arXiv subjects

Yingdong Yang

Publications and source records attributed to Yingdong Yang.

3 recordsLinked to original sources

arXiv2026

Submitted and Diagnostic Analysis of Full-Text Temporal Retrieval for LongEval-Sci

LongEval-Sci evaluates scientific retrieval under collection change, where a system should be effective on the current corpus and remain usable as documents accumulate over time. This paper reports both official Task 1 results and development diagnostics for LongEval-Sci 2026. We compare the official PyTerrier BM25 and Qwen3 dense baselines with full-text BM25, additive and router variants, temporal full-text retrieval, temporal+citation retrieval, RM3 query expansion, cross-encoder reranking, and reciprocal rank fusion (RRF). In the official DCTR evaluation, the temporalized full-text runs are our strongest submissions: FT BM25+temporal and FT BM25+temporal+citation obtain the best ARP on all three snapshots (0.285, 0.267, and 0.180 nDCG@10) and reduce snapshot-3 relative change from 0.481 for the BM25 pivot to 0.368. Citation features match the temporal-only variant but do not provide a measurable additional gain in the official summary. Our internal snapshot-1 diagnostics show a complementary pattern: full-text BM25 is the strongest single development retriever (DCTR nDCG@10 = 0.3302, MAP = 0.2853), RRF gives the best deep recall (Recall@1000 = 0.9667), and some uncalibrated overlays can sharply degrade top-rank quality. We therefore conclude that full-text retrieval is the strongest foundation, temporal integration can improve official longitudinal effectiveness when applied to that foundation, and citation evidence still requires cleaner ablation and calibration. Beyond ranking, we also report a qualitative weekly IR-system update-monitoring analysis based on ingestion velocity and stale-coverage drift.

cs.IR↗

arXiv2026

Candidate-Constrained Retrieval-Augmented Generation for LongEval-RAG: System Design and Empirical Analysis

We present a candidate-constrained retrieval-augmented generation system for LongEval-RAG, where each query is associated with an organizer-provided candidate set and all retrieved evidence and final citations must remain within that set. The system combines deterministic provenance tracking with passage-based retrieval, deterministic query expansion, pseudo-relevance feedback (PRF), reciprocal rank fusion (RRF), lightweight evidence reranking, citation-aware evidence aggregation, and optional MiniLM sentence reranking. We evaluate ten pipeline variants using a primary organizer evaluation and a supplementary self-generated diagnostic protocol. The primary evaluation shows that the strongest balanced variant is rule-minilm: a rule-based chunking pipeline with query expansion, PRF, RRF, reranking, citation prior, and late MiniLM sentence selection. This variant obtains the highest BERTScore, retrieval precision, nugget coverage, and average grade among our submissions. The result suggests that the main gain does not come from more complex semantic or topic-shift chunking, but from pairing stable rule-based evidence units with sentence-level neural selection before generation. The supplementary LLM-judge evaluation remains useful for early diagnosis and additional analysis, but it emphasizes different systems than the primary gold-answer and nugget-based evaluation, highlighting the need for multi-metric RAG evaluation.

cs.CL↗

arXiv2025

Bayesian Analysis of Hotel Booking Cancellations: A Hierarchical Modeling Approach

This study presents a comprehensive Bayesian analysis of hotel booking cancellations using PyMC, comparing three model specifications of increasing complexity. We investigate how lead time, special requests, and parking requirements affect cancellation probability, and explore interaction effects with hotel type. Using MCMC sampling (NUTS algorithm) on 5,000 booking records, we find strong evidence that longer lead times increase cancellation probability (posterior mean: 0.600, 95\% HDI: [0.532, 0.661]), while special requests (posterior mean: -0.642) and parking (posterior mean: -3.879) significantly reduce cancellation risk. Model comparison via WAIC reveals that the full interaction model provides the best predictive performance, suggesting that the effects of booking characteristics vary systematically between city and resort hotels. This Bayesian approach enables full uncertainty quantification and provides actionable insights for revenue management.

stat.AP↗