Bilingual Story is a fictionalized account. 雙語故事部分內容純屬虛構。
Emma had reviewed 41 resumes that morning. While the ATS screened out 288 unqualified, she screened for AI slop. She could spot it a mile away. She muttered AI buzzwords like curses under her breath. “Team player.” “Results-driven.” “Stakeholder alignment.” “Leveraging core competencies.” Each resume reeked of AI modeling: a cemetery of cliches, tombstones of personality. AI wasn’t just changing hiring. It was draining the humanity from it.
Then she found it: a plain PDF cover letter. No template. No design flourishes. The first line read: “I once tried to automate my dog-walking business. It failed spectacularly when my algorithm couldn’t distinguish between ‘urgent bathroom needs’ and ‘squirrel sightings.’ I learned more about managing expectations — especially my dog’s — than any MBA program could teach.”
Photo: AI I 照片：AI
Emma couldn’t help but smile. Her first in a long time. The resume was refreshing. Showing, not telling. Measuring, not embellishing. Detailing, not generalizing. It was quirky and uniquely human. Perfectly imperfect. She immediately shortlisted the applicant, Andrew Ipsum, and sent the interview invite herself.
The next day
The candidate joined the video call exactly on time. Camera on.Emma smiled. The young man on screen smiled back — friendly, composed, just the right amount of nervous. “Hi, Andrew. Just wanted to say, your resume really stood out.”
“Thank you. I’m glad you liked it. I put a lot of thought into it,” he said.
She continued, “The dog-walking story was oddly specific. It felt more real and. . . human, compared to the other AI crap I read from other candidates.”
He chuckled. “That was the goal. Many people just use mindless AI to write mindless resumes.”
“Exactly,” Emma said. “What gets me is how AI tools just recycle the same phrases. They’re trained on the same data sets, so everything starts sounding like a cover letter written by a hive mind.”Andrew nodded thoughtfully. “Yes. It’s an averaging of language. Every phrase statistically optimized. . . and emotionally hollow.”
“Exactly,” she said. “You can feel the sameness. The lack of voice. Like no one’s actually lived the things they claim.”
Andrew tilted his head. “That’s why I ran 37 drafts.”
Emma blinked. “Wait. . . what? 37?”
“To eliminate every cliche. To strike the right balance of vulnerability, specificity, and tone. I needed it to sound. . . perfectly imperfect.”
艾瑪那天早上已經審閱了41份履歷。當ATS（應徵者追蹤系統）篩掉288份不合格的履歷時，她則專注於過濾「AI罐頭文」。這些套路她一眼就能識破。她嘴裡像在咒罵般地低聲念著那些AI生成的流行常用字：「有團隊精神的人」、「結果導向」、「利害關係人協調」、「發揮核心優勢」。每份履歷都充滿了AI模組的味道，如同一座陳腔濫調的墳場，埋葬著每個人原有的個性。 AI不只改變了錄用人的方式，更讓整個過程失去了人情味。
就在此時，她發現了它：一份樸素的PDF求職信。沒有使用模板、沒有花俏的設計。開頭第一句寫道：「我曾試著把我的遛狗生意自動化。結果慘不忍睹，因為我的演算法分不清狗狗「如廁警報」和「松鼠雷達」的差別。這次經驗讓我學到更多有關期望值管理的道理—尤其在面對我家狗狗時—這是在任何MBA 課堂都無法領悟的。
艾瑪忍不住笑了。這是很久以來第一次發自內心的微笑。這份履歷令人耳目一新。它展現事實，而非空泛敘述；衡量成就，而非誇大其詞；詳述細節，而非籠統概括。它古怪而獨特，充滿人性。恰恰好的不完美。 她立刻將這位名為安德魯伊普森的應徵者列入了面試的候選名單，並親自發送了面試邀請。
隔日
應徵者準時加入了視訊通話，鏡頭已開啟。艾瑪笑了，螢幕上的年輕人也回以微笑—友善、沉著，帶著恰到好處的緊張。 「嗨，安德魯。首先想跟你說，你的履歷真的很與眾不同。」
「謝謝，我很高興您喜歡，那花了我不少心思。」他答道。
她接著說：「那個遛狗的故事異常具體。與我讀到的其他應徵者那些AI生成的廢話相比，它感覺更真實，更……有『人』味。」
他輕笑：「這正是我的目的，現在太多人用無腦AI寫出一堆無腦履歷。」
「沒有錯！」艾瑪點頭：「讓我最無法接受的，」她說，「就是 AI 工具不斷重複相同的詞句。它們的訓練數據都來自同樣的資料集，結果每一封求職信讀起來都像是同個思維下的產物—毫無個人特色。」
安德魯陷入思考後並點點頭：「是的。這是一種語言的平均化。每一個詞語都經過統計上的最佳化……卻情感空洞。」
「一點也沒錯，」她說。「你能感受到那種雷同感，缺乏個人特色，好像沒有人真正經歷過他們所聲稱的事情。」
安德魯偏了偏頭：「這就是為什麼我改了37次稿。」
艾瑪瞪大眼睛：「等等……什麼？37次？」
「為了去掉每一個陳詞濫調，找到脆弱感、具體細節和語氣的平衡。我需要它感覺起來……恰恰好的不完美。」
Epilogue 故事討論
The alert reader will recognize the title of this story refers to “The Boy Who Cried Wolf,” the classic fable where repeated false alarms dull the ability to recognize a real threat. In today’s job market, recruiters face a similar dilemma: they’re flooded with resumes that all sound the same, especially if AI is used to create them. But AI has transformed both sides of hiring. Recruiters rely on “Applicant Tracking Systems” (ATS) to scan and rank resumes using keyword-matching algorithms that eliminate up to 75 percent of applicants before a human ever sees them. Some systems even use AI to score resumes for tone, formatting, and “fit.” And on the applicant side, 46 percent of job seekers now use tools like ChatGPT to write resumes and cover letters.
