"AI Impact on Market Research: LLM Feedback Request"

🩺 The Urgent Need for Plain Language in PROs Over half of PRO instruments fail basic Plain Language standards, yet we expect patients to report outcomes accurately. Let’s talk about bridging the gap between accessibility, compliance, and patient trust. Plain Language is not a “nice to have” in PROs. It is an ethical and regulatory imperative for equitable, patient-centred research. While digital innovation has improved access to publications, complex and overly technical language continues to exclude the very people research aims to serve: patients and their caregivers. 💡 Why Plain Language Drives Engagement and Equity We ask patients to engage actively in their own care, yet the language of research often builds barriers instead of bridges. 🔎 Low readability: The average Flesch–Kincaid grade for so-called “Plain Language Summaries” in PRO research is 17.8, far above the recommended eighth-grade level. 🔎 Systematic exclusion: Complex wording disproportionately affects older adults and those with limited health literacy. More than 50% of cancer PROMs fail to meet plain-language criteria. 🔎 Building trust: Translating numerical PRO scores into accessible narratives strengthens comprehension and the perceived value of these measures for patients. Plain Language does not simplify science, but it humanises it. ✅ Let’s Talk Are your current PROMs truly accessible to all patient populations, or are technical barriers compromising your data quality and equity goals? Connect with me today to discuss how we can implement world-class Plain Language strategies, optimize your eCOA migration processes, and ensure your clinical trial communication is both accurate and inclusive. #PlainLanguage #PROM #PRO #eCOA #COA #ClinicalTrials #PatientEngagement #HealthLiteracy #Localization #RegulatoryCompliance #LifeSciencesTranslation #Accessibility #ClinicalResearch #HumanInTheLoop #PlainLanguageMatters

28% of your workday is spent on emails. That's 2.6 hours. Every. Single. Day. Yet most non-native English speakers tell me they: 🔹 Spent 20+ minutes on simple emails 🔹 Re-read messages 5 times before sending 🔹 Second-guess their tone constantly 🔹 Copy from ChatGPT without understanding the phrases 📗 Here's what I learned after 25 years of teaching business English: You don't need AI. You need a system. Once you know the right phrases and when to use them, email writing becomes automatic. 5 minutes, not 20. I've created a free guide with 50 essential phrases for every business situation. Comment 'PHRASES' below and I'll send it to you. 👇 #ProductivityTips #BusinessEnglish #EmailWriting

🧐 Is the source always right? Not really. And it happens more often than you’d think. Sometimes the source text isn’t even the true original but already a translation itself, and the meaning gets blurrier with every step of the process. Sometimes there are simple human or MT errors, typos, or odd phrasing that slipped through. And sometimes, technical issues like segmentation errors or problems that arise when files are imported into tools such as XTM or Smartling break the flow and make the text harder to understand. Last but not least, there are idioms, expressions, or cultural references that simply don’t work in Italian. You can’t just carry them over, or you’ll end up with something that sounds… odd. Take “I’m feeling blue.” In English, it means “I’m sad.” In Italian, if you translate it literally as “Mi sento blu,” it doesn’t just lose its meaning, it loses all logic. That’s why I always tell my clients when something feels wrong or can be improved. Because localization is not about obeying the source at all costs, it’s about making sure the final, and sometimes even the source, message truly serves its purpose. And honestly, it feels great when a client notices. When they appreciate that extra step, that little “extra mile,” to make their content stronger and more authentic. It turns a regular day into one of those I-really-love-my-job days. ____________________________________ 🧠 𝐀𝐈 𝐦𝐚𝐤𝐞𝐬 𝐢𝐭 𝐟𝐚𝐬𝐭. 𝐇𝐮𝐦𝐚𝐧𝐬 𝐦𝐚𝐤𝐞 𝐢𝐭 𝐟𝐢𝐭. AI and MT are tremendous tools. Useful, powerful, fast. Use them if they help, they give you a nice fabric. But if you want something that truly fits your story, your audience, your brand, and brings real results, you need a tailor. That is when you call me. 📩 𝐃𝐌 𝐦𝐞 𝐚𝐧𝐝 𝐥𝐞𝐭’𝐬 𝐬𝐡𝐚𝐩𝐞 𝐲𝐨𝐮𝐫 𝐯𝐨𝐢𝐜𝐞 𝐢𝐧 𝐈𝐭𝐚𝐥𝐢𝐚𝐧. 🌐 Want to learn more? Check out www.serraluisa.com

IF English is your second language, this prompt can help you LEARN how to improve your English. "I am a native ______ speaker, can you help me improve this email?" When you tell it what you speak natively, it can compare your expectations of sentence structure with English and teach you the principles of how the two languages are different. Don't just have AI correct your emails. Have AI teach you.

