DataHammer/scimrc huggingface dataset:
Question: Tell me which of these countries has more than 1 billion populations: Japan, Canada, United States, Russia
Answer: None of these
Question: What is a good city to visit in Michigan in the summer?
Answer: Michigan is well known for the variety of activities it offers year round. In the summer, camping, golfing, bicycling, hiking and swimming are some of the popular activities locals and visitors enjoy. While there are many great cities in the state that cater to these and other activities, a perennial favorite is Traverse City. Traverse City is located on the western side of Michigan right on Lake Michigan and Grand Traverse Bay. This side of Michigan is often referred to as the Middle or Third Coast given the abundant shoreline. Traverse City is known for ease of access to all of the activities listed above as well as offering a small town feel with larger city amenities. There are also numerous cities and destinations all within a day’s drive including the famous Sleeping Bear Sand Dunes national lakeshore. The western side of Michigan is also known for its wine production and Traverse City has over 30 wineries within a 30 minute drive. Throughout the summer, Traverse City hosts many events, the most famous being the National Cherry Festival as Michigan is the largest tart cherry producing state in the US.
Question: How Fast Does An Average Person Run?
Answer: Males run at an average speed of 8 mph, and women run at an average speed of 6.5 mph.
People running for their lives, not for recreational purposes, can run at an average speed of 12 mph. Athletes and gym enthusiasts tend to be faster than the average person and may run at a speed as high as 14 to 17 mph. The numbers change when referring to endurance and long-distance trails. Sprinters running a short distance average 10 mph. However, it’s hard to maintain that momentum for a long distance. When using data on the average running speed for five kilometers, males have an average running speed of 5.9 mph, and women have an average running speed of 5 mph.
Question: Please give me a short description of each of the three categories of performance that Bharatanatyam follows.
Evidence: The repertoire of Bharatanatyam, like all major classical Indian dance forms, follows the three categories of performance in the Natya Shastra. These are Nritta (Nirutham), Nritya (Niruthiyam) and Natya (Natyam).

The Nritta performance is abstract, fast and rhythmic aspect of the dance. The viewer is presented with pure movement in Bharatanatyam, wherein the emphasis is the beauty in motion, form, speed, range and pattern. This part of the repertoire has no interpretative aspect, no telling of story. It is a technical performance, and aims to engage the senses (prakriti) of the audience.
The Nritya is slower and expressive aspect of the dance that attempts to communicate feelings, storyline particularly with spiritual themes in Hindu dance traditions. In a nritya, the dance-acting expands to include silent expression of words through gestures and body motion set to musical notes. The actor articulates a legend or a spiritual message. This part of a Bharatanatyam repertoire is more than sensory enjoyment, it aims to engage the emotions and mind of the viewer.
The Natyam is a play, typically a team performance, but can be acted out by a solo performer where the dancer uses certain standardized body movements to indicate a new character in the underlying story. A Natya incorporates the elements of a Nritya.
Answer: 1. Nritta: This is the pure movement aspect of the dance of Bharatanatyam. The dancer showcases movements in varying form, speed, range and levels. 
2. Nritya: This is the expressive aspect of the dance, wherein the dancer tells a story, usually of a spiritual theme of Hindu tradition, through both facial expressions and movements.
3. Natyam: This is the theatrical aspect of the dance, where the dancer incorporates the elements of Nritya, to enact different characters of a play. This can be performed either solo or as a team.
Question: What did their method base on?
