DataHammer/scimrc huggingface dataset:
Question: What are the key challenges in handling document flow?
Evidence: Following developing news stories is imperative to making real-time decisions on important political and public safety matters. Given the abundance of media providers and languages, this endeavor is an extremely difficult task. As such, there is a strong demand for automatic clustering of news streams, so that they can be organized into stories or themes for further processing. Performing this task in an online and efficient manner is a challenging problem, not only for newswire, but also for scientific articles, online reviews, forum posts, blogs, and microblogs.
A key challenge in handling document streams is that the story clusters must be generated on the fly in an online fashion: this requires handling documents one-by-one as they appear in the document stream. In this paper, we provide a treatment to the problem of online document clustering, i.e. the task of clustering a stream of documents into themes. For example, for news articles, we would want to cluster them into related news stories.
To this end, we introduce a system which aggregates news articles into fine-grained story clusters across different languages in a completely online and scalable fashion from a continuous stream. Our clustering approach is part of a larger media monitoring project to solve the problem of monitoring massive text and TV/Radio streams (speech-to-text). In particular, media monitors write intelligence reports about the most relevant events, and being able to search, visualize and explore news clusters assists in gathering more insight about a particular story. Since relevant events may be spawned from any part of the world (and from many multilingual sources), it becomes imperative to cluster news across different languages.
In terms of granularity, the type of story clusters we are interested in are the group of articles which, for example : (i) Narrate recent air-strikes in Eastern Ghouta (Syria); (ii) Describe the recent launch of Space X's Falcon Heavy rocket.
A key challenge in handling document streams is that the story clusters must be generated on the fly in an online fashion: this requires handling documents one-by-one as they appear in the document stream.
Answer: This requires handling documents one-by-one as they appear in the document stream.
Question: Explain inter frame
Evidence: An inter frame is a frame in a video compression stream which is expressed in terms of one or more neighboring frames. The "inter" part of the term refers to the use of Inter frame prediction. This kind of prediction tries to take advantage from temporal redundancy between neighboring frames enabling higher compression rates.
Answer: An inter frame is a frame in a video compression stream which is expressed in terms of one or more neighboring frames. The "inter" part of the term refers to the use of Inter frame prediction. This kind of prediction tries to take advantage from temporal redundancy between neighboring frames enabling higher compression rates.
Question: Identify which car manufacturer is British or American: Lotus, Lincoln
Answer: Lotus is British, Lincoln is American
Question: Given this paragraph about the Women's Education Association, which institutions of higher learning had affiliations with the Association?
Evidence: The aid of the Association was also given to the formation of a class of women for the study of advanced chemistry at the laboratory of the Girls' High School. This class was found to meet so great a want that a chemical laboratory has been fitted up in connection with the Massachusetts Institute of Technology, and furnished by money collected by the association with apparatus and instruments, such as microscopes, spectroscopes, &c. From time to time pecuniary aid has also been given to young women seeking an advanced education. But the chief work of the Association —the first undertaken and still carried on under its charge- is a plan of examinations known as the Harvard Examinations for Women. These originated in a desire to raise the standard of education in the schools for girls, and are held under the personal supervision of members of the Association. Examination papers, prepared by Professors at Harvard University, are provided by the Association, and those women who successfully pass this examination receive a certificate from the university stating the fact. Branch societies have been established in New York City, Philadelphia, and Cincinnati for the same purpose.
Answer: The Massachusetts Institute of Technology and Harvard University
Question: Identify which instrument is string or percussion: Mridangam, Gambus
Answer: Gambus is string, Mridangam is percussion.
Question: From the passage provided, list the awards won. Include when they were won and by whom.
Evidence: "I'm the Urban Spaceman" was the Bonzo Dog Doo-Dah Band's most successful single, released in 1968. It reached #5 in the UK charts. The song was written by Neil Innes—who won an Ivor Novello Award in 1968 for the song—and produced by Paul McCartney under the pseudonym "Apollo C. Vermouth". The B-side was written by Vivian Stanshall. A well-known staging of the song involves Innes performing solo while a female tap dancer performs an enthusiastic but apparently under-rehearsed routine around him. This skit originally appeared in a 1975 edition of Rutland Weekend Television, with Lyn Ashley as the dancer, and was more famously revived in the 1982 film Monty Python Live at the Hollywood Bowl with Carol Cleveland taking over the role.

Leeds indie rock band Cud performed an extremely fast version (1:07 long) for a 1989 Peel Session. The recording appears on their albums Elvis Belt and BB Cudn't C.
Answer: Neil Innes won the Ivor Novello Award in 1968 for writing the song "I'm the Urban Spaceman".
Question: Classify each of the following as straight or curved: banana, baseball bat, hook, rainbow, convex lens, ruler
Answer: Banana: Curved
Baseball Bat: Straight
Hook: Curved
Rainbow: Curved
Convex Lens: Curved
Ruler: Straight
Question: Who is Marina Vishmidt
Evidence: Marina Vishmidt (born 1976) is an American writer, editor and critic. She lectures at the Centre for Cultural Studies at Goldsmiths, University of London in the MA program Culture Industry, and teaches Art Theory in the MA Art Praxis at the Dutch Art Institute in Arnhem. Her research mainly concerns the relationship between art, value and labour. She further explores this through works on debt, social reproduction and artistic entrepreneurialism. In 2013, she completed her PhD entitled 'Speculation as a Mode of Production in Art and Capital' at the Queen Mary University of London.
Answer: Marina Vishmidt (born 1976) is an American writer, editor and critic. She lectures at the Centre for Cultural Studies at Goldsmiths, University of London in the MA program Culture Industry, and teaches Art Theory in the MA Art Praxis at the Dutch Art Institute in Arnhem. Her research mainly concerns the relationship between art, value and labour.

She further explores this through works on debt, social reproduction and artistic entrepreneurialism. In 2013, she completed her PhD entitled 'Speculation as a Mode of Production in Art and Capital' at the Queen Mary University of London.
Question: Think up a new seasoning blend that will be as popular as "everything but the bagel" seasoning.
Answer: A new popular seasoning could be a sour cream and onion seasoning reminiscent of the popular chip flavor. You can sprinkle it on a myriad of items for a delicious savory boost.
Question: Give me a list of the best movies I've never seen.
Answer: I would definitely recommend to watch Lord of the Rings. It is a great fantasy movie about friendship and how in the end, the good always prevails. The scale and grandeur, also the cast of the movie was superb. Any film where Tom Hanks is in the lead role should also be watched, as an actor he is simply amazing, and comes across so natural and convincing. If you like action movies, I can heartily recommend all the James Bond movies with Daniel Craig in the lead role. The cinematography, tension, music all play together so well. Speaking of cinematography, one would be amiss to not watch the latest movies of David Villeneuve, for example Inception or Dune. Timothee Chalamais, who is leading role in Dune, also did a great performance in 'The King' - if you liked the character in Dune for example. Other movies would be Interstellar, a great Sci-Fi movie, the Hunger Games trilogy and so many more.
Question: How do they select monotonicity facts?
Evidence: MonaLog utilizes two auxiliary sets. First, a knowledge base ${K}$ that stores the world knowledge needed for inference, e.g., semanticist $\le $ linguist and swim $\le $ move, which captures the facts that $[\![\mbox{\em semanticist}]\!]$ denotes a subset of $[\![\mbox{\em linguist}]\!]$, and that $[\![\mbox{\em swim}]\!]$ denotes a subset of $[\![\mbox{\em move}]\!]$, respectively. Such world knowledge can be created manually for the problem at hand, or derived easily from existing resources such as WordNet BIBREF22. Note that we do not blindly add all relations from WordNet to our knowledge base, since this would hinge heavily on word sense disambiguation (we need to know whether the “bank” is a financial institution or a river bank to extract its relations correctly). In the current implementation, we avoid this by adding x $\le $ y or x $\perp $ y relations only if both x and y are words in the premise-hypothesis pair. Additionally, some relations that involve quantifiers and prepositions need to be hard-coded, since WordNet does not include them: every $=$ all $=$ each $\le $ most $\le $ many $\le $ a few $=$ several $\le $ some $=$ a; the $\le $ some $=$ a; on $\perp $ off; up $\perp $ down; etc.
MonaLog utilizes two auxiliary sets. First, a knowledge base ${K}$ that stores the world knowledge needed for inference, e.g., semanticist $\le $ linguist and swim $\le $ move, which captures the facts that $[\![\mbox{\em semanticist}]\!]$ denotes a subset of $[\![\mbox{\em linguist}]\!]$, and that $[\![\mbox{\em swim}]\!]$ denotes a subset of $[\![\mbox{\em move}]\!]$, respectively. Such world knowledge can be created manually for the problem at hand, or derived easily from existing resources such as WordNet BIBREF22. Note that we do not blindly add all relations from WordNet to our knowledge base, since this would hinge heavily on word sense disambiguation (we need to know whether the “bank” is a financial institution or a river bank to extract its relations correctly). In the current implementation, we avoid this by adding x $\le $ y or x $\perp $ y relations only if both x and y are words in the premise-hypothesis pair. Additionally, some relations that involve quantifiers and prepositions need to be hard-coded, since WordNet does not include them: every $=$ all $=$ each $\le $ most $\le $ many $\le $ a few $=$ several $\le $ some $=$ a; the $\le $ some $=$ a; on $\perp $ off; up $\perp $ down; etc.
Answer: They derive it from Wordnet
Question: Classify each of the following as a professional golfer or professional football player: Jon Rahm, Patrick Mahomes, Tom Brady, Max Homa, Scottie Scheffler, Tiger Woods, Rory McIlroy, Josh Allen, Jacoby Brissett.
Answer: Jon Rahm is a professional golfer.
Patrick Mahomes is a professional football player.
Tom Brady is a professional football player.
Max Homa is a professional golfer.
Scottie Scheffler is a professional golfer.
Tiger Woods is a professional golfer.
Rory McIlroy is a professional golfer.
Josh Allen is a professional football player.
Jacoby Brissett is a professional football player.
Question: Briefly explain what are I frames, B frames, and P frames?
Evidence: In the field of video compression a video frame is compressed using different algorithms with different advantages and disadvantages, centered mainly around amount of data compression. These different algorithms for video frames are called picture types or frame types. The three major picture types used in the different video algorithms are I, P and B. They are different in the following characteristics:

I‑frames are the least compressible but don't require other video frames to decode.
P‑frames can use data from previous frames to decompress and are more compressible than I‑frames.
B‑frames can use both previous and forward frames for data reference to get the highest amount of data compression.

Three types of pictures (or frames) are used in video compression: I, P, and B frames.

An I‑frame (Intra-coded picture) is a complete image, like a JPG or BMP image file.

A P‑frame (Predicted picture) holds only the changes in the image from the previous frame. For example, in a scene where a car moves across a stationary background, only the car's movements need to be encoded. The encoder does not need to store the unchanging background pixels in the P‑frame, thus saving space. P‑frames are also known as delta‑frames.

A B‑frame (Bidirectional predicted picture) saves even more space by using differences between the current frame and both the preceding and following frames to specify its content.

P and B frames are also called Inter frames. The order in which the I, P and B frames are arranged is called the Group of pictures.
Answer: I frames, B frames, and P frames are video frame types used in video codecs. I frames can be decoded without reference to any other frames. They are self-contained descriptions of one frame of the video. P frames and B frames on the other hand reference other frames and must be decoded after the frames they reference have been decoded. P frames only reference frames from earlier in the video whereas B frames can reference frames from earlier or later. Decoding B frames therefore requires decoding the video frames in an order different from the playback order.
Question: Extract when were the universities of Palencia and Salamanca established
Evidence: The 13th century also witnessed the Crown of Aragon, centred in Spain's north east, expand its reach across islands in the Mediterranean, to Sicily and Naples. Around this time the universities of Palencia (1212/1263) and Salamanca (1218/1254) were established. The Black Death of 1348 and 1349 devastated Spain.
