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README.md

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+---
+annotations_creators:
+- expert-generated
+language:
+- en
+license: cc-by-4.0
+multilinguality: monolingual
+pretty_name: SciVer
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- fact-checking
+---
+# SCIVER: A Benchmark for Multimodal Scientific Claim Verification
+
+<p align="center">
+  <a href="https://github.com/QDRhhhh/SciVer">🌐 Github</a> •
+  <a href="">📖 Paper</a> •
+  <a href="https://huggingface.co/datasets/chengyewang/SciVer">🤗 Data</a>
+</p>
+
+## 📰 News
+- [May 15, 2025] SciVer has been accepted by ACL 2025 Main!
+
+## 👋 Overview
+
+![image-20250603111710602](./README.assets/image-20250603111710602.png)
+
+**SCIVER** is the first benchmark specifically designed to evaluate the ability of foundation models to verify scientific claims across **text**, **charts**, and **tables**. It challenges models to reason over complex, multimodal contexts with **fine-grained entailment labels** and **expert-annotated rationales**.
+
+> 📌 “Can Multimodal Foundation Models Reason Over Scientific Claims with Text, Tables, and Charts?”
+
+------
+
+## 🌟 Highlights
+
+- 🧪 **3,000 expert-annotated examples** from **1113 scientific papers**
+- 🧠 Four core **reasoning subsets**:
+  - Direct
+  - Parallel
+  - Sequential
+  - Analytical
+- 📚 Context includes **text paragraphs, multiple tables, and charts**
+- 🔍 Labels: `Entailed`, `Refuted`
+- 📈 Evaluated across **21 leading foundation models**, including GPT-4o, Gemini, Claude 3.5, Qwen2.5-VL, LLaMA-3.2-Vision, etc.
+- ⚖️ Includes **step-by-step rationale** and **automated accuracy evaluation**
+
+------
+
+## 🧩 Benchmark Structure
+
+Each SCIVER sample includes:
+
+- A **claim** grounded in multimodal scientific context
+- **Contextual inputs**: text, tables (as images), charts (as images)
+- A **gold entailment label** (entailed / refuted)
+- **Supporting evidence** and a **reasoning rationale**
+
+### 🧠 Subsets by Reasoning Type
+
+1. **Direct Reasoning** – extract simple facts
+2. **Parallel Reasoning** – synthesize info from multiple sources
+3. **Sequential Reasoning** – perform step-by-step inference
+4. **Analytical Reasoning** – apply domain expertise and logic
+
+------
+
+## 📊 Model Evaluation
+
+We evaluate 21 models using Chain-of-Thought prompting.
+
+| Model            | Accuracy  |
+| ---------------- | --------- |
+| 🧑‍🔬Human Expert   | **93.8%** |
+| o4-mini (OpenAI) | 77.7%     |
+| GPT-4o           | 70.9%     |
+| Qwen2.5-VL-72B   | 69.4%     |
+| InternVL3-38B    | 62.5%     |
+
+> Text-only versions of models drop 35–53% in accuracy — showing **multimodal context is essential**.
+
+------
+
+## 🛠️ Quickstart
+
+### 🔁 Step 0: Installation
+
+```bash
+git clone https://github.com/QDRhhhh/SciVer.git
+cd SciVer
+conda create --name sciver python=3.10
+conda activate sciver
+pip install -r requirements.txt
+```
+
+### 🔁 Step 1: Download Dataset from huggingface
+
+```bash
+git lfs install
+git clone https://huggingface.co/datasets/chengyewang/SciVer
+```
+
+### 🔁 Step 2: Run Model Inference
+
+```bash
+bash scripts/vllm_large.sh
+```
+
+This will generate model responses and save them to:
+
+```
+./outputs/
+```
+
+### ✅ Step 3: Evaluate Model Accuracy
+
+```bash
+python acc_evaluation.py
+```
+
+The processed results and accuracy scores will be saved to:
+
+```
+./processed_outputs/
+```
+
+------
+
+## 🤝 Contributing
+
+We welcome contributions for:
+
+- 🧬 Domain extension (e.g., biology, medicine)
+- 🔧 Additional model adapters
+- 📈 New evaluation metrics and visualization tools