Evidence: Laughter is a special, higher-order function that only humans possess. In the analysis of laughter, as Wikipedia says, “Laughter is thought to be a shift of composition (schema)", and laughter frequently occurs when there is a change from a composition of receiver. However, the viewpoint of laughter differs greatly depending on the position of the receiver. Therefore, the quantitative measurement of laughter is very difficult. Image Ogiri on web services such as "Bokete" BIBREF0 have recently appeared, where users post funny captions for thematic images and the captions are evaluated in an SNS-like environment. Users compete to obtain the greatest number of “stars”. Although quantification of laughter is considered to be a very difficult task, the correspondence between evaluations and images on Bokete allows us to treat laughter quantitatively. Image captioning is an active topic in computer vision, and we believe that humorous image captioning can be realized. The main contributions of the present paper are as follows:
BoketeDB
In the experimental section, we compare the proposed method based on Funny Score and BoketeDB pre-trained parameters with a baseline provided by MS COCO Pre-trained CNN+LSTM. We also compare the results of the NJM with funny captions provided by humans. In an evaluation by humans, the results provided by the proposed method were ranked lower than those provided by humans (22.59% vs. 67.99%) but were ranked higher than the baseline (9.41%). Finally, we show the generated funny captions for several images.
The main contributions of the present paper are as follows: BoketeDB In the experimental section, we compare the proposed method based on Funny Score and BoketeDB pre-trained parameters with a baseline provided by MS COCO Pre-trained CNN+LSTM.
Answer: Funny Score and BoketeDB pre-trained parameters。
Question: What are the most airplanes to learn how to fly?
Answer: The most popular single engine propeller plane in the United States is Cessna 172 model, where it gives pilots the ability to complete visual flight rules (VFR) flying as well as Instrument flight rules (IFR). Cessnas tend to be reliable and safe planes with proper maintenance.
Question: What are some tips that kids could use to learn the lines of a musical school play? Write them as if they are narrated by an elementary school student.
Answer: One good method is to practice your lines of the play with other people, who might not necessarily be in the play (for example, practice with your parents!). It's also good to review your lines and repeat them by heart a few minutes later. Then over time try to repeat them without looking at the play book. One thing to avoid is to only read the lines but not say them out loud. Saying the lines out loud is really important for remembering them and practicing your acting. And above all, try to have fun!
Question: Who was General Sir Henry Clinton
Answer: General Sir Henry Clinton, KB (16 April 1730 – 23 December 1795) was a British Army officer and politician who sat in the House of Commons between 1772 and 1795. He is best known for his service as a general during the American War of Independence. First arriving in Boston in May 1775, from 1778 to 1782 he was the British Commander-in-Chief in North America. In addition to his military service, due to the influence of his cousin Henry Pelham-Clinton, 2nd Duke of Newcastle, he was a Member of Parliament for many years. Late in life he was named Governor of Gibraltar, but died before assuming the post.
Question: Classify these dishes into the cuisines that they belong to.
Panipuri, Risotto, Butter Chicken, Lasagna, Naan, Ravioli, Jalebi
Answer: Indian - Panipuri, Butter Chicken, Naan, Jalebi
Italian - Risotto, Lasagna, Ravioli
Question: Write a letter to my landlord, Dave, asking them to hire a plumber to repair a leaking faucet.
Answer: Hi Dave, I'm reaching out about our leaking sink faucet. The sink has been leaking for about a week now, and I've tried tightening the faucet and making sure that the faucet is fully closed, but it still seems to be leaking. It is a pretty constant drip, and is filling a cup up every hour. I think it probably needs to be seen by a plumber who can decide if it is fixable or needs to be replaced. Could you call a plumber and ask them to step by?
Question: Why am I so tired?
Answer: If you're frequently tired without obvious reasons, such as lack of sleep, you should consult a doctor. If you suffer from regular sleep deprivation, you should also consult a doctor.
Question: List some steps I'll need to take in order to buy a car
Answer: - Search the web for car models that interest you
- Go to the dealership for a test drive
- Think about whether to finance, buy or lease your car
- Order a car from the dealership
- Enjoy your new car
Question: How are proof scores calculated?
Evidence: Unification Module. In backward chaining, unification between two atoms is used for checking whether they can represent the same structure. In discrete unification, non-variable symbols are checked for equality, and the proof fails if the symbols differ. In NTP, rather than comparing symbols, their embedding representations are compared by means of a RBF kernel. This allows matching different symbols with similar semantics, such as matching relations like ${grandFatherOf}$ and ${grandpaOf}$ . Given a proof state $= (_, _)$ , where $_$ and $_$ denote a substitution set and a proof score, respectively, unification is computed as follows:
The resulting proof score of $g$ is given by:
$$ \begin{aligned} \max _{f \in \mathcal {K}} & \; {unify}_(g, [f_{p}, f_{s}, f_{o}], (\emptyset , )) \\ & = \max _{f \in \mathcal {K}} \; \min \big \lbrace , \operatorname{k}(_{\scriptsize {grandpaOf}:}, _{f_{p}:}),\\ &\qquad \qquad \qquad \operatorname{k}(_{{abe}:}, _{f_{s}:}), \operatorname{k}(_{{bart}:}, _{f_{o}:}) \big \rbrace , \end{aligned}$$ (Eq. 3)
where $f \triangleq [f_{p}, f_{s}, f_{o}]$ is a fact in $\mathcal {K}$ denoting a relationship of type $f_{p}$ between $f_{s}$ and $f_{o}$ , $_{s:}$ is the embedding representation of a symbol $s$ , $$ denotes the initial proof score, and $\operatorname{k}({}\cdot {}, {}\cdot {})$ denotes the RBF kernel. Note that the maximum proof score is given by the fact $f \in \mathcal {K}$ that maximises the similarity between its components and the goal $\mathcal {K}$0 : solving the maximisation problem in eq:inference can be equivalently stated as a nearest neighbour search problem. In this work, we use ANNS during the forward pass for considering only the most promising proof paths during the construction of the neural network.
Given a proof state $= (_, _)$ , where $_$ and $_$ denote a substitution set and a proof score, respectively, unification is computed as follows:
The resulting proof score of $g$ is given by:

$$ \begin{aligned} \max _{f \in \mathcal {K}} & \; {unify}_(g, [f_{p}, f_{s}, f_{o}], (\emptyset , )) \\ & = \max _{f \in \mathcal {K}} \; \min \big \lbrace , \operatorname{k}(_{\scriptsize {grandpaOf}:}, _{f_{p}:}),\\ &\qquad \qquad \qquad \operatorname{k}(_{{abe}:}, _{f_{s}:}), \operatorname{k}(_{{bart}:}, _{f_{o}:}) \big \rbrace , \end{aligned}$$ (Eq. 3)

where $f \triangleq [f_{p}, f_{s}, f_{o}]$ is a fact in $\mathcal {K}$ denoting a relationship of type $f_{p}$ between $f_{s}$ and $f_{o}$ , $_{s:}$ is the embedding representation of a symbol $s$ , $$ denotes the initial proof score, and $\operatorname{k}({}\cdot {}, {}\cdot {})$ denotes the RBF kernel.
Answer: The answers are shown as follows:
* '= ( , { ll k(h:, g:) if hV, gV

1 otherwise } )

where $_{h:}$ and $_{g:}$ denote the embedding representations of $h$ and $g$ , respectively.
Question: What tools do people use to communicate in 2023?
Answer: There are many tools people use to communicate in 2023.  Email, phone, text message, and direct message on social media platforms are some of the most common; however, people still write letters and send post cards to one another at times as they have for generations.  Another form of communication is in-person communication where groups of people will get together and communicate on a given topic.  This could be at work in a meeting or a group of friends getting together for dinner to catch up.  This form of communication requires no other tools than one's own voice.
Question: How many types of recurrent neural layers in the authors' multi-task learning architecture?
Evidence: Neural network based models have been widely exploited with the prosperities of Deep Learning BIBREF0 and achieved inspiring performances on many NLP tasks, such as text classification BIBREF1 , BIBREF2 , semantic matching BIBREF3 , BIBREF4 and machine translation BIBREF5 . These models are robust at feature engineering and can represent words, sentences and documents as fix-length vectors, which contain rich semantic information and are ideal for subsequent NLP tasks.
