@fabianstelzer will be demo’ing HOLOSHEET and a GPT3 powered Google Sheet for Deforum prompts. Check it out on Fabian’s Twitter:
PROMPT ENGINEERING
-
Demo Day: GPT-3 Powered Tools and Innovations
By
–
Spend Friday at Demo Day with us, @fabianstelzer
, and Diwank Singh. We’ll be demo’ing GPT3 powered prompt generators on Google Sheets, satirical pitch decks, and more. Join our Discord to be a part of it: -
Prompt Engineering for AI Agents: Step-by-Step Operation Sequencing
By
–
The game is “least to most” — have it write out the most mechanical, reliably performed operations first. Then gradually move up to more interpretive and analytical steps, so it’s never asked to do anything multi-step in its head. The prompt should feel like an income tax form.
-
GPT-3 Tokenization Challenges and Model Limitations
By
–
+1 for this approach. GPT-3’s tokenization is a big gotcha. Model doesn’t see text as sequences of characters unless you add spacing/hyphens. It can’t even reliably tell you the last letter of a word without first rewriting it that way. Similar for counting length:
-
K-Shot Learning for Non-RLHF Tuned LLMs
By
–
I’ve never seen it work on anything other than RLHF-tuned GPT-3. I’ve wondered if you could k-shot a non-RLHF model with meta-examples of instruction templates followed by input/output pairs for each, but haven’t tried it.
-
Few-shot Prompt Setup Notes for Scaling Law Experiments
By
–
Some setup notes (cc @EthanJPerez
) – We used the exact 2-shot prompt for Quote Repetition, which is already U-shaped for Gopher/Chinchilla – We used fewer shots for Hindsight – We did few-shot instead of 0-shot for Negation QA – We also showed inverse scaling up to PaLM 62B -
U-shaped Scaling and the Limitations of Inverse Scaling Tasks
By
–
Implications: 1. U-shaped scaling means that inverse scaling may not hold when extrapolated to larger models. 2. The term “inverse scaling task” is underspecified. A task can be inverse scaling for one type of prompting and positive or U-shaped for another type of prompting.
-
CoT Prompting Defends Against Inverse Scaling in Math Tasks
By
–
Second, we show that CoT prompting can defend against inverse scaling. For instance, CoT prompting achieves 100% on 7 out of 8 subtasks for Redefine Math.
-
Exploring Instruction Induction for Inferring AI Instruction Templates
By
–
Wonder how well it fares for longer instructions — I played around a while ago with using instruction induction to infer “instruction templates” from examples and it seemed to work: