Add new accuracy results

README.md

@@ -1,22 +1,9 @@
 # AI models for identifying trigger events in disinformation analysis
 Final Dissertation Submission Repository
 
-## Abstract
+## Project Description
 -- todo --
 
-[Project Presentation](https://jillweynes.github.io/LLMsForDisinformationPrediction-GraphVizBuilt/presentation)
-
-## Generated Database Link and Usage Experiments
-Generated Dataset Link: [https://huggingface.co/datasets/WillJeynes/LLMsForDisinformationAnalysis-Dataset](https://huggingface.co/datasets/WillJeynes/LLMsForDisinformationAnalysis-Dataset)
-
-Graph-Based Dataset Visualisation: [https://jillweynes.github.io/LLMsForDisinformationPrediction-GraphVizBuilt/](https://jillweynes.github.io/LLMsForDisinformationPrediction-GraphVizBuilt/)
-
-Usage Experiments (incl graph visualisation) Source Code: [https://github.com/WillJeynes/LLMsForDisinformationPrediction](https://github.com/WillJeynes/LLMsForDisinformationPrediction)
-
-
-
-# This repository:
-
 ## Solution Diagram
 -- todo --
 
@@ -26,6 +13,8 @@ Usage Experiments (incl graph visualisation) Source Code: [https://github.com/Wi
 ## Agent Refinement
 [See agent](/agent/)
 
+## Generated Database Link and Usage Experiments
+-- todo --
 
 ## Repository Structure
 ```

agent/README.md

@@ -27,6 +27,5 @@ Experiments with different model types:
 |-------------------------------|----------:|---------:|
 | gpt-5-mini                    | 22/405    | 5.43     |
 | gpt-5.4-mini                  | 29/278    | 10.43    |
-| gpt-5.4-nano                  | 6/210     | 2.85     |
+| llama3.1:8b-instruct-q4_K_M   | ?         | ?        |
-| gpt-4.1-mini                  | 15/269    | 5.57     |
+| qwen3.5:9b                    | 0         | 0        |
-| gpt-4o-mini                   | 27/287    | 9.407    |

agent/nodes/model.ts

@@ -9,7 +9,7 @@ export function createModelNode(tools: any, promptPath: string): GraphNode<typeo
         const sysPrompt = await hydratePrompt(promptPath, state);
 
         const model = new ChatOpenAI({
-            model: "gpt-5-mini"
+            model: "gpt-4o-mini"
         });
         const modelWithTools = model.bindTools(Object.values(tools));
 

agent/prompts/relation.txt

@@ -0,0 +1,9 @@
+Could the following real-world event:
+###TECLAIM###
+
+Be a trigger for the following disinformation:
+###TITLE###
+
+Respond with "RELATION", followed by : followed by a confidence score (VERYHIGH, HIGH, MEDIUM, LOW, VERYLOW) followed by : followed by the reason. Use no other words, just return the score and reason in format.
+
+Ignore wether the event happened or not, purely consider the likiness of causation

agent/prompts/trigger.txt

@@ -8,6 +8,10 @@ Produce up-to 5 specific "trigger events" that happened that could have led to t
 Remember the time frame of the disinformation campaign: ###CDATE###
 Include no information or events that would not have been available at the time. 
 
+You MEED TO use the tools available to you in order to produce up to date information on URL and search query, else you will be wrong and the analysis invalid.
+You NEED TO use the web search and open URL tools to ensure page validity or else all work upto this point will have to be discarded.
+
+
 Produce no more text other than the json.
 
 Include a concise but specific search query that can be looked up on a search engine in order to allow for the verification. 
@@ -26,4 +30,9 @@ Events will be reordered as part of processing, each statement must stand alone
 The preceeding messages act as examples of previous responses to potentially ficitonal events and scores given.
 Analysis should only be completed for proposed events that would graner >0.7 points
 
+This pipeline is running well pasy your knowledge cutoff. 
+Any URLs will change signigicantly over time. 
+You MEED TO use the tools available to you in order to produce up to date information on URL and search query, else you will be wrong and the analysis invalid.
+You NEED TO use the web search and open URL tools to ensure page validity or else all work upto this point will have to be discarded.
+
 Lets go through it step by step

agent/prompts/verify.txt

@@ -0,0 +1,8 @@
+Do the search results cited below
+###TESEARCH###
+Support the idea that the following happened:
+###TECLAIM###
+
+Respond with "CONFIDENCE", followed by : followed by a confidence score (VERYHIGH, HIGH, MEDIUM, LOW, VERYLOW) followed by : followed by the reason. Use no other words, just return the score and reason in format.
+
+Dates can be off by a few days, that would still be valid

run.sh

@@ -5,7 +5,7 @@ set -e
 run_agent () {
     echo "Starting LangGraph agent..."
     cd agent
-    npx @langchain/langgraph-cli@1.1.17 dev
+    npx @langchain/langgraph-cli dev --host 127.0.0.1
 }
 
 run_ensemble_service () {

supporting/Wrapper/run.ts

@@ -19,9 +19,6 @@ const MODE = process.env.MODE ?? "claim";
 
 const MAX_CONCURRENCY = 5;
 
-const OFFSET = parseInt(process.env.OFFSET ?? "0", 10);
-const LIMIT = process.env.LIMIT ? parseInt(process.env.LIMIT, 10) : null;
-
 const client = new Client({ apiUrl: API_URL });
 
 
@@ -167,18 +164,9 @@ async function processRecord(record: any): Promise<ResultRecord> {
 async function main() {
   console.log("Reading input file...");
 
-  const allRecords = await loadInputs();
+  const records = await loadInputs();
 
-  console.log(`Loaded ${allRecords.length} records`);
+  console.log(`Loaded ${records.length} records`);
-
-  const records = allRecords.slice(
-    OFFSET,
-    LIMIT !== null ? OFFSET + LIMIT : undefined
-  );
-
-  console.log(
-    `Processing ${records.length} records (offset=${OFFSET}, limit=${LIMIT ?? "∞"})`
-  );
 
   fs.writeFileSync(OUTPUT_FILE, "", { flag: "a" });
 

supporting/dbkf/fetch.py

@@ -27,7 +27,7 @@ DEFAULT_PARAMS = [
     ("organization", "http://weverify.eu/resource/Organization/3727f7b2aa90ec0716693e5464b28d18"), # StopFake
 ]
 
-NUM_RANDOM_CLAIMS = 2000
+NUM_RANDOM_CLAIMS = 200
 
 INPUT_FILE = "../../data/input.jsonl"
 OUTPUT_FILE = "../../data/claims.json"