Attribution Crisis

I want to evaluate and, if it fits, integrate a technique from recent AI research into my project.

## Technique: Attribution Crisis
Attribution Crisis: LLM Search Attribution Scorer

When LLMs generate answers from retrieved documents, they often reproduce substantial portions of source content *without citing it* — exploiting the ecosystem. This tool quantifies that gap using lexical overlap (n-gram coverage), semantic similarity (TF-IDF cosine), and citation recall against the retrieved document set. RAG applicability: Audit your RAG pipeline's attribution behaviour. Identify responses that reproduce source content without credit, compute exploitation risk, and enforce citation policies.

## Reference implementation (Vector Ridge Labs, POC-005)
- Live demo: https://osoman2-gradio-pocs.hf.space/?poc=POC-005
- Source code: https://huggingface.co/spaces/osoman2/Gradio_pocs/tree/main/attribution_crisis

## My project
<Describe your stack, the problem you want to solve and where this technique would sit. Example: "Python FastAPI RAG service over 50k PDFs, Postgres + pgvector, OpenAI embeddings, p95 latency budget 800 ms.">

## What I need from you
1. Read the reference source code and explain in at most 5 bullets how the technique works and what it would change compared to my current approach.
2. Decide whether it fits my case. If it does not, say why and stop there.
3. Propose the smallest viable integration: files or modules to touch, new dependencies, configuration.
4. Implement it behind a flag or as a separate function so it can be compared side by side with what I have today.
5. Add an evaluation script that measures agreement of the new metric with human judgement on a sample of my outputs, before and after, on my own data.
6. List the risks (latency, cost, failure modes) and what to monitor once it is in production.

Constraints: keep my existing stack and conventions, avoid heavy dependencies unless the gain is clear, and reference the file in the demo source whenever you reuse its logic.