DataHammer/scimrc huggingface dataset:
Question: What benchmark datasets are used for the link prediction task?
Evidence: We evaluate our proposed models on three commonly used knowledge graph datasets—WN18RR BIBREF26, FB15k-237 BIBREF18, and YAGO3-10 BIBREF27. Details of these datasets are summarized in Table TABREF18.
We evaluate our proposed models on three commonly used knowledge graph datasets—WN18RR BIBREF26, FB15k-237 BIBREF18, and YAGO3-10 BIBREF27.
Answer: The answers are shown as follows:
* WN18RR
* FB15k-237
* YAGO3-10
Question: Write a poem or journal entry about numbers that have special meaning to you.
Answer: My special number is 2. When I was a child, I would get up early every Saturday morning to go to football training. To be honest with you, I hated the training. I hated the cold, I hated the rain, I hated the mud, and I was never very good at football. 

But I will always remember feeling extremely proud playing a game in summertime. The game was in Slough, and I was playing for a team called Old Windsor Tigers. I wore the number 2 shirt, and had an incredible game, playing centre half. That was the only Man of the Match award I ever won. I'm still terrible at football, but I love the sport as a spectator, and that memory will always be special to me.
Question: Provide me with a list of boy names that are currently popular.
Answer: Boy names that are currently popular include Liam, Noah, Oliver, Elijah, James, William, and Benjamin.
Question: What are common executive roles at large companies?
Answer: Large companies contain different sets of executives. Most companies have a chief executive officer who runs the company. Another common executive title is the Chief Financial Officer, the person responsible for accounting, financial planning and reporting. Chief Technology Officers are generally responsible for building and maintaining technology infrastructure, the code base and driving innovation within the organization. The Chief Marketing Officer is generally responsible for marketing, brand awareness and demand generation within the organization. A Chief People Officer is often responsible for human resources, recruitment and talent management. A Chief Security Officer protects the safety and security of the organization.
Question: Is there any word segmentation for  Vietnamese available at the moment?
Evidence: In general, building corpus is carried out through four stages: (1) choose target of corpus and source of raw data; (2) building a guideline based on linguistics knowledge for annotation; (3) annotating or tagging corpus based on rule set in the guideline; and (4) reviewing corpus to check the consistency issue.
Encoding word segmentation corpus using B-I-O tagset can be applied, where B, I, and O denoted begin of word, inside of word, and others, respectively. For example, the sentence “Megabit trên giây là đơn vị đo tốc đọ truyền dẫn dữ liệu ." (”Megabit per second is a unit to measure the network traffic.” in English) with the word boundary result “Megabit trên giây là đơn_vị đo tốc_độ truyền_dẫn dữ_liệu ." is encoded as “Megabit/B trên/B giây/B là/B đơn/B vị/I đo/B tốc/B độ/I truyền/B dẫn/I dữ/B liệu/I ./O" .
Annotation guidelines can be applied to ensure that annotated corpus has less errors because the manual annotation is applied. Even though there are guidelines for annotating, the available output corpora are still inconsistent. For example, for the Vietnamese Treebank corpus of the VLSP project, Nguyen et al. listed out several Vietnamese word segmentation inconsistencies in the corpus based on POS information and n-gram sequences BIBREF14 .
Currently, there are at least three available word segmentation corpus used in Vietnamese word segmentation studies and systems. Firstly, Dinh et al. built the CADASA corpus from CADASA’s books BIBREF15 . Secondly, Nguyen et al. built vnQTAG corpus from general news articles BIBREF7 . More recently, Ngo et al. introduced the EVBCorpus corpus, which is collected from four sources, news articles, books, law documents, and novels. As a part of EVBCorpus, EVBNews, was annotated common tags in NLP, such as word segmentation, chunker, and named entity BIBREF16 . All of these corpora are collected from news articles or book stories, and they are manually annotated the word boundary tags (as shown in Table I).
Currently, there are at least three available word segmentation corpus used in Vietnamese word segmentation studies and systems.
Answer: Yes.
Question: Tell me which one of the following sports are Olympic sports: karate, snooker, wrestling, tai boxing, football, basketball.
Answer: Karate, freestyle and greco roman wrestling, boxing, football and basketball are Olympic sports.
