Update README.md

README.md

@@ -2,31 +2,57 @@
 license: mit
 task_categories:
 - text-generation
+- text-retrieval
 language:
 - en
 tags:
-- code
+- related-work-generation
+- scholarly-positioning
+- citation-evaluation
+- retrieval-augmented-generation
 pretty_name: RWGBench
 size_categories:
 - 10K<n<100K
 ---
 
-RWGBench is a benchmark for evaluating related work generation (RWG) through the lens of **scholarly positioning** and citation decision-making, rather than surface-level text similarity. It includes a large-scale paper collection, a 1,091,394 retrieval corpus, 40,108 papers in computer sccience with full text and citation lists, a curated 100-paper test set and a fully automated evaluation framework.
+# RWGBench
 
+RWGBench is a benchmark for evaluating related work generation as a
+citation-centric scholarly positioning task. It tests whether a system can
+select, organize, and frame prior work for a target paper, rather than only
+producing fluent text that resembles a reference related work section.
 
+## Files
 
-## Dataset
+| File | Entries | Description |
+|---|---:|---|
+| `papers.json` | 40,108 | Source paper collection with parsed metadata, sections, related work text, and citation identifiers. |
+| `corpus.json` | 1,091,394 | Retrieval corpus. Each entry contains `doc_id`, `title`, and `abstract`. |
+| `gold100_papers.json` | 100 | Peer-reviewed evaluation split used for the main experiments. The papers are matched to accepted ICLR, NeurIPS, or ICML records. |
 
-| File                  | # Entries | Description                                                  |
-| --------------------- | --------: | ------------------------------------------------------------ |
-| `papers.json`         |    40,108 | CS papers (arXiv 2020–2025) with full text and citation lists |
-| `corpus.json`         | 1,091,394 | Retrieval corpus — title + abstract per paper                |
-| `gold100_papers.json` |       100 | Quality-filtered test set with gold related work sections    |
+## Evaluation Split Composition
 
-<details>
-<summary>corpus.json &nbsp;—&nbsp; retrieval candidates</summary>
-<br>
+The `gold100_papers.json` split contains 100 peer-reviewed papers matched by
+exact normalized title to accepted OpenReview records. Its venue distribution
+is:
 
+| Venue | Papers |
+|---|---:|
+| ICLR | 42 |
+| NeurIPS | 34 |
+| ICML | 24 |
+
+The publication-year distribution is:
+
+| Year | Papers |
+|---|---:|
+| 2023 | 2 |
+| 2024 | 61 |
+| 2025 | 37 |
+
+## Schema
+
+### `corpus.json`
 
 ```json
 {
@@ -36,12 +62,7 @@ RWGBench is a benchmark for evaluating related work generation (RWG) through the
 }
 ```
 
-</details>
-
-<details>
-<summary>gold100_papers.json &nbsp;—&nbsp; test papers</summary>
-<br>
-
+### `gold100_papers.json`
 
 ```json
 {
@@ -50,16 +71,41 @@ RWGBench is a benchmark for evaluating related work generation (RWG) through the
   "abstract": "...",
   "introduction": "...",
   "related_work": "Model quantization. Quantization is a widely employed technique...",
-  "citations": [191955, 118706, 517176, 264652, 1589, 2253, ... ],
-  "overall_score": 90.6
+  "citations": [191955, 118706, 517176, 264652, 1589],
+  "peer_review": {
+    "match_type": "normalized_title_exact",
+    "primary_venue": {
+      "venue": "ICLR",
+      "year": "2024",
+      "venue_id": "ICLR.cc/2024/Conference",
+      "openreview_id": "UmMa3UNDAz"
+    }
+  }
 }
 ```
 
-`citations` is a list of `doc_id`s from `corpus.json`. `overall_score` is a GLM-4 quality rating (0–100).
-</details>
+`citations` contains `doc_id` values from `corpus.json`. The reference related
+work section is author-written text from the target paper. Venue metadata is
+stored under `peer_review.primary_venue`.
+
+## Use With The Code Repository
+
+Download the dataset files into the GitHub repository's `data/` directory:
+
+```text
+data/
+  papers.json
+  corpus.json
+  gold100_papers.json
+```
+
+Then run the generation and evaluation scripts from the code repository.
+
+## Data Collection And Use
 
+RWGBench is built from public scholarly documents and metadata. Source
+documents may have heterogeneous licenses, so users should follow the license
+terms of the underlying papers when redistributing document-derived text.
 
-## Data Collection & Ethics
-- **Source**: Public arXiv metadata (2020–2025), respecting arXiv's [terms of use](https://arxiv.org/help/api/tou)
-- **Copyright**: Only metadata (titles, abstracts) and author-written content; no full-text.
-- **Ethical Use**: Intended for non-commercial research only
+The benchmark is intended for research on retrieval-augmented generation,
+citation selection, scholarly writing evaluation, and related work generation.