Merge pull request #1

README.md

@@ -10,6 +10,7 @@ tags:
 - scholarly-positioning
 - citation-evaluation
 - retrieval-augmented-generation
+- code
 pretty_name: RWGBench
 size_categories:
 - 10K<n<100K
@@ -22,6 +23,12 @@ 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.
 
+It is presented in the paper
+[RWGBench: Evaluating Scholarly Positioning in Related Work Generation](https://huggingface.co/papers/2606.24894).
+
+The official repository containing evaluation scripts and baselines is
+available on GitHub at [BFTree/RWGBench](https://github.com/BFTree/RWGBench).
+
 ## Files
 
 | File | Entries | Description |
@@ -101,6 +108,28 @@ data/
 
 Then run the generation and evaluation scripts from the code repository.
 
+### Sample Usage
+
+For integration into custom pipelines using the evaluation code from the
+GitHub repository:
+
+```python
+from src.evaluation.single_paper_evaluator import SinglePaperEvaluator
+
+evaluator = SinglePaperEvaluator(
+    gold_papers_path="data/gold100_papers.json",
+    corpus_path="data/corpus.json",
+    use_llm_judge=False,       # set True to include LLM-as-Judge scores
+    llm_model="deepseek-v3",   # any OpenAI-compatible model name
+)
+
+result = evaluator.evaluate(
+    paper_id=...,         # int
+    generated_text=...,   # str, text with [1], [2], ... citations
+    citation_list=[...],  # list of doc_id ints or title strings; index i maps to citation [i+1]
+)
+```
+
 ## Data Collection And Use
 
 RWGBench is built from public scholarly documents and metadata. Source