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

Making Large Language Models Better Reasoners with Alignment

Reasoning is a cognitive process of using evidence to reach a sound conclusion. The reasoning capability is essential for large language models (LLMs) to serve as the brain of the artificial general intelligence agent. Recent studies reveal that fine-tuning LLMs on data with the chain of thought (COT) reasoning process can significantly enhance their reasoning capabilities. However, we find that the fine-tuned LLMs suffer from an \textit{Assessment Misalignment} problem, i.e., they frequently assign higher scores to subpar COTs, leading to potential limitations in their reasoning abilities. To address this problem, we introduce an \textit{Alignment Fine-Tuning (AFT)} paradigm, which involves three steps: 1) fine-tuning LLMs with COT training data; 2) generating multiple COT responses for each question, and categorizing them into positive and negative ones based on whether they achieve the correct answer; 3) calibrating the scores of positive and negative responses given by LLMs with a novel constraint alignment loss. Specifically, the constraint alignment loss has two objectives: a) Alignment, which guarantees that positive scores surpass negative scores to encourage answers with high-quality COTs; b) Constraint, which keeps the negative scores confined to a reasonable range to prevent the model degradation. Beyond just the binary positive and negative feedback, the constraint alignment loss can be seamlessly adapted to the ranking situations when ranking feedback is accessible. Furthermore, we also delve deeply into recent ranking-based alignment methods, such as DPO, RRHF, and PRO, and discover that the constraint, which has been overlooked by these approaches, is also crucial for their performance. Extensive experiments on four reasoning benchmarks with both binary and ranking feedback demonstrate the effectiveness of AFT.Comment: Large Language Models; Reasoning; Alignmen

API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs

Recent research has demonstrated that Large Language Models (LLMs) can enhance their capabilities by utilizing external tools. However, three pivotal questions remain unanswered: (1) How effective are current LLMs in utilizing tools? (2) How can we enhance LLMs' ability to utilize tools? (3) What obstacles need to be overcome to leverage tools? To address these questions, we introduce API-Bank, a groundbreaking benchmark, specifically designed for tool-augmented LLMs. For the first question, we develop a runnable evaluation system consisting of 73 API tools. We annotate 314 tool-use dialogues with 753 API calls to assess the existing LLMs' capabilities in planning, retrieving, and calling APIs. For the second question, we construct a comprehensive training set containing 1,888 tool-use dialogues from 2,138 APIs spanning 1,000 distinct domains. Using this dataset, we train Lynx, a tool-augmented LLM initialized from Alpaca. Experimental results demonstrate that GPT-3.5 exhibits improved tool utilization compared to GPT-3, while GPT-4 excels in planning. However, there is still significant potential for further improvement. Moreover, Lynx surpasses Alpaca's tool utilization performance by more than 26 pts and approaches the effectiveness of GPT-3.5. Through error analysis, we highlight the key challenges for future research in this field to answer the third question.Comment: EMNLP 202

Construction of three-dimensional, homogeneous regenerative cartilage tissue based on the ECG-DBM complex

Introduction: The feasibility of using a steel decalcified bone matrix (DBM)-reinforced concrete engineered cartilage gel (ECG) model concept for in vivo cartilage regeneration has been demonstrated in preliminary experiments. However, the regenerated cartilage tissue contained an immature part in the center. The present study aimed to achieve more homogeneous regenerated cartilage based on the same model concept.Methods: For this, we optimized the culture conditions for the engineered cartilage gel-decalcified bone matrix (ECG-DBM) complex based on the previous model and systematically compared the in vitro chondrogenic abilities of ECG in the cartilage slice and ECG-DBM complex states. We then compared the in vivo cartilage regeneration effects of the ECG-DBM complex with those of an equivalent volume of ECG and an equivalent ECG content.Results and discussion: Significant increases in the DNA content and cartilage-specific matrix content were observed for the ECG-DBM complex compared with the ECG cartilage slice, suggesting that the DBM scaffold significantly improved the quality of ECG-derived cartilage regeneration in vitro. In the in vivo experiments, high-quality cartilage tissue was regenerated in all groups at 8 weeks, and the regenerated cartilage exhibited typical cartilage lacunae and cartilage-specific extracellular matrix deposition. Quantitative analysis revealed a higher chondrogenic efficiency in the ECG-DBM group. Specifically, the ECG-DBM complex achieved more homogeneous and stable regenerated cartilage than an equivalent volume of ECG and more mature regenerated cartilage than an equivalent ECG content. Compared with ECG overall, ECG-DBM had a more controllable shape, good morphology retention, moderate mechanical strength, and high cartilage regeneration efficiency. Further evaluation of the ECG-DBM complex after in vitro culture for 7 and 14 days confirmed that an extended in vitro preculture facilitated more homogeneous cartilage regeneration

Degradable mesoporous semimetal antimony nanospheres for near-infrared II multimodal theranostics.