One formidable constraint of deep neural networks (DNN) is their strong reliance on large amounts of annotated corpus due to substantial parameters to train. A DNN trained on limited data is prone to overfitting and incapable to generalize well. However, constructions of large-scale high-quality labeled datasets are extremely labor-intensive. To solve the problem, these models usually employ a pre-trained lookup table, also known as Word Embedding BIBREF6 , to map words into vectors with semantic implications. However, this method just introduces extra knowledge and does not directly optimize the targeted task. The problem of insufficient annotated resources is not solved either.
Multi-task learning leverages potential correlations among related tasks to extract common features, increase corpus size implicitly and yield classification improvements. Inspired by BIBREF7 , there are a large literature dedicated for multi-task learning with neural network based models BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 . These models basically share some lower layers to capture common features and further feed them to subsequent task-specific layers, which can be classified into three types:
In this paper, we propose a generalized multi-task learning architecture with four types of recurrent neural layers for text classification. The architecture focuses on Type-III, which involves more complicated interactions but has not been researched yet. All the related tasks are jointly integrated into a single system and samples from different tasks are trained in parallel. In our model, every two tasks can directly interact with each other and selectively absorb useful information, or communicate indirectly via a shared intermediate layer. We also design a global memory storage to share common features and collect interactions among all tasks.
We conduct extensive experiments on five benchmark datasets for text classification. Compared to learning separately, jointly learning multiple relative tasks in our model demonstrate significant performance gains for each task.
Our contributions are three-folds:
In this paper, we propose a generalized multi-task learning architecture with four types of recurrent neural layers for text classification.
Answer: Four.
Question: Who gave the UN the land in NY to build their HQ
Answer: John D Rockerfeller
Question: What is the Taylor rule?
Answer: The Taylor rule is a monetary policy targeting rule. The rule was proposed in 1992 by American economist John B. Taylor for central banks to use to stabilize economic activity by appropriately setting short-term interest rates. The rule considers the federal funds rate, the price level and changes in real income. The Taylor rule computes the optimal federal funds rate based on the gap between the desired (targeted) inflation rate and the actual inflation rate; and the output gap between the actual and natural output level. According to Taylor, monetary policy is stabilizing when the nominal interest rate is higher/lower than the increase/decrease in inflation. Thus the Taylor rule prescribes a relatively high interest rate when actual inflation is higher than the inflation target.
Question: How did Frederic Hudson start working at the Herald?
Evidence: Hudson was born in Quincy, Massachusetts, in 1819, and attended the town school in Concord, Massachusetts. When he was 17, he moved to New York City, where his brothers had opened "Hudson's News Room". In 1836, he there met James Gordon Bennett Sr., who had founded the Herald in 1835, and soon went to work for him, becoming the third full-time employee of the paper.
Answer: While working at "Hudson's News Room" in 1836, Hudson met James Gordon Bennett Sr., who had founded the Herald just one year prior in 1835. The 17 year old Hudson went to work for the Herald shortly thereafter, becoming just the third full-time employee of the Herald.
Question: Can the authors comment on how the learning rates and initializations for different predictor heads in all settings were chosen?
