Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/shreyashankar/spade-experiments
Experiments to assess SPADE on different LLM pipelines.
https://github.com/shreyashankar/spade-experiments
Last synced: 7 days ago
JSON representation
Experiments to assess SPADE on different LLM pipelines.
- Host: GitHub
- URL: https://github.com/shreyashankar/spade-experiments
- Owner: shreyashankar
- Created: 2023-12-29T21:45:47.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-04-07T03:24:50.000Z (7 months ago)
- Last Synced: 2024-08-02T15:30:59.921Z (3 months ago)
- Language: Python
- Size: 4.41 MB
- Stars: 16
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# SPADE (System for Prompt Analysis and Delta-Based Evaluation)
This is the code for the paper SPADE: Auto-Generated Assertions for Large Language Model
Pipelines.The SPADE pipeline has the following steps:
1. Decomposing a list of prompts and an example into a set of prompt deltas, assertion concepts, and candidate assertions. This is in `candidate_gen.py`.
2. Generating a set of candidate assertions for each prompt delta. This is also in `candidate_gen.py` (uses a helper function in `assertion_gen.py`).
3. Generates synthetic data and evaluates the assertions (`execute_assertions.py`). The user should label the synthetic data responses as correct or incorrect.
4. Evaluates subsumption of the assertions (`check_subsumes.py`).
5. Runs the assertion selection algorithm (`optimizer.py``).
All the experiments live in paper_experiments/ and have a notebook for each experiment. To run any of the experiments (leveraging cached data so you don't need to run any LLMs), run the `pipeline.ipynb` notebook in the root directory, using the correct experiment name.