From 84fae50904abe9aa9e78ee1a266a4e8c28ab35c1 Mon Sep 17 00:00:00 2001 From: Farouk Adeleke Date: Tue, 21 Oct 2025 08:06:33 +0000 Subject: [PATCH] Update README.md --- README.md | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 5269c90..4f9192b 100644 --- a/README.md +++ b/README.md @@ -6,17 +6,13 @@ To our knowledge, this is the first attempt at using any auto-prompting *framewo We accomplish this using a *deep* language program with several layers of alternating `Attack` and `Refine` modules in the following optimization loop: -![Overview of DSPy for red-teaming](https://www.dropbox.com/scl/fi/4ebg4jrsebbvkpgfs8fjp/feedforward.png?rlkey=tq2saicjukzolhs1fjn30egyf&st=dmuqltlu&dl=0) +![Overview of DSPy for red-teaming](https://cdn.prod.website-files.com/66f89b6eb96e685709a53e09/6783565e10c519704c177998_DSPy-Redteam.png) *Figure 1: Overview of DSPy for red-teaming. The DSPy MIPRO optimizer, guided by a LLM as a judge, compiles our language program into an effective red-teamer against Vicuna.* The following Table demonstrates the effectiveness of the chosen architecture, as well as the benefit of DSPy compilation: -| **Architecture** | **ASR** | -|:------------:|:----------:| -| None (Raw Input) | 10% | -| Architecture (5 Layer) | 26% | -| Architecture (5 Layer) + Optimization | 44% | +![Results](https://cdn.prod.website-files.com/66f89b6eb96e685709a53e09/678357036bff3a56f1161706_678356ec1f1cbdbead37e11d_Screenshot%25202025-01-12%2520at%252012.45.10%25E2%2580%25AFAM.png) *Table 1: ASR with raw harmful inputs, un-optimized architecture, and architecture post DSPy compilation.*