ExplainableML/in-context-impersonation
[NeurIPS 2023 Spotlight] In-Context Impersonation Reveals Large Language Models' Strengths and Biases
This project helps researchers and developers understand how Large Language Models (LLMs) behave when they are asked to act like different people or experts. By providing a persona as part of the input, you can see how the LLM's text output changes and if it performs better or worse on tasks. This is useful for anyone evaluating LLM performance, potential biases, or exploring their nuanced capabilities in areas like reasoning or description.
No commits in the last 6 months.
Use this if you need to systematically test how different 'personas' (like a child, a domain expert, or a specific demographic) influence an LLM's output and task performance.
Not ideal if you are looking for a tool to train new LLM models or to integrate LLM capabilities into a production application directly.
Stars
22
Forks
1
Language
Python
License
MIT
Category
Last pushed
Nov 30, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/ExplainableML/in-context-impersonation"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
PaddlePaddle/PaddleNLP
Easy-to-use and powerful LLM and SLM library with awesome model zoo.
meta-llama/llama-cookbook
Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started...
arcee-ai/mergekit
Tools for merging pretrained large language models.
changyeyu/LLM-RL-Visualized
๐100+ ๅๅ LLM / RL ๅ็ๅพ๐๏ผใๅคงๆจกๅ็ฎๆณใไฝ่ ๅทจ็ฎ๏ผ๐ฅ๏ผ100+ LLM/RL Algorithm Maps ๏ผ
mindspore-lab/step_into_llm
MindSpore online courses: Step into LLM