LinkedIn Collaborative Articles: kryptonite, marmite or plain embarrassing?

in September 2023, LinkedIn alerted me to the existence of its collaborative articles, written using AI. I took a look at them and contributed to some to understand the outcomes of doing so. Rapidly, I have noticed a growing number of dissenting voices about them.

“Contributing” has evolved in a commentary about how LinkedIn uses AI to tackle a question. Each question/article is split into around five to seven main points, with the possibility to add to each section. I have generally posted in the areas of translation, technical translation and linguistics (in relation to the discipline of translation). My “interventions” of typically between 250-750 characters garner between five and thirty reactions on average.

As the quantity of reactions per intervention increases, the number of followers and contact requests has also increased. And with it, LinkedIn recognised me as a “Top Voice” in certain areas. This status is no more than a small graphical badge on my profile. Aside from an initial burst of attention, the badge does not really made any big difference. If you have badges in multiple categories, as I quickly managed to do, you choose which one you want to display on your profile.

However, the badge had a very negative side-effect too. “Compliance” by contributing to Collaborative Articles is poorly received by the kind of followers I want and actively engage with. We talk about subjects like Trados shortcuts or the state of the profession vs the industry. Their contributions are thought-provoking and enhance my knowledge. While my follower has increased, the ones from Collaborative Articles are “fickle followers”. There is far less traction and less genuine engagement. In contrast, those contacts I nurture from events, networks, and groups also interact and produce stimulating content.

Are these the kind of followers I want?

A steady flow of 20 to 30 reactions sufficed for me to attract a large number of followers, albeit maybe not necessarily those followers that I in turn wish to follow back. It has triggered an inundation of contact requests and followers. Of these requests, many have nothing in common other than being a translator – and no common specialisms or language pairs! Frequently there is no real explanation of why they wish to connect.

In one case, one follower called my office and was put through to me and gave me a sales pitch. And then made a request to connect, and followed up by DMs. His polished sales patter did not interest me in the slightest, and I have no intention of connecting. In this regard I am comparatively lucky: my profile picture shows that I am clearly a middle-aged male, so I dodge the unsolicited personal mails that others have to contend with.

In a world of fresh and authentic content…

Authenticity and freshness of content are a LinkedIn mantra. So naturally when Collaborative Articles fail to deliver either, it becomes clear that there is little genuine intention for their content to enrich. Even their titles are prosaic, clumsy and repetitive. After reading only a few articles it became clear how they were generic prompt-based sludge. The prompts invariably spat out similar responses to a vast number of questions, particular questions/titles that only differed by 1-2 words. (It made me wonder if they use the RANDBETWEEN function in Excel to spit out new titles).

Some collaborative articles are clearly untouched by post-editing, and I suspect are clicked through by disinterested gophers on work experience, who genuinely have no idea about the subject matter or field. And many sections seem to have a stance of mantra-like repetition of some false universal truth. The aspect of “universal truth” is something that also prevails in industry-side conferences, where some speakers project the industry view onto professionals in a way that it is the only way to survive. #2023TEF seemed to go down this path to a certain extent, although it was good to see some professionals pushing back against the industry’s universal truth.

Know your field

As an in-house translator, I hold certain clear views on the use of GenerativeAI in translation, particularly the advantages and disadvantages that it poses for the industry and the profession. There are many divergent stances, depending on the area of translation you work in. I am still firmly in  “Camp Profession”, and my stance is in line with professionals who are predominantly self-employed.

Collaborative articles sit firmly in “Camp Industry”. The first wave of LinkedIn collaborative articles on translation seemed to read like “MT is gospel and the only way to work.” This is certainly not the case in the profession. Initially, the near standard use of CAT tools in both profession and industry scarcely got a look in. A few months in and there has clearly been a retraining based on received contributions. “Translation Memory” and “CAT tools” do figure more strongly. The AI still trips up in terms of far too many sections of the collaborative articles that deal with what a CAT tool is. While this suggests an overcoming of the initial bias in the training of the model, it confirms that those submitted contributions are duly being used to train LinkedIn’s AI.

Sometimes collaborative article titles nevertheless remain downright incongruous. They choose to address subjects like “bilingual communication in global enterprises.” This must be due to the AI understanding translation as an exercise of a single source language to a single target language. But “global enterprises” communicate in many languages, although possibly only bilingually in an individual target market. Mere bilingual communication throughout a multinational cooperation is as feasible as a multinational corporation with one desktop PC and a filing cabinet. (Note: this was a humorous dig at the Robinson Corporation from Neighbours).

LinkedIn still appears not to know my field

LinkedIn still spews out subjects I ought to comment on – many of which are very wide of my expertise. I reject any notification to contribute to a subject outside my field, probably about as far “off remit” that I’ll go is to contribute about content strategies, due to having half a clue about them.

To understand the worthlessness of “Top Voices” I acquired one on a subject I was not qualified to talk about. That achieved, I stopped commenting on that subject area. After several months they did eventually remove my “Top Voice”. This demonstrates that uptake is poor among professionals while the decay period for losing this status is a long one.

