DataHammer/scimrc huggingface dataset:
Question: What are their results on the entity recognition task?
Evidence: The performances of the NER experiments are reported separately for three different parts of the system proposed.
Table 6 presents the comparison of the various methods while performing NER on the bot-generated corpora and the user-generated corpora. Results shown that, in the first case, in the training set the F1 score is always greater than 97%, with a maximum of 99.65%. With both test sets performances decrease, varying between 94-97%. In the case of UGC, comparing the F1 score we can observe how performances significantly decrease. It can be considered a natural consequence of the complex nature of the users' informal language in comparison to the structured message created by the bot.
The performances of the NER experiments are reported separately for three different parts of the system proposed.
Results shown that, in the first case, in the training set the F1 score is always greater than 97%, with a maximum of 99.65%. With both test sets performances decrease, varying between 94-97%. In the case of UGC, comparing the F1 score we can observe how performances significantly decrease.
Answer: The answers are shown as follows:
* With both test sets performances decrease, varying between 94-97%
Question: How does their Answer Collection System collect answers for all N visual questions (Figure 6a)?
Evidence: Suppose we have a budget INLINEFORM0 which we can allocate to collect extra answers for a subset of visual questions. Our system automatically decides to which visual questions to allocate the “extra" answers in order to maximize captured answer diversity for all visual questions.
The aim of our system is to accrue additional costs and delays from collecting extra answers only when extra responses will provide more information. Towards this aim, our system involves three steps to collect answers for all INLINEFORM0 visual questions (Figure FIGREF11 a). First, the system applies our top-performing random forest classifier to every visual question in the batch. Then, the system ranks the INLINEFORM1 visual questions based on predicted scores from the classifier, from visual questions most confidently predicted to lead to answer “agreement" from a crowd to those most confidently predicted to lead to answer “disagreement" from a crowd. Finally, the system solicits more ( INLINEFORM2 ) human answers for the INLINEFORM3 visual questions predicted to reflect the greatest likelihood for crowd disagreement and fewer ( INLINEFORM4 ) human answers for the remaining visual questions. More details below.
Towards this aim, our system involves three steps to collect answers for all N visual questions (Figure 6a). First, the system applies our topperforming random forest classifier to every visual question in the batch. Then, the system ranks the N visual questions based on predicted scores from the classifier, from visual questions most confidently predicted to lead to answer “agreement” from a crowd to those most confidently predicted to lead to answer “disagreement” from a crowd. Finally, the system solicits more (R) human answers for the B visual questions predicted to reflect the greatest likelihood for crowd disagreement and fewer (S) human answers for the remaining visual questions.
Answer: First, applies top performing random forest classifier to every visual question in the batch, then ranks the N visual questions based on predicted scores from the classifier, and finally, solicits more (R) human answers for the B visual questions predicted to reflect the greatest likelihood for crowd disagreement and fewer (S) human answers for the remaining visual questions.
Question: Provide a 10-step flow for a heterosexual couple's non-religious wedding ceremony.
Answer: 1) Bride walks down the aisle
2) Officiant welcomes everyone
3) Officiant thanks the bride and groom's parents and all attendees for being there
4) Officiant recognizes family members who have passed away or could not attend
5) Officiant shares a quick history and their experience with the bride and groom
6) Family member or friend shares a non-religious reading, such as a poem or excerpt from a novel
7) Bride and groom exchange vows
8) Bride and groom exchange rings
9) Officiant introduces the bride and groom as husband and wife ("you may kiss the bride!")
10) Bride and groom exit, walking back down the aisle, followed by bridal party
Question: What is Kefir?
Answer: Kefir is a fermented drink made of milk. It is typically made by placing kefir grains in the milk of cows, goats or sheep, and leaving it to ferment overnight at room temperature. Originally from the North Caucasus, it is now popular across many regions in Eastern Europe.
Question: Give me 5 facts about Singapore
Answer: * Singapore is a very tiny country that is only 733 km squared.
* In 2022 the estimated population of Singapore was approximately 5,637,000
* The current president (As of April 2023) of Singapore is Halimah Yacob
* The number one religion in Singapore is Buddhism at approx. 31.1%
* The national language of Singapore is Malay
Question: What can you tell me about about the Landscape Arch in Arches National Park in Utah?
Evidence: The arch is among many in the Devils Garden area in the north of the park. Landscape Arch was named by Frank Beckwith who explored the area in the winter of 1933–1934 as the leader of an Arches National Monument scientific expedition. The arch can be reached by a 0.8 mi (1.3 km) graded gravel trail.