When clarity becomes an act of responsibility: from public institutions to corporate reporting, plain language is redefining how trust is built. This week, Brussels hosts the Brussels Plain Language Experience 2025, a three-day gathering (Nov 5–7) organized by Plain Language Association International (PLAIN), Clarity, the European Parliament, and the European Commission. It’s a global check-in on a simple idea with serious impact: write so people can use what they read. La Nación brought that message into sharp focus in Argentina with “Lenguaje claro, nueva gramática del poder público”. The article shows that clarity is no longer about style but about accountability, and measurable results. Studies from the Plain Language Center at the University of Sydney reveal that when administrative texts are easy to understand, public trust in institutions rises by 10 to 20 percent, while misinterpretation errors drop by 40 percent. Sweden’s decades-long experience confirms the same: every form rewritten in plain language cut follow-up inquiries by an average of 27 percent. As the article concludes, clarity is efficiency. Joanna Richardson, president of the International Plain Language Federation, adds another layer: the goal isn’t to write less, but to write better: “to provide precision and avoid the ambiguity that creates legal uncertainty.” That principle doesn’t stop at the doors of government. Corporate and sustainability reports face the same challenge: making complex information understandable, credible, and genuinely engaging. If readers, especially non-specialists or non-native speakers, can’t follow the story, the data loses meaning. Plain language bridges that gap by sharpening the message, speeding up understanding, and reducing rework. - What to leave behind: 40-word sentences, unexplained acronyms, and corporate jargon. - What to keep: structure, rhythm, purpose, through clear signposting, verbs that show action, and visuals that carry real weight. The gain isn’t cosmetic. Clarity cuts errors, builds trust, and saves time, just as La Nación notes in public communication, and as the Brussels conference is championing across sectors this week. As an Editorial Coordinator, that’s the craft I apply: shaping complex inputs into a coherent narrative that remains elegant, accurate, and accessible to all readers, including those for whom English is not a first language. It’s about finding precision without pretension, tone without excess, and language that serves meaning before form.  Check comments for links to the La Nación article and the Brussels PLAIN conference.

One of the things I love about English? The job titles.😂😂 I find they are one of the most difficult things to translate...Sometimes impossible … and now and again they’re just hilarious. For example: Data Scientist 👉🏻 sounds like a wizard of spreadsheets 🧙♂️ Tree Surgeon 👉🏻 you mean a gardener? 🌳💉 Innovation Sherpa 👉🏻 you carry innovation up a mountain? ⛰️ I’ve noticed English has a knack for creating job titles that are quirky, unexpected, and impossible to translate yet 𝑠𝑜𝑚𝑒ℎ𝑜𝑤 make nearly perfect sense. 💬 What’s the funniest, most confusing or most untranslatable English job title you’ve seen? Drop it in the comments! ______________________________ ⚠️ In finance, medicine, or technical fields, words carry risk, regulation, and reputation. 🚫 Generic won’t cut it 🚫 AI can’t read the room Finding real technical translators you can trust? That’s the hard part. 👉🏻 𝐓𝐡𝐚𝐭’𝐬 𝐰𝐡𝐞𝐫𝐞 𝐰𝐞 𝐜𝐨𝐦𝐞 𝐢𝐧. ✅ High-precision translation by subject-matter experts who have done it IRL. 🔗 JYC Translations - Your Technical Translation Team 🌍 3w jyctranslations com #TechnicalTranslations #FinancialTranslation  #LanguageLovers #TranslationFun #EnglishQuirks #JobTitles #Localization

