Example G (Passage from Wikipedia)
Passage	Interstellar is a 2014 epic science fiction film co-written, directed and produced by Christopher Nolan. It stars Matthew McConaughey, Anne Hathaway, Jessica Chastain, Bill Irwin, Ellen Burstyn, and Michael Caine. Set in a dystopian future where humanity is struggling to survive, the film follows a group of astronauts who travel through a wormhole near Saturn in search of a new home for mankind. Paramount Pictures, Warner Bros. Pictures, and Legendary Pictures co-financed the film. Cinematographer Hoyte van Hoytema shot it on 35 mm in the Panavision anamorphic format and IMAX 70 mm. Interstellar uses extensive practical and miniature effects and the company Double Negative created additional digital effects. Interstellar premiered on October 26, 2014, in Los Angeles, California. In the United States, it was first released on film stock, expanding to venues using digital projectors. The film had a worldwide gross of over $677 million (and $701 million with subsequent re-releases), making it the tenth-highest-grossing film of 2014.
Dialogue	[Teacher bot]: Interstellar is a 2014 epic science fiction film directed and produced by Christopher Nolan.
[Human user]: Who stars in the film?
[Teacher bot]: The film stars Matthew McConaughey, Anne Hathaway, Jessica Chastain, Bill Irwin, Ellen Burstyn, and Michael Caine.
[Human user]: What story does it tell?
[Teacher bot]: The film follows a group of astronauts who travel through a wormhole near Saturn.
Example H (Passage from CNN News)
Passage	The US Food and Drug Administration on Monday granted full approval to the Pfizer/BioNTech Covid-19 vaccine for people age 16 and older. This is the first coronavirus vaccine approved by the FDA, and is expected to open the door to more vaccine mandates. The vaccine will be marketed as Comirnaty, the FDA said in its announcement on Monday. The Pfizer/BioNTech vaccine has been authorized for emergency use in the United States since mid-December for people age 16 and older, and in May, the authorization was extended to those 12 and older. FDA says it's working as fast as possible to fully approve vaccines, as urgency rises amid Covid surge FDA says it's working as fast as possible to fully approve vaccines, as urgency rises amid Covid surge ...
Dialogue	[Teacher bot]: The Pfizer/BioNTech vaccine has been authorized for emergency use in the United States since mid-December for people age 16 and older.
[Human user]: Give me more details.
[Teacher bot]: This is the first coronavirus vaccine approved by the FDA, and is expected to open the door to more vaccine mandates.
[Human user]: So, what will happen next?
[Teacher bot]: The Pfizer/BioNTech Covid-19 vaccine for people age 16 and older will be marketed as Comirnaty, the FDA said in its announcement on Monday.
Example I (Passage from Paper Abstracts)
Passage	We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).
Dialogue	[Teacher bot]: We introduce BERT, which stands for Bidirectional Encoder Representations from Transformers.
[Human user]: What did you propose?
[Teacher bot]: A new language representation model called BERT which is conceptually simple and empirically powerful.
[Human user]: How is the result?
[Teacher bot]: The pre-trained BERT model can be fine-tuned with just one additional additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications.