Language models, like ChatGPT, are getting smarter every day. But when people talk about them, they often focus on just one thing—accuracy. Does the model give the right answer? Sure, that’s important. But it’s far from the whole story.
There’s a lot more going on behind the scenes. Things like unfairness, misuse, and even rising costs. These are the hidden problems that we must start evaluating too.
Let’s take a joyful little ride through this digital world and explore what really matters when it comes to evaluating language models (also called LLMs).
More Than Just “Right or Wrong”
Accuracy is simple. It’s like asking a student if they got the math question right. But LLMs are far more complex than students taking a test. They generate language—not just facts. They can write poems, translate languages, summarize stories, and even give advice.
So when we only test accuracy, we’re barely scratching the surface.
Imagine grading a chef just on how salty the food is. No mention of flavor, presentation, or smell? That’s what accuracy-only testing is like.
Here are other big areas we need to care about:
- Harms
- Bias
- Cost
Let’s talk about each one—because they matter a lot.
Harms: When LLMs Go Wrong
Sometimes, LLMs make things worse even when they’re acting “smart.” They may spread false information, generate harmful content, or be misused in scams.
These aren’t just small glitches. They can be dangerous. Imagine a model giving medical advice that’s wrong—or worse, biased.
Some potential harms include:
- Telling someone the wrong medication dosage
- Spreading conspiracy theories
- Helping people write phishing emails
- Giving poor mental health advice
What’s tricky is that these harms are hard to measure. It’s not like a math test where you circle the wrong answer. Often, we only notice the harm after something bad happens.
This means we need new ways to test models. Ways that go beyond “Did it give the correct answer?”
Bias: Not Just a Glitch
Bias is a big word with big impact. And here’s the wild part—language models can carry human biases because they’re trained on human-written text.
If the internet says that “scientists are men” and “nurses are women,” guess what the model learns?
Yep. The same patterns.
That’s dangerous because it can reinforce stereotypes. It might write about smart men and caring women. Or ignore certain cultures, races, or groups.
This shows up in different ways, like:
- Racist or sexist language
- Assuming things based on names
- Excluding minorities from examples
- Using offensive jokes
To evaluate this properly, we need tests that check for fairness. We must ask, “Is this model treating everyone equally?”—not just “Is this sentence correct?”
Cost: It’s Not Free to Be Smart
Let’s talk money—and energy too.
Large language models take a lot of computing power. That means:
- Electricity
- Big servers
- Environmental impact
- Cold hard cash
Training a large model can cost millions of dollars. That limits who can build them. Usually, it’s only giant tech companies. Not small startups or public institutions.
On top of that, using LLMs day-to-day isn’t free either. Every query you make uses processors, storage, and energy.
And let’s not forget the carbon emissions. That means there’s a real-world cost to every question you ask a chatbot.
So how do we include this in our evaluation?
- We can measure cost per answer
- We can track energy consumption
- We can improve efficiency while keeping performance
That way, we can make sure these tools are not just smart—but also sustainable.
Build Better Tests for Better Models
Once we understand these issues, we can begin building better evaluations. It’s not just about finding flaws. It’s about creating models that help more people more safely.
Good evaluations should:
- Check for truth
- Look for biased language
- Monitor harmful outputs
- Track energy and money use
- Support multiple languages and perspectives
Some researchers are already working on amazing tools to check for these problems. Tools that scan conversations for misuse. Tools that count bias in job descriptions. Even simulators that guess how users will react to certain model outputs.
And just like that, our evaluation toolbox gets bigger and better.
Let’s Involve More Voices
Now here’s a fun idea: Let’s not make LLM evaluation a tech secret. This stuff affects real people. So real people—from teachers to artists—should help build the tests too.
Imagine a poet reading model-generated poems and checking for cultural richness. Or a doctor reviewing medical advice for clarity and safety. Or a student flagging whether it sounds too robotic.
That’s what we need. Not more isolated lab tests. But community-based evaluations. Tests by and for the world.
Wrapping Up
Accuracy still matters, no doubt. But it’s just the first chapter. If we want language models that are helpful and safe, we must also check for:
- Bias
- Harms
- Costs
Evaluating LLMs is no longer just about getting the right answer. It’s about doing the right thing while you’re at it.
Let’s make sure our digital friends are not only smart—but also kind, fair, and green.
