arXiv subjects

Hyunkyu Kim

Publications and source records attributed to Hyunkyu Kim.

3 recordsLinked to original sources

arXiv2026

FinLifeBench: Exhaustive Life-Event History and Financial-State Reconstruction from Longitudinal Banking Dialogue

Repeated banking interactions require assistants to maintain complete, current, and traceable customer records as life changes emerge incidentally in routine requests. Existing benchmarks emphasize question answering, bounded episodes, or targeted recall rather than exhaustive longitudinal reconstruction. We introduce FinLifeBench, which evaluates two tasks over the same cumulative dialogue: reconstructing every life-event instance with its first-establishing session and reconstructing a complete 34-path financial state at consecutive checkpoints. The benchmark contains 6,000 eight-turn Korean banking sessions from 20 independent synthetic trajectories, with deterministic, exhaustive gold for 24 event types and 34 state paths and consensus quality assurance. Across eleven LLMs under a full-context condition, event-anchor recall falls from 0.591 at 15 sessions to 0.445 at 300. Errors are driven primarily by omitted events rather than poor anchor localization, while financial-state reconstruction frequently treats superseded or potentially outdated information as current; the best GCA@15 reaches 0.470. Performance on the two reconstruction tasks is only weakly associated. These results show that models can localize evidence for recovered events while still failing to maintain complete and temporally valid longitudinal records.

cs.AI↗

arXiv2026

Listwise Explanation of Embedding-Based Rankings via Semantic Chunk Grouping

Dense embedding rankers score documents through contextual sentence- and passage-level representations. Yet many listwise explanation methods still attribute rankings to isolated words. This feature-unit mismatch leaves word-level features too fragmented for dense semantic ranking. We introduce ChunkGroupSHAP, a listwise Shapley method that clusters semantically related chunks into shared cross-document features. Masking a group perturbs all documents with related evidence, attributing rankings at a granularity closer to dense representations while preserving the listwise setup. Our findings across MS MARCO, FinanceBench, AILACaseDocs, and FinQA with E5 rankers and BM25 show that the best explanation unit is setting-dependent: word features for lexical BM25, corpus-level groups for dense rankers, and query-local grouping for heterogeneous web retrieval. Feature units should thus follow both the ranker's representational granularity and the structure of the retrieved corpus.

cs.IR↗

arXiv2025

Query Generation Pipeline with Enhanced Answerability Assessment for Financial Information Retrieval

As financial applications of large language models (LLMs) gain attention, accurate Information Retrieval (IR) remains crucial for reliable AI services. However, existing benchmarks fail to capture the complex and domain-specific information needs of real-world banking scenarios. Building domain-specific IR benchmarks is costly and constrained by legal restrictions on using real customer data. To address these challenges, we propose a systematic methodology for constructing domain-specific IR benchmarks through LLM-based query generation. As a concrete implementation of this methodology, our pipeline combines single and multi-document query generation with an enhanced and reasoning-augmented answerability assessment method, achieving stronger alignment with human judgments than prior approaches. Using this methodology, we construct KoBankIR, comprising 815 queries derived from 204 official banking documents. Our experiments show that existing retrieval models struggle with the complex multi-document queries in KoBankIR, demonstrating the value of our systematic approach for domain-specific benchmark construction and underscoring the need for improved retrieval techniques in financial domains.

cs.IR↗