IRC-Bench: Recognizing Entities from Contextual Cues in First-Person Reminiscences

# IRC-Bench: Recognizing Entities from Contextual Cues in First-Person Reminiscences
Source: [https://arxiv.org/abs/2605.06142](https://arxiv.org/abs/2605.06142)
[View PDF](https://arxiv.org/pdf/2605.06142)

> Abstract:When people recount personal memories, they often refer to people, places, and events indirectly, relying on contextual cues rather than explicit names\. Such implicit references are central to reminiscence narratives: first\-person accounts of lived experience used in therapeutic, archival, and social settings\. They pose a difficult computational problem because the intended entity must be inferred from dispersed narrative evidence rather than from a local mention\. We introduce IRC\-Bench, the Implicit Reminiscence Context Benchmark, for evaluating implicit entity recognition in reminiscence transcripts\. The benchmark targets non\-locality: entity\-identifying cues are distributed across multiple, non\-contiguous clauses, unlike named entity recognition, entity linking, or coreference resolution\. IRC\-Bench comprises 25,136 samples constructed from 12,337 Wiki\-data\-linked entities across 1,994 transcripts spanning 11 thematic domains\. Each sample pairs an Entity\-Grounded Narrative, in which the target entity is explicitly mentioned, with an Entity\-Elided Narrative, in which direct mentions are removed\. We evaluate 19 configurations across LLM generation, dense retrieval, RAG, and fine\-tuning\. QLoRA\-adapted Llama 3\.1 8B performs best in the open\-world setting \(38\.94% exact match; 51\.59% Jaccard\), while fine\-tuned DPR leads closed\-world retrieval \(35\.38% Hit@1; 71\.49% Hit@10\)\. We release IRC\-Bench with data, code, and evaluation tools\.

## Submission history

From: Yehudit Aperstein \[[view email](https://arxiv.org/show-email/806637db/2605.06142)\] **\[v1\]**Thu, 7 May 2026 12:39:49 UTC \(1,211 KB\)