Evidence: Consider a task Y , which can correspond to a variety of objectives. For instance, it could be an auxiliary task of predicting the next state when interacting with an RL environment, or simply a classification task on a vision benchmark. We are interested in training a model that can do well on task Y , where the model consists of two components, a backbone representation f θ and a predictor/classifier h φ which is attached over the representation. In a lot of tasks that involve pixel input, spurious correlations can arise due to various reasons such as lack of non-iid data, irrelevant information, confounders etc. that can result in poor generalization at test time. An attractive property for better generalization has been the idea of sparse representations, in that the mutual information of any two dimensions of the representation must be low. However, in order to avoid learning trivial solutions, the representation as a whole should encode enough information about the downstream task as well. A combination of both of these objectives then leads to a model with better generalization capability. In this paper, we balance the above two objectives using the idea of model invariance (MODINV). MODINV deploys multiple predictors over a single representation, while training each predictor independently, each capturing different spurious correlations. The common representation acts as a implicit invariance loss which ensures that only the optimal representation remains at convergence. Intuitively, each predictor can be looked at as an augmented version of the optimal model, where the augmentation is over the model space and refers to particular spurious correlations arising in the model that differ it from the optimal model. Note that each predictor head is the same in architecture, and we only diversify the learning procedure of each head (through different initialization, independent training, different learning rates). This is so that we eventually converge to an optimal representation for a particular predictor/classifier and not for all predictor/classifier families, which is a much more hard to optimize in practise. For all experiments, we use a random routing to decide which data sample is used to train which predictor head. Furthermore, the learning rates of the different predictor heads are chosen intuitively, i.e. if the base rate is 3e-3 then we choose one slightly higher rate (5e-3) and one slightly smaller rate (1e-3) for K=3 predictors.
Furthermore, the learning rates of the different predictor heads are chosen intuitively, i.e. if the base rate is 3e-3 then we choose one slightly higher rate (5e-3) and one slightly smaller rate (1e-3) for K=3 predictors.
Answer: For now, this is done simply by intuition, i.e. if the base lr is 3e-3 then we choose one lr slightly higher, i.e. 5e-3 and one lr slightly smaller, i.e. 1e-3. We believe in future work there is a lot of scope in developing better engineering solutions for such choices. We have noted this in the updated version.
Question: How many goals did Archie Thompson's team mates score?
Evidence: On 11 April 2001, the Australian and American Samoan national association football teams played each other in an Oceanian qualifying match for the 2002 FIFA World Cup. The match was played at the International Sports Stadium in Coffs Harbour, Australia. Australia set a world record for the largest victory in an international football match, winning the game 31–0. Australia's Archie Thompson also broke the record for most goals scored by a player in an international match by scoring 13 goals. David Zdrilic, the scorer of eight goals in the match, scored the second-highest number of goals in an international match since World War I.
Answer: Since Archie Thompson scored 13 goals, his teammates scored 18 goals
Question: Why wasn't the Treaty of Versailles effective?
Answer: The Treaty of Versailles was created almost vindictively by the West since they were seeking justice from Germany. This caused major social and economic problems in Germany as they were not only forced to take blame and return territories, but also pay large reparations to Britain and France. These harsh terms inevitably led to World War II, as bitter and resentful German citizens were easily swayed by Hitler's promises of revenge. The Allies attempt to restrict Germany’s power also failed, as they used a strategy of appeasement when Hitler tried to expand, rendering the rules of the treaty useless.
Question: What is the performance of the model for the German sub-task A?
Evidence: The performance of our models across different languages for sub-task A are shown in table TABREF19. Our model got the first position in the German sub-task with a macro F1 score of 0.62. The results of sub-task B and sub-task C is shown in table TABREF20 and TABREF21 respectively.
Our model got the first position in the German sub-task with a macro F1 score of 0.62.
Answer: The answers are shown as follows:
* macro F1 score of 0.62
Question: Hindi Diwas is observed every year on which date?
Answer: September 14
Question: Which method do they use for word segmentation?
Evidence: The proposed method can learn words related to places from the utterances of sentences. We use an unsupervised word segmentation method latticelm that can directly segment words from the lattices of the speech recognition results of the uttered sentences BIBREF22 . The lattice can represent to a compact the set of more promising hypotheses of a speech recognition result, such as N-best, in a directed graph format. Unsupervised word segmentation using the lattices of syllable recognition is expected to be able to reduce the variability and errors in phonemes as compared to NPYLM BIBREF13 , i.e., word segmentation using the 1-best speech recognition results.
We use an unsupervised word segmentation method latticelm that can directly segment words from the lattices of the speech recognition results of the uttered sentences BIBREF22 .
Answer: The answers are shown as follows:
* unsupervised word segmentation method latticelm