Question: How to define question entailment by the authors?
Evidence: In the context of QA, the goal of RQE is to retrieve answers to a new question by retrieving entailed questions with associated answers. We therefore define question entailment as:
a question INLINEFORM0 entails a question INLINEFORM1 if every answer to INLINEFORM2 is also a complete or partial answer to INLINEFORM3 .
We present below two examples of consumer health questions INLINEFORM0 and entailed questions INLINEFORM1 :
Example 1 (each answer to the entailed question B1 is a complete answer to A1):
A1: What is the latest news on tennitis, or ringing in the ear, I am 75 years old and have had ringing in the ear since my mid 5os. Thank you. 
B1: What is the latest research on Tinnitus?
Example 2 (each answer to the entailed question B2 is a partial answer to A2):
A2: My mother has been diagnosed with Alzheimer's, my father is not of the greatest health either and is the main caregiver for my mother. My question is where do we start with attempting to help our parents w/ the care giving and what sort of financial options are there out there for people on fixed incomes. 
B2: What resources are available for Alzheimer's caregivers? 
The inclusion of partial answers in the definition of question entailment also allows efficient relaxation of the contextual constraints of the original question INLINEFORM0 to retrieve relevant answers from entailed, but less restricted, questions.
In the context of QA, the goal of RQE is to retrieve answers to a new question by retrieving entailed questions with associated answers. We therefore define question entailment as: • a question A entails a question B if every answer to B is also a complete or partial answer to A.
Answer: A question A entails a question B if every answer to B is also a complete or partial answer to A.
Question: What is a mulligan in Magic the Gathering?
Answer: A mulligan in Magic the Gathering is an action you can take if you are not happy with your opening hand in a game. Depending on the format you play, different rules apply. For competitive one versus one formats, the rule is that you draw an initial hand of seven cards. If you are unhappy with the hand, you can shuffle it back in your deck, and again draw a hand of seven cards. If you are then happy with the hand, you can keep it but you have to choose and put one card from your hand and put it on the bottom of the deck. If you are unhappy, you can mulligan again, but then you'll have to put back two cards, and so forth.
Question: Are UGGs considered fashionable?
Answer: UGGs were originally worn in Australia by surfers and in the 90’s became mainstream. However since then, they’ve became unpopular and even described as “basic girl” footwear.  Recently, their popularity has had a resurgence. While the question of whether they are fashionable is largely debated, the comfort they provide is undeniable.
Question: What has the IETF ALTO working group accomplished?
Evidence: Starting around 2005, the widespread use of peer-to-peer applications such as BitTorrent was a serious concern to many network operators, as the massive amounts of network traffic caused by these applications had a significant impact on traffic engineering and revenues. Some network operators tried to throttle this traffic.

In May 2008, in an IETF Workshop on Peer-to-Peer Infrastructure, several areas of work were identified:

1. A standardized interface for the exchange of information between the underlying IP network and an overlay network, such as a peer-to-peer network. The basic idea is, that if the overlay network was aware of the topology and the cost for sending traffic through the underlying IP network, it could optimize decisions with respect to the overlay network's topology (e.g., peer selection) and routing of traffic through the overlay network. The result would be better performance or Quality of Experience in the application while reducing the utilization of the underlying network infrastructure. This work item led to the establishment of the IETF ALTO working group.
2. Content caches in the network. This has been studied in the IETF DECADE working group. However, no new protocol has been developed and standardized.
3. A new congestion control mechanism in the transport layer for background traffic, which "yields" to standard TCP. This was worked on in the IETF LEDBAT working group and has been standardized in RFC 6817.
4. A new DiffServ code point to mark IP packets to have a lower priority than the default "best effort" category has been standardized in RFC 8622.

The IETF ALTO working group was established in November 2008. The first deliverables were the problem statement, the requirements document, the specification of the core ALTO protocol and an ALTO server discovery mechanism. Since then, various extensions have been specified (see below) or are still work in progress (see IETF ALTO Datatracker).

Originally designed to support peer-to-peer file sharing, the concept is broadly applicable to many network problems. However, as of 2021 it has not achieved widespread deployment in the internet. Nevertheless, there have been experiments in Internet service provider (ISP) networks and a deployment to support large data transfers for the Large Hadron Collider at CERN.
