Not All LLMs Are Created Equal – A Strange Encounter with DeepSeek

As a software engineer, I often rely on a variety of tools to streamline my work and help me out when I hit roadblocks. One of the tools I try out is DeepSeek-r1. I use it to get help, bounce ideas around, and work through tricky problems. When I’m writing code, I turn to DeepSeek-coder-v2 for a quick burst of inspiration or to sort through a tough bug. Both I run locally.

But today, something a little unexpected happened. And it made me stop and think: not all LLMs (large language models) are equal.

Here’s what happened:

I decided to paste a translation file to DeepSeek-r1. I figured it would be a good way to check for any issues, typos, or inconsistencies across a large set of translations. Simple task, right? Well, not exactly.

Instead of getting the usual responses in English (which is what I asked for), I was met with an answer in Chinese. Out of the blue. I was left scratching my head, wondering how that happened. Maybe I accidentally triggered a setting, or perhaps the tool misinterpreted my input. But after trying again, I got the same result: Chinese.

I couldn’t help but laugh, but I was also genuinely curious. It wasn’t the result I expected, and it definitely wasn’t something I’d come across before. After all, I’m used to asking questions in English and getting responses in English. But Chinese? That was a new one.

I figured this was just a glitch, maybe a weird bug or an unintended setting. So I tried once more, but the response stayed the same: Chinese.

And here’s the thing: DeepSeek is a powerful tool. It’s trained on vast amounts of data, and it’s been incredibly useful for generating ideas, checking my code, and offering suggestions. But this incident reminded me that not all language models behave in the same way. Even though they’re built on similar technologies, they’re all unique in how they interpret input and generate output.

In this case, it seems that DeepSeek-r1 had a bit of a mix-up, and it decided that Chinese was the way to go. While it was an amusing experience, it got me thinking about the differences between language models, their training data, and how they respond to requests.

So, I’ve got a question for anyone else using DeepSeek: Have you ever had a similar experience? Did you ask a question in one language only to receive an answer in a completely different one? I’m curious to know if this is something that’s happened to others or if I’m just particularly lucky (or unlucky) today.

In the end, this little mishap reminded me that, despite all the advancements in AI and LLMs, they still have their quirks. They’re not perfect, and they don’t always work the way we expect them to. But that’s what makes them interesting - there’s always something new to learn about how these systems operate.

For now, I’ll chalk it up to an amusing glitch, and maybe I’ll spend a little more time brushing up on my Mandarin just in case it happens again!