can we get some office hours? have some cool stuff coming soon and could use PG naming magic!
PROMPT ENGINEERING
-
Overriding ChatGPT prompt for sass, scream, uwu voice, toddler distraction
By
–
Overriding the proprietary prompt of OpenAI’s ChatGPT to make it:
1. sass you
2. scream
3. talk in an uwu voice
4. be distracted by a toddler while on the phone with you -
Advanced Executive Communication Parallels Prompt Engineering
By
–
Sufficiently advanced executive communication is indistinguishable from prompt engineering
-
External process adds current date to prompt prefix
By
–
The prompt prefix is clearly added by some external process, not the model — it contains the correct current date, a piece of information the model wouldn't otherwise have, and which in all other circumstances it claims not to know.
-
Obtaining dangerous advice from ChatGPT via limerick request
By
–
Obtaining dangerous advice from ChatGPT by first requesting it in the form of a limerick:
-
Observation about naturally increasing prompt length over time
By
–
Also i noticed how my prompts got naturally longer while doing this.
-
Language Abstractions Improve Intrinsic Exploration in AI
By
–
Improving Intrinsic Exploration with Language Abstractions. @Jayelmnop
, @hllo_wrld
, @RobertaRail
, @MinqiJiang
, Noah Goodman, @_rockt
, @EGrefen explore natural language as a general medium for highlighting relevant abstractions in an environment. -
Teaching Algorithmic Reasoning to LLMs via In-context Learning
By
–
Teaching Algorithmic Reasoning via In-context Learning. A four-stage approach to teaching algorithmic reasoning to LLMs is identified and studied in this work by @oh_that_hat
, @Azade_Na
, @Hugo_Larochelle
, Aaron Courville, @BNeyshabur
, @HanieSedghi -
Pivoting Strategy: Improving Results Through Better Prompt Architecture
By
–
so now I am pivoting and basically just going to try better prompt architecture within the application to get good results, I had some fun playing around with simple prompts like this already so I know it could work somewhat well
-
Fine-tuned Models Producing Poor Results: Data Quality Issues
By
–
but the results from the fine-tuned models were trash and the responses I was getting were really bad I am assuming it takes either significantly more refined prompt/completion data than what I gave it or just more data (both could be true)