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Probabilistic Consensus through Ensemble Validation: A Framework for LLM Reliability
arxiv.orgLarge Language Models (LLMs) have shown significant advances in text generation but often lack the reliability needed for autonomous deployment in high-stakes domains like healthcare, law, and finance. Existing approaches rely on external knowledge or human oversight, limiting scalability. We introduce a novel framework that repurposes ensemble methods for content validation through model consensus. In tests across 78 complex cases requiring factual accuracy and causal consistency, our framework improved precision from 73.1% to 93.9% with two models (95% CI: 83.5%-97.9%) and to 95.6% with three models (95% CI: 85.2%-98.8%). Statistical analysis indicates strong inter-model agreement ($κ$ > 0.76) while preserving sufficient independence to catch errors through disagreement. We outline a clear pathway to further enhance precision with additional validators and refinements. Although the current approach is constrained by multiple-choice format requirements and processing latency, it offers immediate value for enabling reliable autonomous AI systems in critical applications.
Great, so it’s still wrong 1 out of 20 times, and just got even more energy intensive to run.
Genuine question: how energy intensive is it to run a model compared to training it? I always thought once a model is trained it’s (comparatively) trivial to query?
Source: https://www.washingtonpost.com/technology/2024/09/18/energy-ai-use-electricity-water-data-centers/
How much energy does it take for the PC to be on and the user to type out that email manually?
I assume we will get to a point where energy required starts to reduce as the computing power increases with moores law. However, it’s awful for the environment in the mean time.
I don’t doub that rather than reducing energy, instead they will use more complex models requiring more power for these tasks for the foreseeable future. However eventually it will be diminishing returns on power and efficiency will be more profitable.
For the small ones, with GPUs a couple hundred watts when generating. For the large ones, somewhere between 10 to 100 times that.
With specialty hardware maybe 10x less.
A lot of the smaller LLMs don’t require GPU at all - they run just fine on a normal consumer CPU.
yeah but 10x slower, at speeds that just don’t work for many use cases. When you compare energy consumption per token, there isn’t much difference.
Wouldn’t running on a CPU (while possible) make it less energy efficient, though?
It depends. A lot of LLMs are memory-constrained. If you’re constantly thrashing the GPU memory it can be both slower and less efficient.
Good god. Thanks for the info.
Still requires thirsty datacenters that use megawatts of power to keep them online and fast for thousands of concurrent users
I wonder how that compares to the average human?
I would not accept a calculator being wrong even 1% of the time.
AI should be held to a higher standard than “it’s on average correct more often than a human”.
Not a very good, or easy comparison to make. Against the average, sure, the AI is above the average. But a domain expert like a doctor or an accountant is way much more accurate than that. In the 99+% range. Sure, everyone makes mistakes. But when we are good at something, we are really good.
Anyways this is just a ridiculous amount of effort and energy wasted just to reduce hallucinations to 4.4%.
Actually, not so.
If the AI is trained on narrow data sets, then it beats humans. There’s quite a few examples of this recently with different types of medical expertise.
Cool, where are the papers?
“We just need to drain a couple of lakes more and I promise bro you’ll see the papers.”
I work in the field and I’ve seen tons of programs dedicated to use AI on healthcare and except for data analytics (data science) or computer image, everything ends in a nothing-burger with cheese that someone can put on their website and call the press.
LLMs are not good for decision making (and unless there is a real paradigm shift) they won’t ever be due to their statistical nature.
The biggest pitfall we have right now is that LLMs are super expensive to train and maintain as a service and companies are pushing them hard promising future features that, by most of the research community they won’t ever reach (as they have plateaued): Will we run out of data? Limits of LLM scaling based on human-generated data Large Language Models: a Survey (2024) No “Zero-Shot” Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance
And for those that don’t want to read papers on a weekend, there was a nice episode of computerphile 'ere: https://youtu.be/dDUC-LqVrPU
</end of rant>
Large language models surpass human experts in predicting neuroscience results
A small study found ChatGPT outdid human physicians when assessing medical case histories, even when those doctors were using a chatbot.
Are you kidding me? How did NYT reach those conclusions when the chair flipping conclusions of said study quite clearly states that [sic]“The use of an LLM did not significantly enhance diagnostic reasoning performance compared with the availability of only conventional resources.”
https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2825395
I mean, c’mon!
On the Nature one:
“we constructed a new forward-looking (Fig. 2) benchmark, BrainBench.”
and
“Instead, our analyses suggested that LLMs discovered the fundamental patterns that underlie neuroscience studies, which enabled LLMs to predict the outcomes of studies that were novel to them.”
and
“We found that LLMs outperform human experts on BrainBench”
Is in reality saying: we made this benchmark that LLMs know how to cheat around our benchmark better than experts do, nothing more, nothing else.
Specialized ML models yes, not LLMs to my knowledge, but happy to be proved wrong.