Translation is trust and trust breaks when meaning gets lost. Saturday, we organized a masterclass on grant writing , but there was one tiny problem. The PowerPoint was in English. The session? Entirely in French. 😅 And guess who had to translate the whole thing last minute? Yep ! me. At first, I relied only on my skills, but it quickly became time-consuming (and mentally exhausting). Some sentences just refused to sound right in French. That’s when I decided to bring in the tools DeepL and yes, even ChatGPT and they were game changers. Still, I had to tweak a few sentences here and there to keep the meaning and tone intact. But here’s the thing , in professional writing, that one bad translation can cost you credibility. In NGO reports, it can twist your mission. In academic work, it can distort your data. And in business, it can make your brand sound like it doesn’t understand its own audience. People don’t buy words they buy meaning. If your translation doesn’t capture tone, intention, and culture, it’s not communication , it’s copy-paste in another language. That’s why technical, academic, or professional translation isn’t just about knowing two languages. It’s about knowing how your reader thinks, feels, and decides. This post is getting quite long , so check the comments for two practical tips and two translation tools that make all the difference. 👇🏽

Deducing Meaning: Strategies for Understanding Unfamiliar English Words How can we guess or deduce the meaning and concept of an English word or phrase—or at least gain the necessary understanding—without resorting to a dictionary or a language instructor, and ultimately, how can we perform more successfully in multiple-choice tests? We can refer to several powerful strategies, for example: • Redundancy • Negative Prefixes • Common Meaning vs. Figurative Meaning • Prepositions • Phrasal Verbs • Semantic Corpus • Grammar • Positive Sentence Context • Negative Sentence Context • Word Function in the Text and General Knowledge • Surrounding Words • Sentence Meaning Based on Related Words • Direct Contrast • Two-Part Verbs • Three-Part Verbs • Synonyms • Antonyms • Option Elimination Focus Strategy: Direct Contrast I will elaborate on one of the above items, while the remaining methods are respectfully offered to esteemed colleagues and professors. Direct Contrast In this technique, we become familiar with words that introduce opposing or contradictory meanings within a sentence. Consider this simple sentence: Alan is tall, but... The word 'but' signals a contrast between two characteristics that stand in opposition. If one side is 'tall,' the other side must logically be 'short.' Thus, the second part would be: Mike is short. Now, pay attention to this expression: It is believed that Jammy is a naive boy, but I personally think that he is clever, sharp, and brainy. Even if the pronunciation of the word 'naive' is not perfectly known, we can guess its meaning after the word 'but.' 'Naive' must be the opposite of the list that follows. The list describes someone who is intelligent (clever, sharp, brainy). Therefore, 'naive' must mean something like 'simple,' 'gullible,' or 'not very smart.' A crucial point is that the words following the comma (clever, sharp, and brainy) are clustered within the same semantic concept. Most learners are familiar with 'clever' and 'sharp,' and seeing the comma, they can infer that 'brainy' will also share a similar meaning to 'clever.' Through this approach, one can largely deduce the meaning of hundreds of unfamiliar words, provided one is familiar with the words that signal direct contrast (e.g., but, however, although, yet, while, in contrast). To be continued

🛠️Spotted by my colleague this week. DeepL 𝐓𝐫𝐚𝐧𝐬𝐥𝐚𝐭𝐨𝐫 (one of my favorite translation tools) just made a leap to 100+ languages! 💡We'll be comparing accuracy with other platforms for our i-intelligence online research courses (Russian, Chinese and Arabic-based script courses). 💡One of the techniques I like to use, before comparing, is stacking everything up using Notepad in order to visually detect differences in languages and scripts (Cantonese, Mandarin simplified and Mandarin Traditional in the video). 💡Character choice will affect your results when searching. 🔗Deepl Translate website: https://lnkd.in/ev6R8sy9 Have a nice weekend everyone 😉 #osint #tools #translation #langtwt #languages

Quick RAG question: If you have a multilingual dataset in OpenSearch (English as the base articles + translations in other languages), and all articles are semantically chunked + embedded, what’s the best retrieval strategy for multilingual semantic search given that users query can be in any language and the translated articles in the open search chunks are limited except from the root articles “English”? Option A: • Always search only the English embeddings (base articles). • Even if the user queries in German/Spanish, run semantic search against English chunks. • Then map results back to the user’s language via metadata (root_article_id, translation fields). Option B: • Search all embeddings across all languages directly. • Let the multilingual model handle cross-language similarity. • Then deduplicate using the root_article_id to avoid returning duplicate translations. Ssssooooo my question.. stay with me here: Which do you think ranks better for relevance and language preference and why? Curious how others are handling this in real systems 👀… Especially with models like Snowflake Arctic / multilingual-MiniLM / LaBSE