Using LLMs doesn’t always help readability.

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As an experienced reviewer of recommender systems articles, I have had the privilege of evaluating submissions for numerous large conferences, primarily on the industry track but also on the research track.

A rarely discussed barrier to getting your article accepted at one of these conferences is its readability.

While numerous tools are available to assist writers, it’s important to exercise caution. Before the introduction of LLMs, one of the biggest offenders was Google Translate, which has “helped” non-English speakers translate text. Unfortunately, many of these translations don’t actually mean the same thing. With the introduction of LLMs, many English-speaking authors also hurt readability using tools.

An LLM is a great tool for making your language sound richer and more colorful, which is great if you are writing a novel or other creative piece of content. However, in a scientific article, the best approach is to simplify as much as possible. To convey your research, please make it easier for the reader.

If you do use an LLM or any other tool to help you write it, please do the reviewers and future readers a favor and ensure you and others understand what is written first before submitting.

LLMs are great but are not making Recommender systems obsolete (yet)

LLMs are great and can do mind-boggling things with their language comprehension capabilities. They have generative abilities that make them seem like oracles, but please caution yourself because they are not.

Stuffing an LLM into a recommender system does not solve all problems. In fact, they might create quite a few more than they will solve at this point.

That’s not to say that they don’t have a place in the world of RecSys, but it is another component rather than a replacement altogether. The idea that it will make behavioral data obsolete seems a bit naive to me. Language Models can enhance recommender systems. LLMs significantly enhance recommender systems by leveraging their advanced language comprehension capabilities to generate personalized recommendations. However, it’s essential to recognize their limitations. While they excel in understanding language, they may not adequately address all complexities of user behavior and context, potentially creating more issues than they solve.