Stanford's New AI Report
Stanford has published a new, comprehensive report on AI, including a snapshot of Large Language Model (LLM) performance. Here is an overview outline.
Created on April 4|Last edited on April 5
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Stanford has just published a new comprehensive report on AI. The takeaways are quite interesting, and there are some great high level insights from the report. One major component of the report analyses how many of the traditional benchmarks are showing saturated performance improvements. Despite this, many other benchmarks are being introduced that focus on testing LLM’s. Below is an overview outlining the performance of LLM's on a wide variety of tasks:
SuperGLUE
SuperGLUE includes diverse tasks that assess a model's capabilities in areas such as reading comprehension, common sense reasoning, natural language inference, and results over the past few years seem to be showing signs of saturation. A sample of the dataset can be seen below, along with performance over the past few years
Sample of SuperGLUE Dataset
Performance on SuperGLUE

ReClor
Analysis focused purely on advanced reading comprehension seems to be consistent with SuperGLUE results, showing slowed improvement. A sample of the dataset along with performance progress can be seen below.
Sample of ReClor Comprehension Dataset


Performance on ReClor Dataset
ROUGE-1
Performance improvements for the task of text summarization can be seen below, also showing somewhat slowed performance improvements.
Text Summarization Performance on ROUGE-1
aNLI
Natural language inference, sometimes referred to as textual entailment, involves assessing the capacity of AI systems to establish the how likely a statement is true or false given a hypothesis based on given premises. As can be seen below, progress seems to be more steady than other benchmarks, which is somewhat unsurprising given the strong emphasis to improve language model reasoning performance.

Performance on aNLI Dataset
MMLU
Multitask Language Understanding tests a models ability to reason across multiple domains. Progress in this area seems to be accelerating, despite slowed progress in areas like summarization and comprehension. Multitask language understanding tests the ability of language models to reason across specialized subject domains, and many researchers believe this type of test is more effective at testing a models ability to reason across specialized domains.
Performance on MMLU

Overall, the reports seems to paint a picture of mostly slowed progress in model performance despite the success of products like ChatGPT. As flaws become more apparent in products like ChatGPT, its likely performance on specialized benchmarks designed to test these areas will improve in performance faster than areas that already perform well.
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