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RWGBench: Evaluating Scholarly Positioning in Related Work Generation
New benchmark RWGBench evaluates LLM performance in generating related work sections from a citation-level scholarly positioning perspective, going beyond traditional summarization metrics.
A new benchmark called RWGBench has been posted on arXiv for evaluating large language models in related work generation (RWG). The paper notes that while LLMs have shown strong fluency in scientific writing, the evaluation of RWG remains limited.
Existing RWG evaluations largely inherit summarization-oriented metrics, using lexical or semantic similarity to reference sections as proxies for quality. However, related work writing is fundamentally a citation-level scholarly positioning task that requires selecting, organizing, and framing prior work to establish the contribution's position.
The paper appears under arXiv cs.AI, paper ID 2606.24894. As LLMs increasingly participate in academic writing, evaluating their capabilities from a scholarly positioning perspective is more meaningful than mere text similarity.
Why it matters
This benchmark provides more precise metrics for evaluating LLMs' substantive contribution in academic writing, helping advance the quality of AI-assisted scientific writing.