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I am trying to find support for your last argument. https://paperswithcode.com/sota/common-sense-reasoning-on-arc-challenge https://paperswithcode.com/sota/com
by benob 3y ago
I am trying to find support for your last argument.
https://paperswithcode.com/sota/common-sense-reasoning-on-arc-challenge https://paperswithcode.com/sota/common-sense-reasoning-on-ar...
GPT-3 53.2
GPT-3.5 85.2
LLaMa-65B 56.0
Any idea of the performance of an instruction-fine-tuned version of LLaMa models? I can't seem to find non-aggregated performance figures on ARC.
- behnamoh 3y agoWow, the top 14 are all proprietary models (except 2 models which are not commonly used in OS community anyway). And surprisingly, Llama 33B performs _better_ than Llama 65B!
- NhanH 3y agoThat table is comparing few shot prompting GPT 4 to zero shot llama.
- BoorishBears 3y agoI'm not sure if these ARC scores here are fully comparable to those in scaling, but if they are: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderb... TigerResearch/tigerbot-70b-chat: ARC (76.79) Still below 3.5 and like most top entries no clear training objectives, which usually means they were fine-tuned on the benchmark itself. - ARC is easily the most important benchmark right now for widespread adoption of LLMs by laypeople: it's literal grade school multiple choice, created to the bar of an 8th grader. When you sit people down in front of an LLM and have them interact with it: ARC has by far the closest correlation of how "generally smart" the model will feel.