Shiping Yang, Jie Wu, Wenbiao Ding, Ning Wu, Shining Liang, Ming Gong, Hongzhi Li, Hengyuan Zhang, Angel X. Chang, Dongmei Zhang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Generative approaches powered by large language models (LLMs) have demonstrated emergent abilities in tasks that require complex reasoning abilities.Yet the generative nature still makes the generated content suffer from hallucinations, thus unsuitable for entity-centric tasks like entity linking (EL) requiring precise entity predictions over a large knowledge base.We present Instructed Generative Entity Linker (INSGENEL), the first approach that enables casual language models to perform entity linking over knowledge bases.Several methods to equip language models with EL capability were proposed in this work, including (i) a sequence-to-sequence training EL objective with instruction-tuning, (ii) a novel generative EL framework based on a light-weight potential mention retriever that frees the model from heavy and non-parallelizable decoding, achieving 4× speedup without compromise on linking metrics.INSGENEL outperforms previous generative alternatives with +6.8 F1 points gain on average, also with a huge advantage in training data efficiency and training compute consumption.In addition, our skillfully engineered incontext learning (ICL) framework for EL still lags behind INSGENEL significantly, reaffirming that the EL task remains a persistent hurdle for general LLMs.

The RFID based school gate system is used to estimate students entering or leaving the school when they go through the school gate with RFID tags. In general, the accuracy of the estimation of RFID is sensitive to complex electromagnetic environment changing. For example, the estimation success rate is high in some circumstance but low when there is a car parking in front of directional readers. In this paper, a method is presented by which the direction of the readers would be automatically adjusted according to the received information of the tags. With this method, the accuracy of the estimation of RFID could maintain high even if the electromagnetic environment changes. Experiments also were carried out to evaluate the feasibility of the proposed method. The results showed that the method is highly suitable to keep a stable success rate of motion direction estimation in school gate systems.

The fingerprint based indoor localization is becoming a dominant solution for its high applicability in complex indoor environment. However, the extensive site survey efforts on manpower and time have become a major bottleneck. Based on the crowdsourcing method, the paper puts forward a novel indoor localization with the fusion of RFID (radio frequency identification devices), PDR (pedestrian dead reckoning)and MM (magnetic matching)technologies. First, a zero-effort fingerprint automated construction and site survey update scheme is proposed with the dual-frequency RFID. Second, in order to solve the problem that step length would vary from person to person which results into positioning bias, the RSS technology and floor map is introduced to aid step length estimation. Third, the particle filter is adopted to fuse the advantages of three different technologies to conduct the accurate indoor positioning. In the experiment, the obtained fingerprint database is demonstrated to possess a comparable accuracy with the human-annotated database. Also, the experiment results show that the proposed method achieves the comparable positioning accuracy and the positioning accuracy is 40% higher than the PDR system or RFID positioning system alone.