README.md

---
title: MuDABench Viewer
emoji: 📊
colorFrom: blue
colorTo: indigo
sdk: static
pinned: false
license: apache-2.0
---

# 📊 MuDABench Viewer

Static viewer for **MuDABench** — a benchmark for *multi-document analytical
question answering* over large-scale financial document collections
(Chinese A-share + US market filings).

- Paper: <https://arxiv.org/abs/2604.22239> (ACL 2026 Findings)
- Code: <https://github.com/Zhanli-Li/MuDABench>
- Dataset: <https://huggingface.co/datasets/Zhanli-Li/MuDABench>

Two tabs:

- **📚 Corpus** — every source document (589 PDFs). A searchable dropdown selects
  a document by title (`symbol · year · doctype`); filter by doc type or year.
  The panel shows the document's structured metadata — every `value_*` field
  observed across the questions that cite it, with its schema description — and
  renders the **PDF inline** (streamed from the Hugging Face dataset CDN). A
  collapsible list links to each question that references the document.
- **❓ Eval** — the 332 analytical questions (166 `simple` + 166 `complex`;
  toggle between the two sets). Each question shows the gold **final answer**,
  the **supporting facts** (`source_answer`), and the **supporting documents**
  (each `value_*` field + description; click a document to jump to it in the
  Corpus tab).

## Data

| File | Contents |
|---|---|
| `simple.json` | 166 questions with concise final answers (source, from HF) |
| `complex.json` | 166 questions with longer analytical final answers (source, from HF) |
| `eval.json` | 332 derived question records (Eval tab) |
| `corpus_index.json` | 589 derived document records (Corpus tab) |

The two derived files are regenerated from the source JSON with:

```bash
python scripts/build_data.py
```

**PDFs are not bundled** (the corpus is ~4 GB). Each corpus record's `pdf`
field points at the dataset CDN
(`https://huggingface.co/datasets/Zhanli-Li/MuDABench/resolve/main/data/pdf/<id>.pdf`),
which serves PDFs `inline` with permissive CORS, so the `<iframe>` renders them
directly without the Space having to host them.

## Local dev

```bash
python -m http.server 8000   # then open http://localhost:8000/
```

Serves statically — no build step. Push to the HF Space remote to deploy.

Dataset & code license: Apache-2.0.