Contributing to Collaborative Articles: kryptonite?

By contributing you provide human generated text data to pass into the LLMA large language model (LLM) is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate and predict new content. More, to train the AI that LinkedIn uses. By contributing, you are siding with industry to the detriment of professionals. If you are a language professional, you may be contributing to drowning out your own voice on the platform.

Some professionals have therefore actively chosen to not participate – a very respectable decision. Others have submitted content that either itself is AI-generated, to therefore try to hasten the “rot” of the model. Putting it another way – they try to feed the snake its own tail. My reservation to this approach is that LinkedIn is a platform to promote skills, so such efforts might be futile. No anonymity in posting of contributions might feed negatively into the algorithm and come back to haunt you.

Collaborative Articles: Marmite?

Another comparison would be to compare them to Marmite. But what is to love and hate about them? What I have actually loved is that their pure mediocrity has also helped me identify potential areas to blog about. However, LinkedIn might scrape my blogposts if I share them on LinkedIn to feed its AI and in turn its collaborative articles. I need a better understanding about how LinkedIn obeys a customised robots.txt file or scrapes my website. The “disruptor” in me loves the creative ways other contributors have to shovel in nonsensical AI-generated content into the Collaborative Articles. Hopefully with an eventual effect of “breaking” it.

I rapidly have come to hate the mantra-like repetition of the questions. This has led to my answers becoming a repetitive stream of consciousness. I do not wish to invest my time in writing “snippets” of 250-750 characters as their have a limited impact. One contributor I seem to spot regularly uses it to promote their CAT-related solution. Sadly, the system is also too dense to pick up the fact that it is thinly veiled advertising, and it is not really possible to report such contributions as self-advertising. Then again, why users also have to police the site’s output when it makes money from publishing such dross.

Collaborative Articles: plain embarrassing

Below, I have cited a couple of typical examples of frankly embarrassing statements I have encountered. There are countless others.

“Human translation (HT) is the use of professional or native speakers to translate text or speech from one language to another.”

Collaborative Article on: “You need to translate a document. How do you know which service to choose?“

I understand that fast-moving technical advances blur boundaries. But I despair that LinkedIn fails to grasp the difference between translation and interpreting. Sadly, some non-lay human audiences also seem to struggle in this regard.

Or then there are questions that show that professionals should be in thrall to the industry, a practice I call out wherever possible.

“How can you use translation memoryA database of translation units (TUs) in a computer-assisted translation (CAT) tool. More to optimize pricing for your clients?” This title advocates that translators should punish themselves for the effective use of assistive technology. CAT tools and discounts for fuzzy matches are already being used to exploit translators and drive costs down, effectively penalising their investment in CAT tools. This is part of the industry vs profession schism that agencies exploit. I’d urge translators to work directly with customers, and to forge close working relationships, but to take a balance approached in terms of offering discounts – give them something, but do not reduce yourself to slavery rates.

My response to the Collaborative Article on “How can you use translation memory to optimize pricing for your clients?“

Another recent case referred to CAT environments allowing translators to translate more in more languages.

The article states: “Lastly, scalability is increased by allowing translators to handle larger volumes of content and more languages.” This is a clumsily worded statement. Using a translation memoryA database of translation units (TUs) in a computer-assisted translation (CAT) tool. More in its own right will not allow translators to handle more languages. It is no substitute for their mastery of a language. And nor does its use unlock new languages. It would be far more accurate to say that TMsA database of translation units (TUs) in a computer-assisted translation (CAT) tool. More can allow LSPs to centrally store language data in a large number of languages and under certain circumstances to leverage this language data to assist translations in new source/target language combinations.

My response to the Collaborative Article on “How can you choose the most effective translation memory tools and technologies for your project team?“

Recently, I started calling out Collaborative Articles that fall into the plain embarrassing category. However, I have tried to take an approach that does not give any information away other than what is wrong. Correcting it could lead to my input might being used to retrain the LLMA large language model (LLM) is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate and predict new content. More used. I rate a lot of Collaborative Articles as being poor. Sadly, the feedback categories offered don’t fit the issue to address: AI output is published with minimal human intervention.

Will nobody rid me of this troublesome grift?

Many users on the platform despair at LinkedIn collaborative articles and requests to contribute filling their feeds. It is possible to stop notifications from LinkedIn Collaborative Articles from appearing among your notifications. However, it is not possible to choose to actively have them removed from your feed of other people’s contributions to them. There is a way to block all content from a certain person, but not a certain type of their content by someone.

In this regard, there are other possibilities that I would like to see implemented on LinkedIn. I would also like to block carousels over a dozen slides – it’s another case in hand of “death by PowerPoint” – with inflated decks of slides.

I would also like to choose what kind of posts I see from companies I follow. For example, I follow a lot of internationally active banks, and follow them to see what their news is. A lot of 3rd+ degree connections post about their new job at an institution on another side of the planet. And then, because they tag their new employer, I also see their new employer’s “Welcome on board!” comment to them, which LinkedIn assumes might interest me.