The Natural Arch and Bridge Society (NABS) considers Landscape Arch the fifth longest natural arch in the world, after four arches in China. In 2004, the span of Landscape Arch was measured at 290.1 ft (88.4 m), ±0.8 ft (0.24 m), with a height of 77.5 ft (23.6 m). NABS measured the span of the slightly shorter Kolob Arch in Zion National Park at 287 ft (87 m) in 2006.

The most recent recorded rockfall events occurred in the 1990s when one large slab fell in 1991 and then two additional large rockfalls occurred in 1995. Since the rockfalls, the trail beneath the arch has been closed.
Answer: The Landscape Arch is a natural arch considered the fifth longest natural arch in the world. It is located in the Arches National Park in Utah, USA. You can get to the arch by walking along a low-graded train in the park. The arch has recently lost some of its slabs due to rockfalls.
Question: To which experiment does figure 5 relate? And are all runs run with the same randomized seed?
Evidence: For each configuration, we calculate the criteria score and the corresponding classification accuracy within the setting in Section 4.2. As shown in Figure 3, in general, accuracy performance is positively related to the proposed criteria in both supervised and unsupervised settings, verifying the correctness of using the criteria as the objective in the meta-network training.****To show that our InfoTS can adaptively detect the most effective augmentation based on the data distribution, we follow the setting in Section 4.4 and conduct more ablation studies to investigate comprehensively into the proposed model.
Answer: Thanks for the comments. This part is following Section 4 and thus the setting is consistent with other parts related to the Electricity dataset. All runs were run with the same randomized seed.
Question: Do the authors extend existing models with parameter tying?
Evidence: Even though machine translation has improved considerably with the advent of neural machine translation (NMT) BIBREF0 , BIBREF1 , the translation of pronouns remains a major issue. They are notoriously hard to translate since they often require context outside the current sentence.
As an example, consider the sentences in Figure FIGREF1 . In both languages, there is a pronoun in the second sentence that refers to the European Central Bank. When the second sentence is translated from English to German, the translation of the pronoun it is ambiguous. This ambiguity can only be resolved with context awareness: if a translation system has access to the previous English sentence, the previous German translation, or both, it can determine the antecedent the pronoun refers to. In this German sentence, the antecedent Europäische Zentralbank dictates the feminine gender of the pronoun sie.
It is unfortunate, then, that current NMT systems generally operate on the sentence level BIBREF2 , BIBREF3 , BIBREF4 . Documents are translated sentence-by-sentence for practical reasons, such as line-based processing in a pipeline and reduced computational complexity. Furthermore, improvements of larger-context models over baselines in terms of document-level metrics such as BLEU or RIBES have been moderate, so that their computational overhead does not seem justified, and so that it is hard to develop more effective context-aware architectures and empirically validate them.
To address this issue, we present an alternative way of evaluating larger-context models on a test set that allows to specifically measure a model's capability to correctly translate pronouns. The test suite consists of pairs of source and target sentences, in combination with contrastive translation variants (for evaluation by model scoring) and additional linguistic and contextual information (for further analysis). The resource is freely available. Additionally, we evaluate several context-aware models that have recently been proposed in the literature on this test set, and extend existing models with parameter tying.
The main contributions of our paper are:
Section SECREF2 explains how our paper relates to existing work on context-aware models and the evaluation of pronoun translation. Section SECREF3 describes our test suite. The context-aware models we use in our experiments are detailed in Section SECREF4 . We discuss our experiments in Section SECREF5 and the results in Section SECREF6 .
Additionally, we evaluate several context-aware models that have recently been proposed in the literature on this test set, and extend existing models with parameter tying.
Answer: Yes, they do.
Question: What is a Romance language?
Answer: A romance language refers to the language family of languages which are derived from Latin, which are spoken in many areas that were a part of the Ancient Roman empire.
Question: What is the difference between a putter and a driver in golf?
