In this case they really made the numbers smaller than they should be, so it's hard to see that the scale is on the order of single digits. It looks like this is about a 3-4% improvement over GPT-4o-mini and Gemini Pro 1.5.
The bigger problem here is not the axis baseline, but the fact that I have no idea (as a non-AI-researcher) what benchmark this is, or if 0 is even the natural minimum. The caption should at least mention what the natural range of the x-axis is.
The figure labels it as as "average score [on] 6 multimodal reasoning benchmarks", and the caption notes that the full results are in table 7 - which lists those benchmarks: MMStar-R, MMBench-R, MMVet-R, MathVista, AI2D, Hallusion
I think it's mostly fine as a lead diagram giving an overview before going into detail.
Always a pass from me, gets things off on the wrong foot right away.
Further this is a paper on arXiv, so the idea by some that it's meant to deceive -- as if the target audience isn't going to immediately look at the axis labels, and for more dig into what the benchmarks even were -- is not convincing.
I'd hold more criticism for the fact that their lead graphic specifically excludes options which beat it (e.g. GPT-4o, Sonnet), though these details can be found in the chart below.
Still interesting. And this "structuring AI" approach is how the next evolution in AI is happening.
Unfortunately the practice of showing the latter slice runs along that of showing the whole bars, so a better convention to distinguish the two would be beneficial.
For example, "breaking" the bars (on the left side), similarly to when some bars run too far on the right side. I.e.:
| ==//====|
| ==//========|
| ==//===|
+----------------
Consider their Proposed Method:
"Each stage is initiated at the model’s discretion, without external prompt engineering frameworks or additional prompting. Specifically, we provide the model with four pairs of special tags: <SUMMARY></SUMMARY>, <CAPTION></CAPTION>, <REASONING></REASONING>, and <CONCLUSION></CONCLUSION>.
These tags correspond to summarizing the response approach, describing relevant image content, conducting reasoning, and preparing a final answer, respectively. Upon training, the model autonomously selects these tags as needed, activating each stage based on its own judgment.
As with OpenAI o1 [63], all stages are completed by the model in a single inference pass."
Quote from this paper: “ Moreover, they (VLM) frequently deviate from a logical reasoning toward conclusions, instead of presenting a conclusion prematurely and subsequently attempting to justify it. Given that language models generate responses token-by-token, once an erroneous conclusion is introduced, the model typically continues along a flawed reasoning path.”
I care about whether these VLMs can accurately _see_ and _describe_ things in a picture. Meanwhile the vision part of these benchmarks are a lot of extremely basic OCR that any VLMs of the past year can do. The gains in score come from the LM improving logic skills not from the actual vision ability improving.
For instance, if I have a CAD model of a screw fastened to a wall, can I teach it that its a screw fastened to a wall?
I have years worth of potential training data.
Consider this a multi-million dollar problem.
LLAMA can be trusted to summarize and format information, and some of the other models can be OK coding assistances, but when I was showing Ollama off to a friend I struggled to think of anything useful other than a party trick of "yup that's what is in the picture".
Obviously it would be useful to blind people, but the hard part is using it for something where the person could just look at the picture. Possibly could be used on a security camera and combined with a basic keyword alert, but I imagine there's a lot of false positives and false negatives.