17 papers
Event Ontology Expansion via LLM-Based Conceptualization
Weicheng Ren, Zixuan Li, Long Bai +3
Event ontology expansion aims to discover emerging event types from data and extend them to appropriate positions in the existing event ontology.. Existing methods typically cluste…
Querit-Reranker: Training Compact Multilingual Rerankers via Efficient Label-Free Distribution Adaptation
Yunfei Zhong, Jun Yang, Wei Huang +7
Deployable multilingual rerankers must generalize across languages, domains, and target ranking tasks while remaining efficient enough for second-stage reranking. However, adapting…
Lost in a Single Vector: Improving Long-Document Retrieval with Chunk Evidence Aggregation
Shanshan Lyu, Yiwei Wang, Yujun Cai +2
Dense retrieval ranks one query vector against one document vector. On long documents, this interface can fail when a short but decisive span is weakened during document encoding b…
SAW: Stage-Aware Dynamic Weighting for Multi-Objective Reinforcement Learning in Large Language Models
Yuchen He, Baolong Bi, Shenghua Liu +7
Although multi-objective reinforcement learning (MORL) is central to aligning large language models with complex human preferences, the prevailing practice of static weighted summa…
Code-on-Graph: Iterative Programmatic Reasoning via Large Language Models on Knowledge Graphs
Weiwei Ding, Zixuan Li, Long Bai +7
Knowledge Graphs (KGs) are widely used to mitigate the limitations of Large Language Models (LLMs), such as outdated knowledge and hallucinations. Existing LLM-KG integration frame…
Can LLM Rerankers Predict Their Own Ranking Performance?
Shiyu Ni, Keping Bi, Jiafeng Guo +3
Retrieval effectiveness varies substantially across queries, making it important to estimate ranking quality before relevance judgments are available. Query performance prediction…