in-context-retrieval

This paper systematically studies in-context retrieval at million-token scale, introducing BlockSearch, a 0.6B LM retriever, and analyzing attention dilution. The model matches or outperforms dense retrieval on benchmarks like MS MARCO and NQ, and significantly outperforms on tasks requiring different similarity notions, highlighting the potential of in-context retrieval while emphasizing attention control under extreme context growth.