Answer: The IETF ALTO working group was formed to solve the problems caused by the widespread use of peer-to-peer applications such as BitTorrent. The working group has provided a problem statement, requirements document, and the specifications of the core ALTO protocol and ALTO server discovery mechanism. There is still no widespread adoption of the ALTO protocol as of 2021 although some Internet service provider (ISP) networks have experimented with it, and CERN has deployed it to support large data transfers for the Large Hadron Collider.
Question: What is WordPress?
Evidence: WordPress (WP or WordPress.org) is a free and open-source content management system (CMS) written in hypertext preprocessor (PHP) language and paired with a MySQL or MariaDB database with supported HTTPS. Features include a plugin architecture and a template system, referred to within WordPress as "Themes". WordPress was originally created as a blog-publishing system but has evolved to support other web content types including more traditional mailing lists and Internet fora, media galleries, membership sites, learning management systems (LMS) and online stores.
Answer: WordPress is a Content Management System (CMS) written in hypertext preprocessor (PHP). It uses either a MySQL or MariaDB database and has a robust library of WordPress plugins and themes for customization.
Question: 5 steps to make a fire
Answer: Step 1: Clear a Circular Space on the Ground. ...
Step 2: Line the Circular Space With Large Rocks. ...
Step 3: Place the Small Sticks and Twigs in the Circular Space As a Platform. ...
Step 4: Place Dry Grass, and Leaves on the Kindling. ...
Step 5: Light the Fire With a Lighter or Matches.
Question: Evaluation can you give short info on dataset size at the beginning?
Evidence: 1 # Define slots 2 slot(s1). slot(s2). slot(s3). slot(s4). 3 # Define identifiers for the objects in the image 4 # (there are up to four objects in one image). 5 obj(o1). obj(o2). obj(o3). obj(o4). 6 # Assign each slot to an object identifier 7 {assign_one_slot_to_one_object(X, O): slot(X)}=1 :- obj(O). 8 # Make sure the matching is one-to-one between slots 9 # and objects identifiers. 10 :- assign_one_slot_to_one_object(X1, O1), 11 assign_one_slot_to_one_object(X2, O2), 12 X1==X2, O1!=O2. 13 # Define all Neural-Probabilistic Predicates 14 npp(color_attr(X), [red, blue, green, grey, brown, 15 magenta, cyan, yellow, bg]) :- slot(X). 16 npp(shape_attr(X), [circle, triangle, square, bg]) :- slot(X). 17 npp(shade_attr(X), [bright, dark, bg]) :- slot(X). 18 npp(size_attr(X), [big,small,bg]) :- slot(X). 19 # Object O has the attributes C and S and H and Z if ... 20 has_attributes(O, C, S, H, Z) :- slot(X), obj(O), 21 assign_one_slot_to_one_object(X, O), 22 color(+X, -C), shape(+X, -S), 23 shade(+X, -H), size(+X, -Z).
Answer: The size of the data set is always determined by the queries in SLASH. These are attached to the source code in Appendix B. In MNIST Addition, there are always two images involved in one question. Consequently, the training dataset was 30000 in total size and the test dataset of 5000. For ShapeWorld4, one query was asked for each shape, resulting in maximally 80000 questions for training (20000 images) and maximally 20000 (5000 images) for testing in total. We have included this information in our new version.
Question: What is a Husky?
Answer: A Husky is a type of dog.
Question: What does a typical financial planning and analysis team structure consist of?
Answer: Typically, a financial planning and analysis team will consist of a financial analyst, senior financial analyst, finance manager, director, and VP. This team will usually fall under the chief financial officer for the company.
Question: How would the world look like in say 200 years from now?