Answer: In golf, a driver is generally used from the tee box to hit the ball as far as possible at the beginning of a hole. A putter is used when on the green to hit the ball short distances on the ground into the hole.
Question: What is their definition of hate speech?
Evidence: Our annotation guidelines are similar to the guidelines used by BIBREF9 . We define hateful speech to be the language which explicitly or implicitly threatens or demeans a person or a group based upon a facet of their identity such as gender, ethnicity, or sexual orientation. The labeling of hateful speech in our corpus is binary. A comment will be labeled as hateful or non-hateful.
We define hateful speech to be the language which explicitly or implicitly threatens or demeans a person or a group based upon a facet of their identity such as gender, ethnicity, or sexual orientation.
Answer: The answers are shown as follows:
* language which explicitly or implicitly threatens or demeans a person or a group based upon a facet of their identity such as gender, ethnicity, or sexual orientation
Question: how long do roasted coffee beans last before you should throw them out?
Answer: Freshly roasted coffee beans should be used between two and ten days after roasting.  If you use them too soon, they can be bitter and off-gas carbon dioxide.  If you use them more than two weeks after roasting, most of the nuance of the flavor is gone due to changes in the chemical composition of the beans.
Question: What is Java?
Answer: Java is a high-level, class-based, object-oriented programming language that is designed to have as few implementation dependencies as possible. It is a general-purpose programming language intended to let programmers write once, run anywhere (WORA), meaning that compiled Java code can run on all platforms that support Java without the need to recompile. Java applications are typically compiled to bytecode that can run on any Java virtual machine (JVM) regardless of the underlying computer architecture. The syntax of Java is similar to C and C++, but has fewer low-level facilities than either of them. The Java runtime provides dynamic capabilities (such as reflection and runtime code modification) that are typically not available in traditional compiled languages. As of 2019, Java was one of the most popular programming languages in use according to GitHub, particularly for client–server web applications, with a reported 9 million developers.
Question: what is the architecture of the baseline model?
Evidence: Our baseline model is composed of the following typical components: word embedding, input encoder, alignment, aggregation, and prediction. Below we discuss these components in more details.
FLOAT SELECTED: Figure 1: A high level view of our basic model.
Our baseline model is composed of the following typical components: word embedding, input encoder, alignment, aggregation, and prediction.
FLOAT SELECTED: Figure 1: A high level view of our basic model.
Answer: The answers are shown as follows:
* Our baseline model is composed of the following typical components: word embedding, input encoder, alignment, aggregation, and prediction.
Question: From the passage provided, extract the date when the 34th Wisconsin Infantry Regiment was organized.
Evidence: The 34th Wisconsin Infantry Regiment was a conscripted infantry regiment that served in the Union Army during the American Civil War. The 34th Wisconsin Infantry was composed of men drafted by state authorities under General Order No. 94. The regiment was organized at Madison, Wisconsin, and mustered into Federal service December 31, 1862.
Answer: The 34th Wisconsin Infantry Regiment was organized on December 31, 1862.
Question: What is the baseline for the experiments?
Evidence: Baselines and Approach
In this section, we outline baseline models like $BERT_{large}$. We further propose three approaches: optimized BERT-based models, distilled pretrained models, and the use of ensemble methods for the task of subjectivity detection.
Baselines and Approach ::: Baselines
FastTextBIBREF4: It uses bag of words and bag of n-grams as features for text classification, capturing partial information about the local word order efficiently.
BiLSTM: Unlike feedforward neural networks, recurrent neural networks like BiLSTMs use memory based on history information to learn long-distance features and then predict the output. We use a two-layer BiLSTM architecture with GloVe word embeddings as a strong RNN baseline.
BERT BIBREF5: It is a contextualized word representation model that uses bidirectional transformers, pretrained on a large $3.3B$ word corpus. We use the $BERT_{large}$ model finetuned on the training dataset.
FLOAT SELECTED: Table 1: Experimental Results for the Subjectivity Detection Task
Baselines and Approach
In this section, we outline baseline models like $BERT_{large}$. We further propose three approaches: optimized BERT-based models, distilled pretrained models, and the use of ensemble methods for the task of subjectivity detection.


