William Jeynes
57 lines
2.9 KiB
Markdown
57 lines
2.9 KiB
Markdown
# AI models for identifying trigger events in disinformation analysis
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Final Dissertation Submission Repository
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## Abstract
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-- todo --
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[Project Presentation](https://jillweynes.github.io/LLMsForDisinformationPrediction-GraphVizBuilt/presentation)
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## Generated Database Link and Usage Experiments
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Generated Dataset Link: [https://huggingface.co/datasets/WillJeynes/LLMsForDisinformationAnalysis-Dataset](https://huggingface.co/datasets/WillJeynes/LLMsForDisinformationAnalysis-Dataset)
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Graph-Based Dataset Visualisation: [https://jillweynes.github.io/LLMsForDisinformationPrediction-GraphVizBuilt/](https://jillweynes.github.io/LLMsForDisinformationPrediction-GraphVizBuilt/)
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Usage Experiments (incl graph visualisation) Source Code: [https://github.com/WillJeynes/LLMsForDisinformationPrediction](https://github.com/WillJeynes/LLMsForDisinformationPrediction)
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# This repository:
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## Solution Diagram
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-- todo --
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## Classifier Refinement
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[See RAGAS_Service](/supporting/RAGAS_Service/)
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## Agent Refinement
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[See agent](/agent/)
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## Repository Structure
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```
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├── run.sh # Bash script to run project elements from one place
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├── data/ # Holder from project data
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| ├── blocked.jsonl # Web search results blocked by the Iffy list
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| ├── error.log # Log file containing critical exceptions
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| ├── claims.json # Retreived claims from dbkf fetcher
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| ├── dev-eng.csv
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| ├── train-eng.csv # Normalized disinformation claims in CSV format from CLAN
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| ├── Iffy.json # Iffy dataset of disinformation domains
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| ├── input.jsonl # Response in cleaned format to give as context to agent
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| ├── ranked.jsonl # Cleaned trigger event response from scorer frontend
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| └── results.jsonl # Output from wrapper script, read and modified by scorer
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├── literature/
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| └── report.pdf # Final submission report
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├── agent/ # Code for main project pipeline
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| ├── agent.ts # Graph definition file
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| ├── conditionals/ # Conditional translations
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| ├── prompts/ # System promps, plus replacement code
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| ├── tools/ # Internal and LLM facing tools
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| └── utils/ # Logger
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└── supporting/
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├── dbkf/ # Tool to download claims from DBKF for use in wrapper
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├── RAGAS_Service # Small python API to make RAGAS metrics available in the TS projects (required to run pipeline)
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├── scorer # Frontend for labelling data, plus associated analysis
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└── Wrapper # Bulk run pipeline on pre-downloaded claims
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```
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