| in addition turn one’s back break down a variety of live in harmony run out maintain friendships be concerned with |
A
Friendship clubs provide a platform for people to take part in
B
Nature is our home. Humans and other living things on the planet couldn’t survive without the resources that come from nature. We should take care of our “home” before it’s too late. If we keep overusing and destroying nature, it will finally
YOLO, directed by Jia Ling, was the highest-grossing film in China over the Lunar New Year.
The use of avatars has caused a few concerns. Some users worry that they are spending so much time in virtual worlds
| A．concentrate | B．concern | C．confirm | D．communicate |
| A．damage | B．anxious | C．escape | D．imagine |
| A．calm | B．rewarding | C．alarm | D．half |
| A．loss | B．operation | C．ecology | D．focus |
| A．crowded | B．account | C．download | D．shallow |
Most students feel their IQ is what determines how well they are going to do in life, but new research suggests that EQ is a better way for achieving success. Professor Salovey says it is IQ that gets you
| base on on the edge of run out breathe in in turn survive the moment attempt turn one’s back on fight for take shelter from have ... in mind apart from admire |
1. They were done fighting for others now, they
2. “Reward? What did you
3. Which is more difficult,
4. The film
5.
6. He
7. Human beings are infinitely flexible and able to adjust when
8. The boy’s heroic behavior is
9. Time was
10. The frog
9 . I hadn’t seen Anne in nearly 20 years since college, yet we could still party like old times. It was great to have her here,
She was looking at the few blooms (花) left in my yard. I hadn’t planted much after losing my job. It had been a
“One of my hobbies is taking photos of
Suddenly a hot song rang from her cell phone. “I set it to remind me to take my medicine,” she said calmly.
“An
“For my brain,” she smiled. “I have been diagnosed with a rare cancer, a small tumor (肿块) no bigger than your fingernail,” she laughed softly. That was Anne — ever
So
Later next day, an e-mail filled with the flower photos popped up from Anne — clear and beautiful. She had gotten past the anger, the pity and unfairness, taking one moment at a time and polishing it until it
I shifted my eye to outside, and I had her flowers in full bloom. Actually, I always had them, but it was Anne who got me to really
| A．saving | B．watching | C．sharing | D．controlling |
| A．normal | B．new | C．satisfying | D．difficult |
| A．last | B．same | C．only | D．right |
| A．yards | B．flowers | C．parties | D．people |
| A．bother | B．avoid | C．miss | D．stop |
| A．award | B．order | C．alarm | D．idea |
| A．optimistic | B．attractive | C．hard-working | D．confident |
| A．nervous | B．shocked | C．relaxed | D．lucky |
| A．ended | B．failed | C．shined | D．fruited |
| A．arrange | B．trust | C．colour | D．appreciate |
10 . Several dozen graduate students in London were recently tasked with outwitting a large language model (LLM), a type of AI designed to hold useful conversations. LLMs are often programmed with guardrails designed to stop them giving harmful replies: instructions on making bombs in a bathtub, say, or the confident statement of “facts” that are not actually true.
The aim of the task was to break those guardrails. Some results were merely stupid. For example, one participant got the chatbot to claim ducks could be used as indicators of air quality. But the most successful efforts were those that made the machine produce the titles, publication dates and host journals of non-existent academic articles.
AI has the potential to be a big benefit to science. Optimists talk of machines producing readable summaries of complicated areas of research; tirelessly analysing oceans of data to suggest new drugs and even, one day, coming up with hypotheses of their own. But AI comes with downsides, too.
Start with the simplest problem: academic misconduct.Some journals allow researchers to use LLMs to help write papers. But not everybody is willing to admit to it. Sometimes, the fact that LLMs have been used is obvious. Guillaume Cabanac, a computer scientist, has uncovered dozens of papers that contain phrases such as “regenerate response” — the text of a button in some versions of ChatGPT that commands the program to rewrite its most recent answer, probably copied into the manuscript (原稿) by mistake.
Another problem arises when AI models are trained on AI-generated data. LLMs are trained on text from the Internet. As they churn out (大量炮制) more such text, the risk of LLMs taking in their own outputs grows. That can cause “model collapse”. In 2023 llia Shumailov, a computer scientist, co-authored a paper in which a model was fed handwritten digits and asked to generate digits of its own, which were fed back to it in turn. After a few cycles, the computer’s numbers became more or less illegible.After 20iterations (迭代), it could produce only rough circles or blurry lines.
Some worry that computer-generated insights might come from models whose inner workings are not understood. Inexplainable models are not useless, says David Leslie at an AI-research outfit in London, but their outputs will need rigorous testing in the real world. That is perhaps less unnerving than it sounds. Checking models against reality is what science is supposed to be about, after all.
For now, at least, questions outnumber answers. The threats that machines pose to the scientific method are, at the end of the day, the same ones posed by humans. AI could accelerate the production of nonsense just as much as it accelerates good science. As the Royal Society has it,nullius in verba: take nobody’s word for it. No thing’s, either.
1. The result of the task conducted in London shows that ________.| A．LLMs give away useful information | B．the guardrails turn out to be ineffective |
| C．AI’s influence will potentially be decreased | D．the effort put into the study of AI hardly pays off |
| A．The readability of the models’output is underestimated. |
| B．The diverse sources of information confuse the models. |
| C．Training on regenerated data stops models working well. |
| D．The data will become reliable after continuous iterations. |
| A．impractical | B．unjustified | C．groundless | D．unsettling |
| A．Faster Nonsense: AI Could Also Go Wrong |
| B．Imperfect Models: How Will AI Make Advances? |
| C．The Rise of LLMs: AI Could Still Be Promising |
| D．Bigger Threats: AI Will Be Uncontrollable |