Baselines and Approach ::: Baselines
FastTextBIBREF4: It uses bag of words and bag of n-grams as features for text classification, capturing partial information about the local word order efficiently.

BiLSTM: Unlike feedforward neural networks, recurrent neural networks like BiLSTMs use memory based on history information to learn long-distance features and then predict the output. We use a two-layer BiLSTM architecture with GloVe word embeddings as a strong RNN baseline.

BERT BIBREF5: It is a contextualized word representation model that uses bidirectional transformers, pretrained on a large $3.3B$ word corpus. We use the $BERT_{large}$ model finetuned on the training dataset.
FLOAT SELECTED: Table 1: Experimental Results for the Subjectivity Detection Task
Answer: The answers are shown as follows:
* FastText
* BERT 
* two-layer BiLSTM architecture with GloVe word embeddings
Question: Was the Pixie Lott song "Mama Do" ever the #1 single?
Evidence: "Mama Do (Uh Oh, Uh Oh)" debuted at number one on the UK Singles Chart, selling 58,840 downloads in its first week. Lott described her reaction upon learning the song reached number one: "I was in bed at my mum and dad's, where I still live, in Brentwood, Essex. I was on the laptop, and I got a phone call from my A&R man. He really dragged it out. I said, 'Please put me out of my misery.' And he said, 'I'm really, really sorry but... you're No 1!' I spent the rest of the day running around the house, screaming."

The single was certified silver by the British Phonographic Industry (BPI) on 28 August 2009 for sales in excess of 200,000 copies. Exactly 11 years later, on 28 August 2020, the certification was upgraded to gold for sales and streams of over 400,000. As a result of the single's popularity at the time, the single's iTunes B-side, "Use Somebody", debuted and peaked, during the single's release week, on the UK Singles Chart at number 52 on sales of 4,409 downloads.

The single also had considerable success outside the UK, reaching the top 10 in Denmark and France, and the top 20 in Ireland, Netherlands and New Zealand.
Answer: "Mama Do" debuted at #1 on the UK Singles Chart in its first week and also was successful outside the UK, appearing in the Top 10 in Denmark and France and the Top 20 in Ireland, the Netherlands, and New Zealand. The British Phonographic Industry certified it as Silver the same year it was released and upgraded the certification to Gold eleven years later.
Question: Give me a list of the best right point breaks in Southern California
Answer: Rincon, Malibu, Trestles
Question: What task do they evaluate on?
Evidence: We demonstrate our approach on the task of answering open-domain fill-in-the-blank natural language questions. By giving open vocabulary semantic parsers direct access to KB information, we improve mean average precision on this task by over 120%.
We demonstrate our approach on the task of answering open-domain fill-in-the-blank natural language questions.
Answer: Fill-in-the-blank natural language questions
Question: Seven Wonders of the Underwater World
Answer: Here's a list of the Seven Wonders of the Underwater World:

- Palau
- Belize Barrier Reef, Belize
- Great Barrier Reef, Australia
- Deep-sea hydrothermal vents (worldwide)
- Galápagos Islands, Ecuador
- Lake Baikal, Russia
- Northern Red Sea, bordered by Saudi Arabia and Yemen on the eastern shore, and Egypt, Sudan, Eritrea, and Djibouti on the western shore
Question: How much better was the BLSTM-CNN-CRF than the BLSTM-CRF?
Evidence: Table 2 shows our experiments on two models with and without different pre-trained word embedding – KP means the Kyubyong Park’s pre-trained word embeddings and EG means Edouard Grave’s pre-trained word embeddings.
FLOAT SELECTED: Table 2. F1 score of two models with different pre-trained word embeddings
Table 2 shows our experiments on two models with and without different pre-trained word embedding – KP means the Kyubyong Park’s pre-trained word embeddings and EG means Edouard Grave’s pre-trained word embeddings.
FLOAT SELECTED: Table 2. F1 score of two models with different pre-trained word embeddings
Answer: Best BLSTM-CNN-CRF had F1 score 86.87 vs 86.69 of best BLSTM-CRF
Question: List three US states whose name begins with C. Then tell me the capital of the first one.
Answer: Three US states that begin with C are:

1. California
2. Colorado
3. Connecticut

The state capital of California is Sacramento.
Question: What season is the best season to visit Chicago?
Answer: The best 90 days of the year in Chicago is summer. The weather is usually always hot! You can run/walk/bike on the lakefront path, rent a boat and go to the "playpen", enjoy a Chicago street festival. Everyone is out and about and it's a marked change from winter.
Question: What is the dataset used?
Evidence: The corpus consists of a collection of 2,346 clinical notes (admission notes, progress notes, and discharge summaries), which amounts to 2,372,323 tokens in total (an average of 1,011 tokens per note). All the notes were written in English and extracted from the EHRs of 183 psychosis patients from McLean Psychiatric Hospital in Belmont, MA, all of whom had in their history at least one instance of 30-day readmission.
The corpus consists of a collection of 2,346 clinical notes (admission notes, progress notes, and discharge summaries), which amounts to 2,372,323 tokens in total (an average of 1,011 tokens per note). All the notes were written in English and extracted from the EHRs of 183 psychosis patients from McLean Psychiatric Hospital in Belmont, MA, all of whom had in their history at least one instance of 30-day readmission.
Answer: The answers are shown as follows:
* EHRs of 183 psychosis patients from McLean Psychiatric Hospital in Belmont, MA
Question: How many countries do the Netherlands share a land border with?
Answer: The Netherlands shares a land border with four countries: Germany, Belgium, Luxembourg, and the Netherlands Antilles.
Question: What does recurrent deep stacking network do?
Evidence: As indicated in its name, Recurrent Deep Stacking Network stacks and concatenates the outputs of previous frames into the input features of the current frame. If we view acoustic models in ASR systems as functions projecting input features to the probability density outputs, we can see the differences between conventional systems and RDSN clearer. Denote the input features at frame $t$ as $x_t$ , and the output as frame $t$ as $y_t$ . We can see that RDSN tries to model
As indicated in its name, Recurrent Deep Stacking Network stacks and concatenates the outputs of previous frames into the input features of the current frame.
Answer: Stacks and joins outputs of previous frames with inputs of the current frame
Question: Retrieve the NFL teams Doug Peterson was affiliated with both as a player and coach in the text
Evidence: Douglas Irvin Pederson (born January 31, 1968) is an American football coach and former quarterback who is the head coach for the Jacksonville Jaguars of the National Football League (NFL). Pederson spent most of his 13-season playing career as a backup to Brett Favre on the Green Bay Packers, where he was part of the team that won a Super Bowl title in Super Bowl XXXI. He was also a backup to Dan Marino on the Miami Dolphins and a starter for the Philadelphia Eagles and Cleveland Browns until retiring in 2004.

Pederson began his coaching career under Andy Reid, serving as an assistant for the Eagles from 2009 to 2012. After Reid became the head coach of the Kansas City Chiefs in 2013, Pederson followed him to serve as the Chiefs' offensive coordinator. He returned to the Eagles as their head coach in 2016, a position he held for five seasons. His greatest success was when he led the franchise to its first Super Bowl title in 2017's Super Bowl LII, making him one of four individuals to win a Super Bowl as a player and head coach.
Answer: Jacksonville Jaguars, Green Bay Packers, Miami Dolphins,  Philadelphia Eagles, Cleveland Browns, Kansas City Chiefs