Answer: What I envision will happen in the future, say, in a 100 years is that we will have a world with all self sustaining technology. We will be harnessing power easily from abundant natural resources inside and outside of the solar system. All work will be automated and done by machines. Human labor will not be required anymore, be it in the factories or service industry or even in the medical fields. The elite class or the top 1% rich in the world will own all of the corporations and run it. They will not need the rest of the 99% of people in the world as they do will not require their services to run factories and corporations and make them profitable. The elite will see them including any existing forms of their government as a liability. There would be absolutely no functioning government in any part of the world and access to the elite cities or areas will be cut off from the rest of the population. The only people saved would be famous artists or athletes or anyone with creative skills. Rest of the people will be left to fend for themselves and all the progress made as a civilized society will be lost. Whomever is able to survive will set up small communities and continue life. But life will be hard for lack of food and medical care. Some communities may attempt to take out the elite cities but will fail. Eventually the 99% would perish after a few hundred years. The elites will have problems of their own like fights between corporations. But almost all of them will survive for generations and several thousands of years until some catastrophic event wipes them all out be it an alien invasion or something from natural causes.
Question: What are the best social media sites
Answer: Twitter, facebook, tiktok, snapchat
Question: Classify each of the following as being part of a car, house, or both: window, steering wheel, air conditioner, couch, kitchen, engine
Answer: Window: Both
Steering Wheel: Car
Air Conditioner: Both
Couch: Car
Kitchen: House
Engine: Car
Question: I need to purchase food at the grocery store but I dont have a car to get there. What are some of the other ways I could get the food I need?
Answer: I could use a taxi service like Uber or Lyft to take me there and back.
I can ask a friend to give me a ride in their car.
I can walk to the store if it is a reasonable distance.
I could ride a bike or a scooter.
I can use a food delivery service like Instacart to deliver my groceries to my house.
I could take public transportation like a bus or the subway, if available in my area.
Question: What's the downside of this study?
Evidence: In this work, we have proposed an automated approach for the novel task of suggesting news articles to Wikipedia entity pages to facilitate Wikipedia updating. The process consists of two stages. In the first stage, article–entity placement, we suggest news articles to entity pages by considering three main factors, such as entity salience in a news article, relative authority and novelty of news articles for an entity page. In the second stage, article–section placement, we determine the best fitting section in an entity page. Here, we remedy the problem of incomplete entity section profiles by constructing section templates for specific entity classes. This allows us to add missing sections to entity pages. We carry out an extensive experimental evaluation on 351,983 news articles and 73,734 entities coming from 27 distinct entity classes. For the first stage, we achieve an overall performance with P=0.93, R=0.514 and F1=0.676, outperforming our baseline competitors significantly. For the second stage, we show that we can learn incrementally to determine the correct section for a news article based on section templates. The overall performance across different classes is P=0.844, R=0.885 and F1=0.860.
In the future, we will enhance our work by extracting facts from the suggested news articles. Results suggest that the news content cited in entity pages comes from the first paragraphs. However, challenging task such as the canonicalization and chronological ordering of facts, still remain.
challenging task such as the canonicalization and chronological ordering of facts, still remain.
Answer: Challenging task such as the canonicalization and chronological ordering of facts, still remain.
Question: How many types of bass are there?
Answer: There are two main types of bass: the electric bass and the acoustic bass. There are also various other types of bass, such as the upright bass, the fretless bass, and the double bass.
Question: Name some famous cartoon cats.
Answer: Some famous cartoon cats include Heathcliff, Garfield, Tom, Sylvester, Felix, and the Cheshire Cat.
Question: What approach do the authors propose to solve such a problem?
Evidence: Neural machine translation (NMT) BIBREF0 , BIBREF1 , BIBREF2 has enabled end-to-end training of a translation system without needing to deal with word alignments, translation rules, and complicated decoding algorithms, which are the characteristics of phrase-based statistical machine translation (PBSMT) BIBREF3 . Although NMT can be significantly better than PBSMT in resource-rich scenarios, PBSMT performs better in low-resource scenarios BIBREF4 . Only by exploiting cross-lingual transfer learning techniques BIBREF5 , BIBREF6 , BIBREF7 , can the NMT performance approach PBSMT performance in low-resource scenarios.
However, such methods usually require an NMT model trained on a resource-rich language pair like French INLINEFORM0 English (parent), which is to be fine-tuned for a low-resource language pair like Uzbek INLINEFORM1 English (child). On the other hand, multilingual approaches BIBREF8 propose to train a single model to translate multiple language pairs. However, these approaches are effective only when the parent target or source language is relatively resource-rich like English (En). Furthermore, the parents and children models should be trained on similar domains; otherwise, one has to take into account an additional problem of domain adaptation BIBREF9 .