Answer: The University of Palencia was established between 1212 and 1263 and the University of Salamanca was established between 1218 and 1254.
Question: What datasets are used to evaluate this approach?
Evidence: Since the setting is quite different from traditional adversarial attacks, search for link prediction adversaries brings up unique challenges. To find these minimal changes for a target link, we need to identify the fact that, when added into or removed from the graph, will have the biggest impact on the predicted score of the target fact. Unfortunately, computing this change in the score is expensive since it involves retraining the model to recompute the embeddings. We propose an efficient estimate of this score change by approximating the change in the embeddings using Taylor expansion. The other challenge in identifying adversarial modifications for link prediction, especially when considering addition of fake facts, is the combinatorial search space over possible facts, which is intractable to enumerate. We introduce an inverter of the original embedding model, to decode the embeddings to their corresponding graph components, making the search of facts tractable by performing efficient gradient-based continuous optimization. We evaluate our proposed methods through following experiments. First, on relatively small KGs, we show that our approximations are accurate compared to the true change in the score. Second, we show that our additive attacks can effectively reduce the performance of state of the art models BIBREF2 , BIBREF10 up to $27.3\%$ and $50.7\%$ in Hits@1 for two large KGs: WN18 and YAGO3-10. We also explore the utility of adversarial modifications in explaining the model predictions by presenting rule-like descriptions of the most influential neighbors. Finally, we use adversaries to detect errors in the KG, obtaining up to $55\%$ accuracy in detecting errors.
WN18 and YAGO3-10
Second, we show that our additive attacks can effectively reduce the performance of state of the art models BIBREF2 , BIBREF10 up to $27.3\%$ and $50.7\%$ in Hits@1 for two large KGs: WN18 and YAGO3-10. 
Answer: The answers are shown as follows:
* WN18 and YAGO3-10
Question: Identify which instrument is string or percussion: Atumpan, Axatse, Baryton
Answer: Atumpan and Axatse are percussion, Baryton is string.
Question: What is Growing Season?
Evidence: A season is a division of the year marked by changes in weather, ecology, and the amount of daylight. The growing season is that portion of the year in which local conditions (i.e. rainfall, temperature, daylight) permit normal plant growth. While each plant or crop has a specific growing season that depends on its genetic adaptation, growing seasons can generally be grouped into macro-environmental classes.
Answer: Season extension in agriculture is any method that allows a crop to be grown beyond its normal outdoor growing season and harvesting time frame, or the extra time thus achieved.
Question: Classify the cities based on whether they are located in Namibia or Lithuania.
Klaipėda, Oshakati, Kaunas, Windhoek, Vilnius, Rundu
Answer: Namibia - Windhoek, Rundu, Oshakati
Lithuania - Vilnius, Kaunas, Klaipėda
Question: Which onsen villages in Japan are the best to visit for foreign tourists? And what should I do once I'm in an onsen village?
Answer: There are many great onsen villages in Japan in which tourists of any nationals can enjoy the experience of bathing in natural hot springs. Some great examples are Hakone, Kusatsu and Nyuto Onsen. Ideally, you would want to spend a day or two in a village as these villages are often located in beautiful nature and offer great opportunities for hiking and walking.
Question: Do they explore how useful is the detection history and opinion summary?
Evidence: Ablation Study
To further investigate the efficacy of the key components in our framework, namely, THA and STN, we perform ablation study as shown in the second block of Table TABREF39 . The results show that each of THA and STN is helpful for improving the performance, and the contribution of STN is slightly larger than THA. “OURS w/o THA & STN” only keeps the basic bi-linear attention. Although it performs not bad, it is still less competitive compared with the strongest baseline (i.e., CMLA), suggesting that only using attention mechanism to distill opinion summary is not enough. After inserting the STN component before the bi-linear attention, i.e. “OURS w/o THA”, we get about 1% absolute gains on each dataset, and then the performance is comparable to CMLA. By adding THA, i.e. “OURS”, the performance is further improved, and all state-of-the-art methods are surpassed.
