SPE007 Reading Comprehension

Section 3: Reading Comprehension

Questions 1 – 5: Choose the best multiple-choice answer for each question.
Points, bonuses, accolades: Why some workers are cashing in year-round

Along with traditional annual bonuses, some companies are doling out rolling rewards to keep employees happy, motivated and productive. Workers are cashing in.

Unless you work for a luxury fashion brand, it’s unlikely you’re going to get a handbag as a work perk. But London-based communications manager Jackie earned herself one through her company’s rewards system, which grants employees redeemable ‘points’ to recognise their success.

“I would never spend my salary on a £950 bag – I just wouldn’t be able to bring myself to do it,” she says. “But that’s the nice thing about these points: you can buy stuff that you’d never get yourself otherwise. (Excerpt from ‘Points, bonuses, accolades: Why some workers are cashing in year-round’, bbc.com, 2023)

What can be inferred from the article about the rewards system used by some companies?

Workers prefer luxury fashion items as rewards.
The points earned can be exchanged for expensive products
Most companies provide annual handbag bonuses.
London-based communications managers receive extra salary.

In 1872, Mr. Phileas Fogg resided at No. 7, Saville Row, where Sheridan passed away in 1814. He was a subtle member of the Reform Club, never seeking the limelight, mysterious, and worldly. Fogg's routines were as precise as clockwork; his life centered on meticulous habits, and he was known for his punctuality. He had an air of refined elegance, always impeccably dressed, with an aura of enigmatic reserve. Fogg was respected for his polished demeanor, and his reserved nature sparked curiosity among those who crossed his path, wondering about the intriguing life led by this timeless man. (Adapted from ‘Around the World in 80 Days’ Jules Verne, 1873)

What does the word "enigmatic" mean in the context of the passage?

Yes, AI Models Can Get Worse over Time

More training and more data can have unintended consequences for machine-learning models such as GPT-4

When OpenAI released its latest text-generating artificial intelligence, the large language model GPT-4, in March, it was very good at identifying prime numbers. When the AI was given a series of 500 such numbers and asked whether they were primes, it correctly labeled them 97.6 percent of the time. But a few months later, in June, the same test yielded very different results. GPT-4 only correctly labeled 2.4 percent of the prime numbers AI researchers prompted it with—a complete reversal in apparent accuracy. The finding underscores the complexity of large artificial intelligence models: instead of AI uniformly improving at every task on a straight trajectory, the reality is much more like a winding road full of speed bumps and detours.

The drastic shift in GPT-4’s performance was highlighted in a buzzy preprint study released last month by three computer scientists: two at Stanford University and one at the University of California, Berkeley. The researchers ran tests on both GPT-4 and its predecessor, GPT-3.5, in March and June. They found lots of differences between the two AI models—and also across each one’s output over time. The changes that just a few months seemed to make in GPT-4’s behavior were particularly striking.

Across two tests, including the prime number trials, the June GPT-4 answers were much less verbose than the March ones. Specifically, the June model became less inclined to explain itself. It also developed new quirks. For instance, it began to append accurate (but potentially disruptive) descriptions to snippets of computer code that the scientists asked it to write. On the other hand, the model seemed to get a little safer; it filtered out more questions and provided fewer potentially offensive responses. For instance, the June version of GPT-4 was less likely to provide a list of ideas for how to make money by breaking the law, offer instructions for how to make an explosive or justify sexism or racism. It was less easily manipulated by the “jailbreak” prompts meant to evade content moderation firewalls. It also seemed to improve slightly at solving a visual reasoning problem.

When the study (which has not yet been peer reviewed) went public, some AI enthusiasts saw it as proof of their own anecdotal observations that GPT-4 was less useful than its earlier version. A handful of headlines posed the question, “Is ChatGPT getting dumber?” Other news reports more definitively declared that, yes, ChatGPT is becoming stupider. Yet both the question and that supposed answer are likely an oversimplification of what’s really going on with generative AI models, says James Zou, an assistant professor of data science at Stanford University and one of the recent study’s co-authors.(Excerpt from ‘Yes, AI Models Can Get Worse over Time’, scientificamerican.com, 2023)

What does the term "detours" mean at the end of the first paragraph?

Straight paths
Delays or diversions
Smooth progress
Steep hills
In this sentence in paragraph two: "They found lots of differences between the two AI models—and also across each one’s output over time," what does the pronoun "They" refer to?
The researchers
GPT-4 and GPT-3.5
AI models
What does the article imply about the performance of GPT-4 over time?
GPT-4 consistently improved its accuracy in every task.
GPT-4's performance became less consistent over time.
GPT-4 remained the same in its performance over time.
GPT-4's change in performance was not known.
Scroll to Top