also has the advantage of still being in the United States. So, you may save time compared to going to Vancouver since there is no border crossing. This difference can be fairly trivial though since the border between the US and Canada is often fairly fast.

Besides travel time, something to consider is what kind of activity you want to do. Vancouver has a lot of great chinese cuisine and also has Stanley park which is a great place to go walking and cycling. Portland is known for Voodoo donuts, a popular and unique donut store. 

Overall, both are great options and it boils down to what things you enjoy doing most.
Question: When going skydiving, categorize each of the following as 'useful' or 'not useful': a parachute, an Acme anvil, a helmet, a go-pro camera, a small herd of miniature ponies, wraparound sunglasses, a backpack full of uranium
Answer: Useful: a parachute, a helmet, a go-pro camera, wraparound sunglasses
Not useful: an Acme anvil, a small herd of miniature ponies, a backpack full of uranium
Question: Who won the 1974 Nobel Memorial Prize in Economic Sciences?
Evidence: Friedrich August von Hayek CH FBA (/ˈhaɪək/ HY-ək, German: [ˈfʁiːdʁɪç ˈʔaʊɡʊst fɔn ˈhaɪɛk] (listen); 8 May 1899 – 23 March 1992), often referred to by his initials F. A. Hayek, was an Austrian-British intellectual who made contributions to economics, political science, psychology, intellectual history, philosophy and other fields. Hayek shared the 1974 Nobel Memorial Prize in Economic Sciences with Gunnar Myrdal for work on money and economic fluctuations, and the interdependence of economic, social and institutional phenomena. His account of how prices communicate information is widely regarded as an important contribution to economics that led to him receiving the prize
Answer: Friedrich August von Hayek and Gunnar Myrdal shared the 1974 Nobel Memorial Prize in Economic Sciences.
Question: How does KANE capture both high-order structural and attribute information of KGs in an efficient, explicit and unified manner?
Evidence: The process of KANE is illustrated in Figure FIGREF2. We introduce the architecture of KANE from left to right. As shown in Figure FIGREF2, the whole triples of knowledge graph as input. The task of attribute embedding lays is embedding every value in attribute triples into a continuous vector space while preserving the semantic information. To capture both high-order structural information of KGs, we used an attention-based embedding propagation method. This method can recursively propagate the embeddings of entities from an entity's neighbors, and aggregate the neighbors with different weights. The final embedding of entities, relations and values are feed into two different deep neural network for two different tasks including link predication and entity classification.
The task of attribute embedding lays is embedding every value in attribute triples into a continuous vector space while preserving the semantic information. To capture both high-order structural information of KGs, we used an attention-based embedding propagation method.
The final embedding of entities, relations and values are feed into two different deep neural network for two different tasks including link predication and entity classification.
Answer: The answers are shown as follows:
* To capture both high-order structural information of KGs, we used an attention-based embedding propagation method.
Question: What is an ionic bond?
Answer: An ionic bond is a bond where an electron from one atom is transferred to another atom. An example of an ionic bond is NaCl, or salt.
Question: Who were Hank Williams early musical influences?
Evidence: Born and raised in Alabama, Williams learned guitar from African-American blues musician Rufus Payne in exchange for meals or money. Payne, along with Roy Acuff and Ernest Tubb, had a significant influence on Williams' musical style. Williams began his professional career in Montgomery in 1937 when local radio station WSFA hired him to perform on a 15-minute program. He formed the Drifting Cowboys backup band, which was managed by his mother, and dropped out of school to devote his time to his career. When several of his band members were drafted during World War II, he had trouble with their replacements, and WSFA terminated his contract because of his alcoholism.
Answer: As a child Hank Williams was influenced by popular country artists such as Roy Acuff and Earnest Tubb, but he was also influenced by African-American blues musician Rufus Payne. Payne taught Williams how to play guitar, and was a significant influence on his musical style.
Question: How does TP-N2F compare to LSTM-based Seq2Seq in terms of training and inference speed?
Evidence: FLOAT SELECTED: Table 2: Results of AlgoLisp dataset
Generating Lisp programs requires sensitivity to structural information because Lisp code can be regarded as tree-structured. Given a natural-language query, we need to generate code containing function calls with parameters. Each function call is a relational tuple, which has a function as the relation and parameters as arguments. We evaluate our model on the AlgoLisp dataset for this task and achieve state-of-the-art performance. The AlgoLisp dataset BIBREF17 is a program synthesis dataset. Each sample contains a problem description, a corresponding Lisp program tree, and 10 input-output testing pairs. We parse the program tree into a straight-line sequence of tuples (same style as in MathQA). AlgoLisp provides an execution script to run the generated program and has three evaluation metrics: the accuracy of passing all test cases (Acc), the accuracy of passing 50% of test cases (50p-Acc), and the accuracy of generating an exactly matching program (M-Acc). AlgoLisp has about 10% noisy data (details in the Appendix), so we report results both on the full test set and the cleaned test set (in which all noisy testing samples are removed). TP-N2F is compared with an LSTM seq2seq with attention model, the Seq2Tree model in BIBREF17, and a seq2seq model with a pre-trained tree decoder from the Tree2Tree autoencoder (SAPS) reported in BIBREF18. As shown in Table TABREF18, TP-N2F outperforms all existing models on both the full test set and the cleaned test set. Ablation experiments with TP2LSTM and LSTM2TP show that, for this task, the TP-N2F Decoder is more helpful than TP-N2F Encoder. This may be because lisp codes rely more heavily on structure representations.
FLOAT SELECTED: Table 2: Results of AlgoLisp dataset
As shown in Table TABREF18, TP-N2F outperforms all existing models on both the full test set and the cleaned test set.
Answer: Full Testing Set accuracy: 84.02
Cleaned Testing Set accuracy: 93.48
Question: This table lists the change over time in population of a small town in France. Between  which two consecutive entries from the table did the rate of population growth change the most? Explain your math.
Evidence: Historical population
Year	Pop.	±%
1911	752	—    
1962	437	−41.9%
1968	486	+11.2%
1975	398	−18.1%
1982	393	−1.3%
1990	382	−2.8%
1999	395	+3.4%
2008	441	+11.6%
Answer: The rate of population growth changed the most between the entries for 1962 and 1968. Between 1911 and 1962, there was a -41.9% change in population, and between 1962 and 1968 there was a +11.2% change in population. The rate of change of population growth was |(−41.9% - +11.2%)| = 53.1% change, which is larger than the absolute difference between any other two consecutive rows in the table.
Question: Name some TV shows that American actor David Duchovny has been in.
Answer: The X-Files, Californication, Twin Peaks, The Red Shoe Diaries, Aquarius
Question: Give me a list of the core things to focus on when training for a run or race
Answer: The three things you should focus on are:

1) Endurance: eg. how far you can run

2) Speed: eg. how fast you can run your given distance

3) Strength: how well you can power through changes in elevation
Question: Can Solar panels help you maintain power during a storm ?
Answer: It would really depend on how much cloud coverage you are getting but your panels should be able to generate some power.
Question: Extract the awards that Bob Sanders gained throughout his career, and put them in a comma-separated list.
Evidence: Demond "Bob" Sanders (born February 24, 1981) is an American former professional football player who was a safety in the National Football League (NFL) for eight seasons. He played college football for the Iowa Hawkeyes and was drafted by the Indianapolis Colts in the second round of the 2004 NFL Draft. He was named NFL Defensive Player of the Year in 2007 and also played for the San Diego Chargers in his final season in 2011.

Sanders was nicknamed "The Sandman" because of his hard hits and tackles, and was also called "The Eraser" by former Colts' coach Tony Dungy because of his tendency to erase the mistakes of his teammates. He spent the majority of his career battling injuries, having played more than six games in a single season only twice, in 2005 and 2007.
Answer: NFL Defensive Player of the Year in 2007
Question: Tell me which of these States' shapes have exactly four sides: Utah, Colorado, Florida, Washington, California.
Answer: Colorado has exactly four sides in its shape.
Question: Tell me which of the following items are trees and which ones are weeds: aspen, ash, chickweed, crabgrass, oak, almond, dandelion
Answer: tree: aspen, ash, oak, almond
weed: chickweed, crabgrass, dandelion
Question: Which of these four novels has the least in common with the others?

1. Red Harvest by Dashiell Hammett
2. One Hundred Years of Solitude by Gabriel Garcia Marquez
3. The Big Sleep by Raymond Chandler
4. The Hound of the Baskervilles by Sir Arthur Conan Doyle
Answer: One Hundred Years of Solitude has the least in common with the other books. It is part of a genre called Magical Realism, which depicts a world that strongly resembles real life but contains magical aspects that would normally be found in the Fantasy genre. The other three novels are all Detective fiction, and belong to the Hardboiled sub-genre. Hardboiled detective stories typically feature a deeply cynical (and often unlikable) protagonist, organized crime elements, and a prohibition-era setting.
Question: Should a human be happy all the time?
Answer: Answer 1: Happiness is not only subjective but also temporal - what brought you joy 2 years ago might be boring/unpleasant now. So, instead of optimizing for happiness at a given time, it might actually be better to realize what makes us feel down or not content and avoid them. By avoiding such circumstances/conditions, we can be content and maybe that's what need in life: not strive to be happy all the time but avoid what we truly don't like so that happiness takes care of its own.

Answer 2: Yes! Life is too short to get caught in dull moments so a human needs to optimize for maximum happiness. The definition of happiness differs and that's ok; we all live once and we should spend our time looking for new things that make us happier.

Answer 3: No. Most meaningful lessons in life are learned in situations when we're not particularly happy: Loss of a close person, financial break downs, unexpected health conditions. Getting through these hard times is actually makes a person stronger and more prepared for other calamities in life. We should not strive to be happy "all the time" -  instead we should take things as they come. In life, there is a need for moments of discontent as well as a need for moments of joy.
Question: What is the Trans Tahoe Relay?
Answer: Each year there is a race held to swim across Lake Tahoe. The race was created and is still sponsored by The Olympic Club of San Francisco. Teams are created and entered from across the globe but primarily North America. The format of the race is a six person relay; all male, all female. or half male/female. Starting on the Nevada side of the lake each member of the team swims for 30 minutes following the team's boat. After each of the swimmers has completed the 30 minute rotation the swimmers then rotate again every 10 minutes. This is done is sequence until the team reach the other side of the lake in California. The swimmers need to touch one-another when rotating between swimmers and there are no wetsuits or fins allowed.
Question: In sec 4.1, how does the agent predict the task described in the language instructions?
Evidence: We focus on the ALFRED challenge, where an agent is asked to follow human instructions to complete long-horizon household tasks in indoor scenes (simulated in AI2Thor). Each task in ALFRED consists of several subgoals for either navigation (moving in the environment) or object interactions (interacting with at least one object). Language inputs contain a high-level task description and a sequence of low-level step-by-step instructions (each corresponding to a subgoal). The agent is a simulated robot with access to the states of the environment only through a front-view RGB camera with a relatively small field of view. The agent's own state is a 5-tuple (x, y, r, h, o), where x, y are its 2D position, r the horizontal rotation angle, h the vertical camera angles (also called "horizon") and o the type of object held in its hand. The state space of the agent is discrete, with navigation actions: MoveAhead (moving forward by 0.25m), RotateLeft & RotateRight (rotating in the horizontal plane by 90 • ) and LookUp & LookDown (adjusting the horizon by 15
where positive h indicates facing downward. With these discrete actions, the agent has full knowledge of the relative changes ∆x, ∆y, ∆r and ∆h. Each of the 7 object interaction actions (PickUp, Open, Slice, etc.) is parametrized by an binary mask for the target object, which is usually predicted with a pre-trained instance segmentation module. Featuring long-horizon tasks with a range of interactions, the ALFRED challenge evaluates an agent's ability to perform tasks over unseen test scenes, while only allowing ≤1000 steps and ≤10 action failures for each task at inference time.
Language inputs contain a high-level task description and a sequence of low-level step-by-step instructions (each corresponding to a subgoal).
Answer: As introduced in Sec. 3, the input format of tasks in ALFRED consists of a sequence of low-level instructions, each corresponding to a subgoal.
Question: What is the difference between basketball and netball?
Answer: Basketball and netball are similar, but the team numbers, rules, and equipment are different. Basketball has ten players, players can dribble and run with the ball, and the ball and basket are larger. Netball has seven players, players are not allowed to dribble or run with the ball, and the ball and basket are slightly smaller. Netball was originally developed as a women's sport although modern-day men now play netball as well.