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

2026-05-07

AI Summary

This work addresses the challenge of implicit entity recognition in first-person retrospective narratives, where entities such as persons, locations, or events are often omitted without explicit mention. To tackle this problem, the authors propose a novel task formulation that leverages non-local contextual cues for inference and introduce IRC-Bench, a benchmark dataset pairing explicit and implicit narrative instances to systematically evaluate model capabilities. The study integrates large language model (LLM) generation, dense retrieval, retrieval-augmented generation (RAG), and QLoRA fine-tuning across 19 distinct model configurations. Experimental results demonstrate that a QLoRA-finetuned Llama-3.1-8B achieves a 38.94% exact match accuracy under open-world settings, while a finetuned DPR retriever attains a Hit@1 score of 35.38% in closed-world retrieval, confirming both the efficacy of the proposed approach and the difficulty of the benchmark.

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.

Problem

• implicit entity recognition
• reminiscence narratives
• contextual cues
• non-locality
• entity inference

Innovation

• implicit entity recognition
• non-local context
• reminiscence narratives
• IRC-Bench
• entity elision