Cong Tran

Graph retrieval for Wikipedia multi-hop question answering no longer needs a large model to run open extraction over the whole corpus: a local GLiNER2 extractor builds the graph under a preset relation schema, then discovers corpus-specific relations from entity pairs the schema left unconnected and injects the resulting triples after the fact. On 1,000 questions each from MuSiQue, 2WikiMultihopQA and HotpotQA, the full pipeline reaches F1 of 0.4577, 0.6637 and 0.7154, above the BGE-based HippoRAG2 baseline; on MuSiQue, adaptive expansion adds 93 relation types and 1,691 triples and raises F1 by 0.0225 over the seed-only graph. Final answer generation still calls GPT-4o-mini.

Graph retrieval for Wikipedia multi-hop question answering no longer needs a large model to run open extraction over the whole corpus: a local GLiNER2 extractor builds the graph under a preset relation schema, then discovers corpus-specific relations from entity pairs the schema left unconnected and injects the resulting triples after the fact. On 1,000 questions each from MuSiQue, 2WikiMultihopQA and HotpotQA, the full pipeline reaches F1 of 0.4577, 0.6637 and 0.7154, above the BGE-based HippoRAG2 baseline; on MuSiQue, adaptive expansion adds 93 relation types and 1,691 triples and raises F1 by 0.0225 over the seed-only graph. Final answer generation still calls GPT-4o-mini.

Retrieval-augmented multihop question answering suffers from noisy context, and CLaRa's compression of retrieved documents into dense latent memory makes the structural relations between documents harder to exploit. GraphCLaRa places a roughly 1.3M-parameter graph reranker before the frozen CLaRa reader: it builds a query-aware document graph, propagates evidence with a graph attention network, and adaptively decides how many documents to pass on. Across four QA benchmarks it raises answer F1 on three, by 6.6 on 2Wiki, 6.4 on MuSiQue and 2.5 on NQ, while using fewer documents, and trails by 1.1 F1 on the retrieval-saturated HotpotQA.

Retrieval-augmented multihop question answering suffers from noisy context, and CLaRa's compression of retrieved documents into dense latent memory makes the structural relations between documents harder to exploit. GraphCLaRa places a roughly 1.3M-parameter graph reranker before the frozen CLaRa reader: it builds a query-aware document graph, propagates evidence with a graph attention network, and adaptively decides how many documents to pass on. Across four QA benchmarks it raises answer F1 on three, by 6.6 on 2Wiki, 6.4 on MuSiQue and 2.5 on NQ, while using fewer documents, and trails by 1.1 F1 on the retrieval-saturated HotpotQA.