1 . Last week, I sent the same request to ChatGPT, the latest artificial-intelligence chatbot from OpenAI. “Upon the Firth of Forth, a bridge doth stand,” it began. In less than a minute, the program had created in full a rhyming Shakespearean sonnet (莎士比亚十四行诗). Tools like ChatGPT seem poised to change the world of poetry — and so much else — but poets also have a lot to teach us about artificial intelligence. If algorithms (算法) are getting good at writing poetry, it’s partially because poetry was always an algorithmic business.
Even the most rebellious (叛逆的) poets follow more rules than they might like to admit. When schoolchildren are taught to imitate the structure of sonnet, they are effectively learning to follow algorithmic constraints. Should it surprise us that computers can do so, too?
But considering how ChatGPT works, its ability to follow the rules for sonnets seems a little more impressive. No one taught it these rules. It is based on a newer kind of AI known as a large language model (LLM). To put it simply, LLMs analyze large amounts of human writing and learn to predict what the next word in a string of text should be, based on context. One frequent criticism of LLMs is that they do not understand what they write; they just do a great job of guessing the next word.
When a private verse by Dickinson makes us feel like the poet speaks directly to us, we are experiencing the effects of a technology called language. Poems are made of paper and ink — or, these days, electricity and light. There is no one “inside” a Dickinson poem any more than one by ChatGPT. Of course, every Dickinson poem reflects her intention to create meaning. When ChatGPT puts words together, it does not intend anything. Some argue that writings by LLMs therefore have no meaning, only the appearance of it. If I see a cloud in the sky that looks like a giraffe, I recognize it as an accidental similarity. In the same way, this argument goes, we should regard the writings of ChatGPT as merely imitating real language, meaningless and random as cloud shapes.
When I showed my friends the sonnet by ChatGPT, they called it “soulless and barren.” Despite following all the rules for sonnets, the poem is predictable. But is the average sonnet by a human any better? If we now expect computers to write not just poems but good poems, then we have set a much higher bar.
1. What is the main idea of paragraph 1 and paragraph 2?| A．ChatGPT will make a difference to poetry based on algorithms. |
| B．There is no doubt that AI can copy the grammatical rules of poetry. |
| C．Poetry guidelines provide a possibility for AI’s poetry writing. |
| D．There is a similarity between algorithms and poetry. |
| A．ChatGPT is trained to follow the rules by LLMs. |
| B．ChatGPT can analyze and predict human languages. |
| C．ChatGPT is technologically supported by LLMs. |
| D．ChatGPT itself learn to follow the rules. |
| A．He talks about cloud to describe the meaninglessness of AI’s poetry. |
| B．He tells of Dickinson to describe the meaninglessness AI’s poetry. |
| C．He mentions cloud to suggest its close relationship with AI’s poetry. |
| D．He refers to Dickinson to suggest her close relationship with AI’s poetry. |
| A．Acceptable and favorable | B．Amazed and admiring |
| C．Indifferent and uncaring | D．Doubtful and uneasy |
1.分析产生这一现象原因;
2.该现象造成的不良影响;
3.发出积极的倡议。
注意:
1.写作词数应为80左右;
2.短文的题目和首句已为你写好(不计入总词数)。
Too much expenditure on fashion
Recently, an increasing number of students are pursuing fashion in our class.
_______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________1. What’s the talk mainly about?
| A．The development of the bicycle. |
| B．The materials of making the bicycle. |
| C．The process of the bicycle’s becoming popular. |
| A．The bicycle couldn’t change directions. |
| B．The wheels of the bicycle weren’t well fixed. |
| C．The rider’s feet couldn’t leave the ground. |
| A．In 1817. | B．In 1839. | C．In 1869. |
| A．Bicycles first had rubber tires. |
| B．The “safety bicycle” appeared. |
| C．Bicycles could run faster. |
4 . The Great PowerPoint Panic of 2003.
Sixteen minutes before touchdown on the morning of February 1, 2003, the space shuttle Columbia (“哥伦比亚”号航天飞机)
The immediate
By the start of 2003, the phrase “death by PowerPoint” had well and truly entered the
Wired ran an excerpt (节选) from Tufte’s booklet in September 2003 under the headline “PowerPoint Is Evil.” A few months later, The New York Times Magazine included his assessment — summarized as “PowerPoint Makes You Dumb” — in its
Despite the backlash it inspired in the
On its face at least, the idea that PowerPoint makes us stupid looks like a textbook case of misguided technological doomsaying. Today’s concerns about social media somehow resemble the PowerPoint critique. Both boil down to a worry that new media technologies
| A．disappeared | B．disintegrated | C．distributed | D．disappointed |
| A．side | B．cause | C．feature | D．issue |
| A．collected | B．unified | C．dropped | D．single |
| A．discounted | B．viewed | C．accessed | D．founded |
| A．muted | B．absorbed | C．buried | D．sunk |
| A．technical | B．popular | C．negative | D．special |
| A．possibly | B．reasonably | C．ordinarily | D．necessarily |
| A．accommodated | B．combined | C．distinguished | D．enhanced |
| A．abstract | B．repetition | C．review | D．brief |
| A．press | B．publication | C．media | D．criticism |
| A．opened | B．created | C．threw | D．jumped |
| A．rules | B．harmonizes | C．impacts | D．roars |
| A．feature | B．encourage | C．value | D．defend |
| A．Therefore | B．However | C．Certainly | D．Surprisingly |
| A．difference | B．truth | C．time | D．concern |
注意：1. 词数80左右；2. 可以适当增加细节，以使行文连贯：3. 开头和结尾已给出，不计入总词数。
Dear Professor Williams,
I am honored to give a presentation about Chinese culture to international students at your invitation.
