README.md

### **Implementation Steps: Validating Information with Context**  

Validating the accuracy or degree of truthfulness of a given piece of information requires **context**—factual and relevant details surrounding the claim. Here’s how we approach this process step-by-step:

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### **Step 1: Retrieving Context from Knowledge Graph Substitute - FAISS with Semantic Search**  
Instead of relying on a **traditional Knowledge Graph (KG)**, we use **FAISS (Facebook AI Similarity Search)**, a **faster, scalable, and flexible alternative** for semantic search.  

#### **Why FAISS is Better than a Traditional KG**  
1. **Sentence-Level Retrieval**: Unlike traditional KGs that often rely on pre-defined **entities and relationships**, FAISS uses dense **embeddings** to directly match the **semantic meaning** of entire sentences.  
2. **Scalable and High-Speed Retrieval**: FAISS efficiently handles **millions of embeddings**, making it highly scalable for real-world applications.  
3. **Flexibility**: It works with **unstructured text**, removing the need to pre-process information into entities and relations, which is often time-consuming.  
4. **Generalization**: FAISS enables **approximate nearest neighbor (ANN) search**, allowing retrieval of contextually related results, even if they are not exact matches.

#### **Dataset Used**  
We leverage the **News Category Dataset** ([Kaggle Link](https://www.kaggle.com/datasets/rmisra/news-category-dataset)), which contains **news headlines and short descriptions** across various categories.  

- **Why This Dataset?**  
  It covers a **wide range of topics**, making it useful for general-purpose context building.  
  - Headlines and descriptions provide **rich semantic embeddings** for similarity searches.  
  - Categories allow filtering relevant results if required (e.g., "science" or "technology").

**Process:**
1. We use **SentenceTransformer (all-MiniLM-L6-v2)** to generate embeddings for the query (the input news).  
2. We search against pre-computed embeddings stored in a **FAISS index** to retrieve the **top-K most relevant entries**.  
3. These results form the **initial context**, capturing related information already present in the dataset.

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### **Step 2: Online Search for Real-Time Context**  
To **augment** the context retrieved from FAISS, we incorporate **real-time online search** using an API.  

#### **Why Online Search is Critical?**  
- **Fresh Information**: News and facts evolve, especially in areas like **science, technology, or politics**. Online search ensures access to the **latest updates** that may not exist in the static dataset.  
- **Diverse Sources**: It broadens the scope by pulling information from **multiple credible sources**, reducing bias and enhancing reliability.  
- **Fact-Checking**: Search engines often index **trusted fact-checking websites** that we can incorporate into the context.

**Process:**
1. Use an API with a **search query** derived from the input news.  
2. Retrieve relevant snippets, headlines, or summaries.  
3. Append these results to the **context** built using FAISS.

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### **Step 3: Building Context from Combined Sources**  
Both FAISS-based retrieval and **online search results** are combined into a **single context string**. This provides a **comprehensive knowledge base** around the input information.  

- **Why Combine Both?**  
  - FAISS offers **pre-indexed knowledge**—ideal for **static facts** or concepts.  
  - Online search complements it with **dynamic and up-to-date insights**—perfect for verifying **recent developments**.  

This layered context improves the model’s ability to assess the **truthfulness** of the given information.

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### **Step 4: Truthfulness Prediction with Zero-Shot Classification Model**  
We use the **Facebook/BART-Large-MNLI** model, a **zero-shot classification** model, for evaluation.  

#### **Why BART-Large-MNLI?**  
1. **Zero-Shot Capability**: It can handle claims and hypotheses without needing **task-specific training**—perfect for this flexible, multi-domain use case.  
2. **Contextual Matching**: It compares the input claim (news) with the constructed context to assess **semantic consistency**.  
3. **High Accuracy**: Pre-trained on **natural language inference tasks**, making it adept at understanding relationships like **entailment** and **contradiction**.  
4. **Multi-Label Support**: Can evaluate multiple labels simultaneously, ideal for **degrees of truthfulness**.

**Process:**
1. Input the **news** as the claim and the **context** as the hypothesis.  
2. Compute a **truthfulness score** between **0 and 1**, where:  
   - **0**: Completely **false**.  
   - **1**: Completely **true**.  
3. Generate **explanations** based on the score and suggest actions (e.g., further verification if uncertain).

---

### **End-to-End Example**  
**Input News:**  
"Scientists Demonstrate 'Negative Time' In Groundbreaking Quantum Experiment."  

**Context Built:**  
- **FAISS Search:** Finds prior research on **quantum time reversal** and **entanglement theories**.  
- **Online Search:** Retrieves recent articles discussing **quantum breakthroughs** and expert views.  

**Model Evaluation:**  
- Model compares the news with the combined context and outputs:  
  **Score: 0.72** (Likely True).  

**Result Explanation:**  
```plaintext

News: "Scientists Demonstrate 'Negative Time' In Groundbreaking Quantum Experiment."

Truthfulness Score: 0.72 (Likely true)

Analysis: You can reasonably trust this information, but further verification is always recommended for critical decisions.

```

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### **Why This Approach Works?**  
1. **Balanced Context**: Combines static knowledge (KG substitute) and dynamic knowledge (real-time search).  
2. **Model Flexibility**: Zero-shot model adapts to diverse topics without retraining.  
3. **Scalable and Cost-Effective**: Uses pre-trained models, FAISS indexing, and simple APIs for implementation.  
4. **Interpretability**: Outputs include confidence scores and explanations for transparency.

This modular approach ensures that the **truthfulness assessment** is **scalable**, **explainable**, and **adaptable** to new domains.