Permutation-invariant fine-tuning (PI-FT) randomizes field order and applies dropout during embedding model training to eliminate sensitivity to serialization order, reducing order-change penalty from 7.4 to 0.2 nDCG@10 on a generated multilingual DevDataBench while outperforming zero-shot baselines
arXiv preprint arXiv:2402.16829 (2024)
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A graph-based MIS prompt selection method on embedding similarity graphs yields reduced benchmark subsets with highly consistent LLM rankings (Kendall's W ≥ 0.90 in 99.2% of cases) and 25-48% size reduction at higher thresholds.
Language composition in training data creates opposing effects on CLIR and mono-IR performance for Korean-English retrieval, which model merging can partially resolve.
The paper describes the organization, tasks, datasets, and participation results for the TalentCLEF 2026 challenge, which received 113 team registrations and over 400 submissions.
SentimentLens applies aspect-based sentiment analysis, semantic categorization, and cross-modal reconciliation of sentiment and ratings on over 10,000 hotel reviews to reveal regional and category-level variations and operational conflicts.
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Field Order Should Not Matter: Permutation-Invariant Embedding Model Fine-Tuning for Structured Metadata Retrieval
Permutation-invariant fine-tuning (PI-FT) randomizes field order and applies dropout during embedding model training to eliminate sensitivity to serialization order, reducing order-change penalty from 7.4 to 0.2 nDCG@10 on a generated multilingual DevDataBench while outperforming zero-shot baselines
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Consistent and Distinctive: LLM Benchmark Efficiency via Maximum Independent Set Prompt Selection on Similarity Graphs
A graph-based MIS prompt selection method on embedding similarity graphs yields reduced benchmark subsets with highly consistent LLM rankings (Kendall's W ≥ 0.90 in 99.2% of cases) and 25-48% size reduction at higher thresholds.
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Improving Korean-English Cross-Lingual Retrieval: A Data-Centric Study of Language Composition and Model Merging
Language composition in training data creates opposing effects on CLIR and mono-IR performance for Korean-English retrieval, which model merging can partially resolve.
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Overview of the TalentCLEF 2026: Skill and Job Title Intelligence for Human Capital Management
The paper describes the organization, tasks, datasets, and participation results for the TalentCLEF 2026 challenge, which received 113 team registrations and over 400 submissions.
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SentimentLens: Reconciling Sentiment and Ratings via Dual-Modality in the Hospitality Sector
SentimentLens applies aspect-based sentiment analysis, semantic categorization, and cross-modal reconciliation of sentiment and ratings on over 10,000 hotel reviews to reveal regional and category-level variations and operational conflicts.