In this paper, we work on a linguistically distant and thus challenging language pair Japanese INLINEFORM0 Russian (Ja INLINEFORM1 Ru) which has only 12k lines of news domain parallel corpus and hence is extremely resource-poor. Furthermore, the amount of indirect in-domain parallel corpora, i.e., Ja INLINEFORM2 En and Ru INLINEFORM3 En, are also small. As we demonstrate in Section SECREF4 , this severely limits the performance of prominent low-resource techniques, such as multilingual modeling, back-translation, and pivot-based PBSMT. To remedy this, we propose a novel multistage fine-tuning method for NMT that combines multilingual modeling BIBREF8 and domain adaptation BIBREF9 .
We have addressed two important research questions (RQs) in the context of extremely low-resource machine translation (MT) and our explorations have derived rational contributions (CTs) as follows:
To the best of our knowledge, we are the first to perform such an extensive evaluation of extremely low-resource MT problem and propose a novel multilingual multistage fine-tuning approach involving multilingual modeling and domain adaptation to address it.
and our explorations have derived rational contributions (CTs) as follows: To the best of our knowledge, we are the first to perform such an extensive evaluation of extremely low-resource MT problem and propose a novel multilingual multistage fine-tuning approach involving multilingual modeling and domain adaptation to address it.
Answer: A novel multilingual multistage fine-tuning approach involving multilingual modeling and domain adaptation.
Question: Who wrote Democracy in America?
Answer: Alexis de Tocqueville wrote Democracy in America
Question: Extract a bullet point list in the format {movie} ({date}) from the life of director Quentin Tarantino
Evidence: Quentin Jerome Tarantino (/ˌtærənˈtiːnoʊ/; born March 27, 1963) is an American film director, writer, producer, and actor. His films are characterized by stylized violence, extended dialogue including the pervasive use of profanity and references to popular culture.

Tarantino began his career as an independent filmmaker with the release of the crime film Reservoir Dogs in 1992. His second film, Pulp Fiction (1994), a dark comedy crime thriller, was a major success with critics and audiences winning numerous awards, including the Palme d'Or and the Academy Award for Best Original Screenplay. In 1996, he appeared in From Dusk till Dawn, also writing the screenplay. Tarantino's third film, Jackie Brown (1997), paid homage to blaxploitation films.

In 2003, Tarantino directed Kill Bill: Volume 1, inspired by the traditions of martial arts films; it was followed by Volume 2 in 2004. He then made the exploitation-slasher Death Proof (2007), part of a double feature with Robert Rodriguez released under the collective title Grindhouse. His next film, Inglourious Basterds (2009), follows an alternate account of World War II. He followed this with Django Unchained (2012), a slave revenge Spaghetti Western, which won him his second Academy Award for Best Original Screenplay. His eighth film The Hateful Eight (2015) is a revisionist Western thriller and opened to audiences with a roadshow release. His most recent film, Once Upon a Time in Hollywood (2019), is a comedy drama set in the late 1960s about the transition of Old Hollywood to New Hollywood. A novelization of the film was also published in 2021, becoming his debut novel.

Tarantino's work has been subject to controversy, such as the depictions of violence, frequent inclusion of racial slurs and the alleged negligence of safety in his handling of stunt scenes on Kill Bill: Volume 2. During Tarantino's career, his films have garnered a cult following, as well as critical and commercial success, he has been considered "the single most influential director of his generation". Apart from receiving the Palme d'Or and two Academy Awards, his other major awards include two BAFTAs and four Golden Globes.
Answer: - Reservoir Dogs (1992)
- Pulp Fiction (1994)
- From Dusk till Dawn (1996)
- Jackie Brown (1997)
- Kill Bill: Volume 1 (2003)
- Kill Bill: Volume 1 (2004)
- Death Proof (2007)
- Inglourious Basterds (2009),
- Django Unchained (2012)
- The Hateful Eight (2015)
- Once Upon a Time in Hollywood (2019)
Question: Classify each of the following as either a plant or an animal: Oak Tree, mouse, grass, daisy, zebra, venus fly trap, hawk, whale.
Answer: The following are plants: Oak Tree, grass, daisy, venus fly trap. The following are animals: Mouse, Zebra, hawk, whale.