> measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer.
This is so useful because it makes you actually trust a models output. A high score on benchmarks is not as useful because a model overtrained to always answer will give confidently wrong responses. But this index measures how often it is correct while penalizing wrong responses so that a high score means you can trust this model more and when it doesn't know it is more likely to tell you that it really doesn't know rather than making shit up.
Fable also performs a lot better than opus 5 here which correlates very strongly with perceived strength despite the models performing similarly on e.g. DeepSWE
Astra is a big jump from sol and performs the same or slightly better than fable here.
Hallucinations are very damaging to a model’s utility. But doesn’t the Omniscience Index focus on knowledge-based queries? To me, using LLMs for their memorized knowledge is very 2023 and suboptimal.
IMO, what really makes a model useful is its ability to process information within its context reliably and faithfully. I don’t care if it hallucinates George Washington’s favorite color, but I do care about it hallucinating the results of tool calls.
Fair but I think those two behaviours are strongly correlated at least the index does represent my personal experience very well where fable is way better than opus opus is better than sol. And I haven't tried Astra yet but it having 44/43 is very interesting at least.
I don't agree. The main issue with their scoring/methodology is that the numbers make it seem like 5-6 models have little to no difference when in fact there is a significant difference between fable and opus and sol and astra for example. They are popular mainstream but most of their benchmarks are either not a representation of model strengths enough or they are not doing a good job of showcasing it properly. The fact that muse and 3.8 were high a day back shows they are just the modern version of lmareana for the mass audience and PR stunts.
The Epoch Capabilities Index uses an Elo-based aggregation method that dynamically adjusts for benchmark difficulty and they put error bars on their scores, both of which put them miles ahead of Artificial Analysis: https://epoch.ai/eci?view=graph&tab=leaderboard
> A high score on benchmarks is not as useful because a model overtrained to always answer will give confidently wrong responses.
It's not useful because the benchmarks often measure the wrong thing. They're here yapping about AGI and yet the benchmarks treat it like a trained dog. Fetch this. 100 points.
Each "problem" in these benchmarks likely has more than 1 solution that can be considered correct and even should be graded in many ways. Yet we see in many benchmarks higher effort (or thinking levels) don't help because the benchmark penalizes for doing "more" than what the answers asks for. So what did you ask for?
In human school you often get marks on the process and not just the end result. Thinking tokens have been cut. All we group on is things like cost, turns and time but not the what else.
This matches what I've seen building anything that uses an LLM for narrow structured output rather than open ended chat, things like classifying a diff into a fixed set of categories or summarizing a change. The aggregate benchmark score barely predicts how it behaves in production. What actually breaks a pipeline is a confident wrong answer on the small slice of inputs that don't fit the pattern it saw during training, not a lack of raw capability. A model that says it isn't sure on the edge cases is far more useful to me than one that scores higher on average but never admits uncertainty, because the wrong but confident output is the one that slips through review unnoticed.
https://artificialanalysis.ai/evaluations/omniscience
> measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer.
This is so useful because it makes you actually trust a models output. A high score on benchmarks is not as useful because a model overtrained to always answer will give confidently wrong responses. But this index measures how often it is correct while penalizing wrong responses so that a high score means you can trust this model more and when it doesn't know it is more likely to tell you that it really doesn't know rather than making shit up.
Fable also performs a lot better than opus 5 here which correlates very strongly with perceived strength despite the models performing similarly on e.g. DeepSWE
Astra is a big jump from sol and performs the same or slightly better than fable here.