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Jiaying Jin
Applied Analytics, Columbia University, NY, USA
Computer Science & IT

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Calibrated Resume-Job Matching for Trustworthy LLM-Assisted Recruiter Screening: Pairwise Matching, Probability Calibration, and Selective Refusal on Two Public Recruitment Datasets Jiaying Jin
Journal of Technology Informatics and Engineering Vol. 4 No. 3 (2025): DECEMBER | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i3.529

Abstract

Recruiter screening increasingly relies on large language model (LLM)-assisted workflows, but high-stakes applications require reproducible matching, calibrated probabilities, and reliable handling of uncertain cases. This study evaluates a screening framework combining matching, calibration, and selective refusal using two public datasets: resume-job-description-fit for supervised pairwise learning and Resume-Screening-Dataset for benchmarking and external generalization. After deterministic preprocessing, we compared cosine similarity, alignment features, TF-IDF pairwise models, and hybrid models integrating text, alignment, and title information. The strongest probabilistic models were calibrated with Platt scaling and isotonic regression and evaluated under confidence-based refusal. On the resume-job-description-fit test set, the best three-class model achieved a macro-F1 of 0.450. For binary shortlist-versus-reject screening, the title-augmented hybrid model obtained 0.654 balanced accuracy, 0.647 F1, and 0.699 AUROC. Platt calibration improved probability estimates by reducing the Brier score from 0.232 to 0.226 and negative log-likelihood from 0.772 to 0.675. Selective refusal further improved in-domain accuracy, while cross-dataset transfer remained weak (AUROC 0.47–0.51). These results indicate that matching, calibration, and selective refusal enhance trustworthy within-domain screening, although human review remains essential under distribution shift.
Evidence-Chain Reliable RAG: Hallucination Detection, Source Attribution, and Deterministic Provenance Explanations Jiaying Jin
Journal of Technology Informatics and Engineering Vol. 4 No. 2 (2025): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i2.535

Abstract

Retrieval-augmented generation (RAG) reduces unsupported generation by grounding answers in source content, but retrieval alone does not guarantee that every output claim is attributable to evidence. This paper presents Evidence-Chain Reliable RAG, an empirical hallucination-detection and provenance framework that scores whether generated response sentences are supported by the corresponding RAG source record. The evaluation uses the complete RAGTruth JSONL data available for this study: 2,965 source records, 17,790 assistant responses, and 14,289 exact-offset hallucination spans across Data2Text, QA, and summarization. The experiment converts word-level spans into response-level, sentence-level, and character-span targets; extracts lexical, BM25, TF-IDF, unsupported-number, unsupported-entity, refusal, and Evidence-Chain Score features; and compares seven methods. On the official held-out test split of 2,700 responses, RandomForest achieved the best case-level F1 of 0.626 and PR-AUC of 0.553. The proposed ECS-Span model achieved case-level F1 of 0.614, ROC-AUC of 0.742, and PR-AUC of 0.536 while also producing deterministic provenance explanations. At sentence level, RandomForest again achieved the highest F1 of 0.321; the proposed method obtained F1 of 0.312, ROC-AUC of 0.777, and PR-AUC of 0.245. Exact character-span localization remained difficult, with character-level F1 of 0.197 because sentence-level predictions often include supported text around shorter hallucinated spans. The findings indicate that evidence-chain features are useful for interpretable RAG auditing, but precise span extraction requires token-level sequence labeling or a comparable fine-grained model.
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