A Unified Framework for Multi-intent Spoken Language Understanding with prompting

Multi-intent Spoken Language Understanding has great potential for widespread implementation. Jointly modeling Intent Detection and Slot Filling in it provides a channel to exploit the correlation between intents and slots. However, current approaches are apt to formulate these two sub-tasks differently, which leads to two issues: 1) It hinders models from effective extraction of shared features. 2) Pretty complicated structures are involved to enhance expression ability while causing damage to the interpretability of frameworks. In this work, we describe a Prompt-based Spoken Language Understanding (PromptSLU) framework, to intuitively unify two sub-tasks into the same form by offering a common pre-trained Seq2Seq model. In detail, ID and SF are completed by concisely filling the utterance into task-specific prompt templates as input, and sharing output formats of key-value pairs sequence. Furthermore, variable intents are predicted first, then naturally embedded into prompts to guide slot-value pairs inference from a semantic perspective. Finally, we are inspired by prevalent multi-task learning to introduce an auxiliary sub-task, which helps to learn relationships among provided labels. Experiment results show that our framework outperforms several state-of-the-art baselines on two public datasets.Comment: Work in progres

RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge

LLMs and AI chatbots have improved people's efficiency in various fields. However, the necessary knowledge for answering the question may be beyond the models' knowledge boundaries. To mitigate this issue, many researchers try to introduce external knowledge, such as knowledge graphs and Internet contents, into LLMs for up-to-date information. However, the external information from the Internet may include counterfactual information that will confuse the model and lead to an incorrect response. Thus there is a pressing need for LLMs to possess the ability to distinguish reliable information from external knowledge. Therefore, to evaluate the ability of LLMs to discern the reliability of external knowledge, we create a benchmark from existing knowledge bases. Our benchmark consists of two tasks, Question Answering and Text Generation, and for each task, we provide models with a context containing counterfactual information. Evaluation results show that existing LLMs are susceptible to interference from unreliable external knowledge with counterfactual information, and simple intervention methods make limited contributions to the alleviation of this issue

Effects of insecticidal proteins of Enterobacter cloacae NK on cellular immunity of Galleria mellonella larvae

Enterobacter cloacae produces insecticidal proteins capable of causing toxicity in pests, but the insecticidal mechanisms of these proteins for insect control remain unclear. To elucidate the mechanisms, the purified insecticidal protein from E. cloacae NK was administered to Galleria mellonella larvae either by intraperitoneal injection or by feeding. The number of hemocytes, apoptosis in immune cells, and polyphenol oxidase (PO) activity of G. mellonella larvae were detected by hemocytometer, Annexin V-FITC/PI, and UV–vis spectrophotometer, respectively. With the extension of the invasion time of NK insecticidal protein, the number of hemocytes in G. mellonella larvae decreased significantly (p < 0.05), whereas the apoptosis rate of hemocytes increased. The activity of PO showed a trend of rising-peak-sharp decline and the melanization reaction was deepened simultaneously. Moreover, the phagocytosis and coating capabilities of hemocytes decreased, and the intraperitoneal injection method was more effective than the feeding method. Taking together, the insecticidal protein of E. cloacae NK inhibits and destroys the cellular immune response of G. mellonella larvae, which suggests an important role in killing the host insect

Accelerating the Phosphatase-like Activity of Uio-66-NH₂ by Catalytically Inactive Metal Ions and Its Application for Improved Fluorescence Detection of Cardiac Troponin I

Compared with natural enzymes, nanozymes usually exhibit much lower catalytic activities, which limit the sensitivities of nanozyme-based immunoassays. Herein, several metal ions without enzyme-like activities were engineered onto Uio-66-NH2 nanozyme through postsynthetic modification. The obtained Mn+@Uio-66-NH2 (Mn+ = Zn2+, Cd2+, Co2+, Ca2+and Ni2+) exhibited improved phosphatase-like catalytic activities. In particular, a 12-fold increase in the catalytic efficiency (kcat/Km) of Uio-66-NH2 was observed after the modification with Zn2+. Mechanism investigations indicate that both the amino groups and oxygen-containing functional groups in Uio-66-NH2 are the binding sites of Zn2+, and the modified Zn2+ ions on Uio-66-NH2 serve as the additional catalytic sites for improving the catalytic performance. Furthermore, the highly active Zn2+@Uio-66-NH2 was used as a nanozyme label to develop a fluorescence immunoassay method for the detection of cardiac troponin I (cTnI). Compared with pristine Uio-66-NH2, Zn2+@Uio-66-NH2 can widen the linear range by 1 order of magnitude (from 10 pg/mL–1 μg/mL to 1 pg/mL–1 μg/mL) and also lower the detection limit by 5 times (from 4.7 pg/mL to 0.9 pg/mL)