___________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
Sincerely yours,
Li Hua
1.由外语教学与研究出版社出版；
2.每本书均由世界名著改写；
3.分级阅读，共分六级；
4.一级对应初一学生 ，六级对应高三学生。
____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
Mental health conditions, including everything from depression and phobias (恐惧) to anorexia (厌食) and schizophrenia (精神分裂症) are shockingly common. In the UK, one in four people experience them each year, so it is likely that you, or someone you know, has sought help from a professional. That process usually begins with a diagnosis. Then you start on a treatment tailored to your condition. It seems an obvious approach, but is it the right one? “For millennia, we’ve put all these psychiatric (神经病的) conditions in separate corners,” says neuroscientist Anke Hammerschlag at Vrije University Amsterdam, the Netherlands. “But maybe that’s not how it works biologically.”
There is growing evidence that she is correct. Instead of being separate conditions, many mental health problems appear to share an underlying cause, something researchers now call the “P factor”. This realization could thoroughly change how we diagnose and treat mental health conditions, putting more focus on symptoms instead of labels and offering more general treatments. It also explains puzzling patterns in the occurrence of these conditions in individuals and families. Rethinking mental health this way could be revolutionary.
At first glance, the idea that different mental health conditions with distinct symptoms share an underlying cause seems unrealistic. The key to understanding it lies in its name. “P factor” has intentional parallels with one of the most famous concepts in psychology. More than a century ago, British psychologist Charles Spearman noted that children’s performance on one kind of mental task, say verbal fluency, was correlated with their mental skill in other areas, like mathematical reasoning, spatial manipulation and logic. In other words, children who are good at one thing tend to be good at another, while those who struggle in one area tend to struggle in others. Using a statistical tool called factor analysis, Spearman showed that this is because these different mental abilities are all linked to an overarching cognitive capacity, which he named general intelligence, or the “G factor”.
________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
8 . On March 7, 1907, the English statistician Francis Galton published a paper which illustrated what has come to be known as the “wisdom of crowds” effect. The experiment of estimation he conducted showed that in some cases, the average of a large number of independent estimates could be quite accurate.
This effect capitalizes on the fact that when people make errors, those errors aren’t always the same. Some people will tend to overestimate, and some to underestimate. When enough of these errors are averaged together, they cancel each other out, resulting in a more accurate estimate. If people are similar and tend to make the same errors, then their errors won’t cancel each other out. In more technical terms, the wisdom of crowds requires that people’s estimates be independent. If for whatever reasons, people’s errors become correlated or dependent, the accuracy of the estimate will go down.
But a new study led by Joaquin Navajas offered an interesting twist (转折) on this classic phenomenon. The key finding of the study was that when crowds were further divided into smaller groups that were allowed to have a discussion, the averages from these groups were more accurate than those from an equal number of independent individuals. For instance, the average obtained from the estimates of four discussion groups of five was significantly more accurate than the average obtained from 20 independent individuals.
In a follow-up study with 100 university students, the researchers tried to get a better sense of what the group members actually did in their discussion. Did they tend to go with those most confident about their estimates? Did they follow those least willing to change their minds? This happened some of the time, but it wasn’t the dominant response. Most frequently, the groups reported that they “shared arguments and reasoned together”. Somehow, these arguments and reasoning resulted in a global reduction in error. Although the studies led by Navajas have limitations and many questions remain, the potential implications for group discussion and decision-making are enormous.
1. What is paragraph 2 of the text mainly about?| A．The methods of estimation. | B．The underlying logic of the effect. |
| C．The causes of people’s errors. | D．The design of Galton’s experiment. |
| A．the crowds were relatively small | B．there were occasional underestimates |
| C．individuals did not communicate | D．estimates were not fully independent |
| A．The size of the groups. | B．The dominant members. |
| C．The discussion process. | D．The individual estimates. |
| A．Unclear. | B．Dismissive. | C．Doubtful. | D．Approving. |
注意：词数100左右。
___________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
1. Why are the two speakers upset?
| A．It may snow during their vacation. |
| B．They may not be able to take their vacation. |
| C．They may fail to join the graduation ceremony. |
| A．They are going skiing. |
| B．They have made bookings for their plane. |
| C．Their flight has been cancelled. |
| A．The earthquake. |
| B．The bad winter. |
| C．A terrible flu. |
| A．Talk to Professor Hampton. |
| B．Speak to all of the other people. |
| C．Call the travel agency. |