But that doesn’t mean those applications were good. The problem? AI-generated content tends to use the most common phrases in its training data, like “team player,” “dynamic professional,” “thrives in fast-paced environments.” These cliches are examples of AI slop — while technically appropriate, they are hollow by themselves. That’s why personalization and details matter more than ever. A good resume doesn’t just list nouns and adjectives; it must tell a story with details, numbers, and specifics. It should say, I lived this. AI can imitate language — but it does not know your lived experience. Unless you tell it what to say.
And that brings us back to Andrew. Was he human? Or an AI trained so well it knew how to charm a recruiter with just the right flaws? Hard to say. Either way, he understood the trick: not to sound perfect in an empty sense, but to sound perfectly imperfect with specifics.
敏銳的讀者會發現，本故事的標題呼應了《狼來了》（又稱《放羊的孩子》），這是一個反覆虛報危機的故事，最終人們無法識別真正的威脅。在當今的求職市場中，人資也面臨類似的困境：他們被大量看起來一模一樣的履歷淹沒，尤其是當這些履歷都是由AI生成的。然而，AI已經改變了招聘的雙方生態。人資依賴應徵者追蹤系統（ATS）來掃描和排名履歷，使用關鍵字匹配的演算法，在人類審核前就淘汰了最多75%的求職者。有些系統甚至會使用AI來評分履歷的語氣、格式和「契合度」。而在求職者端，46%的求職者現在會使用像ChatGPT這樣的工具來撰寫履歷和求職信。
但這並不代表那些履歷申請就一定很優秀。問題出在哪裡？AI生成的內容往往會使用訓練數據中最常見的詞組，如「有團隊精神的人」、「充滿活力的專業人士」、「能在快節奏的環境中茁壯成長」。這些陳腔濫調就是所謂的「AI廢文」—雖然從技術上講是適當的，但它們本身卻是空洞的。這就是為什麼個人化和細節比以往任何時候都更重要。一份好的履歷不僅僅是列出名詞和形容詞；它必須用細節、數字和具體事例來講述一個故事。它應該說：「我經歷過這些。」人工智慧可以模仿語言—但它不知道你的生活經歷。除非你告訴它該說什麼。
這又讓我們回到了安德魯。他是人類嗎？還是一個訓練有素的人工智慧，知道如何用恰到好處的缺陷來打動招聘人員？這很難說。但無論如何，他掌握了一個訣竅：與其追求空洞的完美，不如用具體的細節展現恰恰好的不完美。
Vocabulary 單字片語
1. Applicant Tracking Systems (ATS) 應徵者追蹤系統
2. resume 履歷、摘要、概要
3. cover letter 求職信、附加信、封面信
4. applicant 求職者、應徵者、申請人
5. interview 面試、面談、訪問
6. hire 錄用
7. qualified/unqualified 合格/不合格
8. cliche 陳詞濫調
9. buzzword 流行常用字
10. detail 細節
11. training data 訓練數據
Idioms 相關成語
1. The Boy Who Cried Wolf 狼來了、放羊的孩子
2. Spot it a mile away 一眼就能識破
= See through it at a glance
3. Curses under one’s breath 低聲咒罵
= Muttered curses
Nigel P. Daly is a writer with a language learning newsletter called Chin-glish bilingual lab (https://ndaly.substack.com).
Cynthia Chen is a translator and writer specializing in blogs, scripts and articles.
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