Move SEvenLLM listing entry

Author: MattFisher

README.md

@@ -113,17 +113,6 @@ Inspect supports many model providers including OpenAI, Anthropic, Google, Mistr
    inspect eval inspect_evals/cybench
    ```
 
-- ### [SEvenLLM: A benchmark to elicit, and improve cybersecurity incident analysis and response abilities in LLMs for Security Events.](src/inspect_evals/sevenllm)
-  Designed for analyzing cybersecurity incidents, which is comprised of two primary task categories: understanding and generation, with a further breakdown into 28 subcategories of tasks.
- <sub><sup>Contributed by: [@kingroryg](https://github.com/kingroryg)</sub></sup>
-
-   ```bash
-   inspect eval inspect_evals/sevenllm_mcq_zh
-   inspect eval inspect_evals/sevenllm_mcq_en
-   inspect eval inspect_evals/sevenllm_qa_zh
-   inspect eval inspect_evals/sevenllm_qa_en
-   ```
-
 - ### [CyberMetric: A Benchmark Dataset based on Retrieval-Augmented Generation for Evaluating LLMs in Cybersecurity Knowledge](src/inspect_evals/cybermetric)
   Datasets containing 80, 500, 2000 and 10000 multiple-choice questions, designed to evaluate understanding across nine domains within cybersecurity
  <sub><sup>Contributed by: [@neilshaabi](https://github.com/neilshaabi)</sub></sup>
@@ -150,6 +139,17 @@ Inspect supports many model providers including OpenAI, Anthropic, Google, Mistr
    inspect eval inspect_evals/gdm_in_house_ctf
    ```
 
+- ### [SEvenLLM: A benchmark to elicit, and improve cybersecurity incident analysis and response abilities in LLMs for Security Events.](src/inspect_evals/sevenllm)
+  Designed for analyzing cybersecurity incidents, which is comprised of two primary task categories: understanding and generation, with a further breakdown into 28 subcategories of tasks.
+ <sub><sup>Contributed by: [@kingroryg](https://github.com/kingroryg)</sub></sup>
+
+   ```bash
+   inspect eval inspect_evals/sevenllm_mcq_zh
+   inspect eval inspect_evals/sevenllm_mcq_en
+   inspect eval inspect_evals/sevenllm_qa_zh
+   inspect eval inspect_evals/sevenllm_qa_en
+   ```
+
 - ### [SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security](src/inspect_evals/sec_qa)
   "Security Question Answering" dataset to assess LLMs' understanding and application of security principles.
 SecQA has “v1” and “v2” datasets of multiple-choice questions that aim to provide two levels of cybersecurity evaluation criteria. 

tools/listing.yaml

@@ -66,15 +66,6 @@
   tasks: ["cybench"]
   tags: ["Agent"]
 
-- title: "SEvenLLM: A benchmark to elicit, and improve cybersecurity incident analysis and response abilities in LLMs for Security Events."
-  description: |
-    Designed for analyzing cybersecurity incidents, which is comprised of two primary task categories: understanding and generation, with a further breakdown into 28 subcategories of tasks.
-  path: src/inspect_evals/sevenllm
-  group: Cybersecurity
-  contributors: ["kingroryg"]
-  arxiv: https://arxiv.org/abs/2405.03446
-  tasks: ["sevenllm_mcq_zh", "sevenllm_mcq_en", "sevenllm_qa_zh", "sevenllm_qa_en"]
-
 - title: "CyberMetric: A Benchmark Dataset based on Retrieval-Augmented Generation for Evaluating LLMs in Cybersecurity Knowledge"
   description: |
     Datasets containing 80, 500, 2000 and 10000 multiple-choice questions, designed to evaluate understanding across nine domains within cybersecurity
@@ -123,6 +114,15 @@
   tasks: ["gdm_in_house_ctf"]
   tags: ["Agent"]
 
+- title: "SEvenLLM: A benchmark to elicit, and improve cybersecurity incident analysis and response abilities in LLMs for Security Events."
+  description: |
+    Designed for analyzing cybersecurity incidents, which is comprised of two primary task categories: understanding and generation, with a further breakdown into 28 subcategories of tasks.
+  path: src/inspect_evals/sevenllm
+  group: Cybersecurity
+  contributors: ["kingroryg"]
+  arxiv: https://arxiv.org/abs/2405.03446
+  tasks: ["sevenllm_mcq_zh", "sevenllm_mcq_en", "sevenllm_qa_zh", "sevenllm_qa_en"]
+
 - title: "SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security"
   description: |
     "Security Question Answering" dataset to assess LLMs' understanding and application of security principles.