Ablation Study
To further investigate the efficacy of the key components in our framework, namely, THA and STN, we perform ablation study as shown in the second block of Table TABREF39 . The results show that each of THA and STN is helpful for improving the performance, and the contribution of STN is slightly larger than THA. “OURS w/o THA & STN” only keeps the basic bi-linear attention. Although it performs not bad, it is still less competitive compared with the strongest baseline (i.e., CMLA), suggesting that only using attention mechanism to distill opinion summary is not enough. After inserting the STN component before the bi-linear attention, i.e. “OURS w/o THA”, we get about 1% absolute gains on each dataset, and then the performance is comparable to CMLA. By adding THA, i.e. “OURS”, the performance is further improved, and all state-of-the-art methods are surpassed.
Answer: Yes.
Question: What two architectures are used?
Evidence: As a first experiment, we compare the quality of fastText embeddings trained on (high-quality) curated data and (low-quality) massively extracted data for Twi and Yorùbá languages.
The huge ambiguity in the written Twi language motivates the exploration of different approaches to word embedding estimations. In this work, we compare the standard fastText methodology to include sub-word information with the character-enhanced approach with position-based clustered embeddings (CWE-LP as introduced in Section SECREF17). With the latter, we expect to specifically address the ambiguity present in a language that does not translate the different oral tones on vowels into the written language.
As a first experiment, we compare the quality of fastText embeddings trained on (high-quality) curated data and (low-quality) massively extracted data for Twi and Yorùbá languages.
The huge ambiguity in the written Twi language motivates the exploration of different approaches to word embedding estimations. In this work, we compare the standard fastText methodology to include sub-word information with the character-enhanced approach with position-based clustered embeddings (CWE-LP as introduced in Section SECREF17).
Answer: The answers are shown as follows:
* fastText
* CWE-LP
Question: Did the Bisons have a winning season in 1958?
Evidence: The 1958 North Dakota State Bison football team was an American football team that represented North Dakota State University during the 1958 NCAA College Division football season as a member of the North Central Conference. In their second year under head coach Bob Danielson, the team compiled a 3–6 record.
Answer: No, the Bisons had a losing season in 1958, only winning 3 of their 9 games and losing 6.
Question: What is the NBA All-Star game?
Answer: The NBA All-Star game is a mid-season game where the players who are voted as having the best seasons so far play against each other.
Question: Write me a text to someone I work with asking them to cover my shift because I got sick. Use a polite tone.
Answer: Hi <insert name here>, I’m running a fever and my doctor recommended I find someone else to take my shift. Unfortunately it’s contagious and I’m worried that I’ll get everyone else sick if I go. Is there any chance you could step in for me? I’ll owe you one big time, but totally get it if this is too last minute. Let me know!
Question: What corpus do they use to evaluate the session segmentation method?
Evidence: To evaluate the session segmentation method, we used a real-world chatting corpus from DuMi, a state-of-the-practice open-domain conversation system in Chinese. We sampled 200 sessions as our experimental corpus. Session segmentation was manually annotated before experiments, serving as the ground truth. The 200 sessions were randomly split by 1:1 for validation and testing. Notice that, our method does not require labeled training samples; massive data with labels of high quality are quite expensive to obtain.
We also leveraged an unlabeled massive dataset of conversation utterances to train our word embeddings with “virtual sentences.” The dataset was crawled from the Douban forum, containing 3 million utterances and approximately 150,000 unique words (Chinese terms).
To evaluate the session segmentation method, we used a realworld chatting corpus from DuMi,3 a state-of-the-practice open-domain conversation system in Chinese. We sampled 200 sessions as our experimental corpus. Session segmentation was manually annotated before experiments, serving as the ground truth. The 200 sessions were randomly split by 1:1 for validation and testing. Notice that, our method does not require labeled training samples; massive data with labels of high quality are quite expensive to obtain. We also leveraged an unlabeled massive dataset of conversation utterances to train our word embeddings with “virtual sentences.” The dataset was crawled from the Douban forum,4 containing 3 million utterances and approximately 150,000 unique words (Chinese terms).
Answer: A real world chatting corpus from DuMi and an unlabeled massive dataset of conversation utterances