Metallic and semimetallic mesoporous frameworks are of great importance owing to their unique properties and broad applications. However, semimetallic mesoporous structures cannot be obtained by the traditional template-mediated strategies due to the inevitable hydrolytic reaction of semimetal compounds. Therefore, it is yet challenging to fabricate mesoporous semimetal nanostructures, not even mention controlling their pore sizes. Here we develop a facile and robust selective etching route to synthesize monodispersed mesoporous antimony nanospheres (MSbNSs). The pore sizes of MSbNSs are tunable by carefully controlling the partial oxidation of Sb nuclei and the selective etching of the as-formed Sb2O3. MSbNSs show a wide absorption from visible to second near-infrared (NIR-II) region. Moreover, PEGylated MSbNSs are degradable and the degradation mechanism is further explained. The NIR-II photothermal performance of MSbNSs is promising with a high photothermal conversion efficiency of ~44% and intensive NIR-II photoacoustic signal. MSbNSs show potential as multifunctional nanomedicines for NIR-II photoacoustic imaging guided synergistic photothermal/chemo therapy in vivo. Our selective etching process would contribute to the development of various semimetallic mesoporous structures and efficient multimodal nanoplatforms for theranostics

Sequence Analysis of Ancient River Blocking Events in SE Tibetan Plateau Using Multidisciplinary Approaches

The temporary or permanent river blocking event caused by mass movement usually occurs on steep terrain. With the increase of mountain population and land use pressure and the construction of water conservancy and hydropower projects, river blocking events have gradually attracted people’s attention and understanding. The area in this study is affected by strong tectonic activity in the Jinsha River suture zone and the rapid uplift of the Tibetan Plateau. In the past 6000 years, there have been at least five obvious river blocking events in the reach. The number and density are very rare. Combining field investigation, indoor interpretation, laboratory tests, optically stimulated luminescence (OSL) dating, SBAS-InSAR and previous studies, multidisciplinary approaches are used to systematically summarize the analysis methods and further the understanding of one river blocking event and multiple river blocking events from different perspectives. Especially in multiple river blocking events, we can get the wrong results if interaction is not considered. Through this study, the general method of analyzing the river blocking event and the problems that should be paid attention to in sampling are given, and relatively reliable historical results of river blocking events are obtained. This method has applicability to the identification and analysis of river blocking events and age determination of dams with multiple river blockages

Promising Colloidal Rhenium Disulfide Nanosheets: Preparation and Applications for In Vivo Breast Cancer Therapy

Photothermal therapy (PTT) has become an important therapeutic strategy in the treatment of cancer. However, exploring novel photothermal nanomaterials with satisfactory biocompatibility, high photothermal conversion efficiency, and efficient theranostic outcomes, remains a major challenge for satisfying clinical application. In this study, poly-ethylene glycol modified rhenium disulfide (PEG-ReS2) nanosheets are constructed by a simple-liquid phase exfoliation method. The PEG-ReS2 nanosheets were demonstrated to have good solubility, good biocompatibility, low toxicity, and strong capability of accumulating near-infrared (NIR) photons. Under 808 nm laser irradiation, the PEG-ReS2 nanosheets were found to have an excellent photothermal conversion efficiency (PTCE) of 42%. Moreover, the PEG-ReS2 nanosheets were demonstrated to be ideal photothermal transduction agents (PTAs), which promoted rapid cancer cell death in vitro and efficiently ablated tumors in vivo. Interestingly, the potential utility of up-regulation or down-regulation of miRNAs was proposed to evaluate the therapeutic outcomes of PEG-ReS2 nanosheets. The expression levels of a set of miRNAs in tumor-bearing mice were restored to normal levels after PTT therapy with PEG-ReS2 nanosheets. Both down-regulation miRNAs (miR-125a-5p, miR-34a-5p, miR-132-3p, and miR-148b-3p) and up-regulation miRNAs (miR-133a-3p, miR-200c-5p, miR-9-3p, and miR-150-3p) were suggested to be important clinical biomarkers for evaluating therapeutic outcomes of breast cancer-related PTT. This work highlights the great significance of PEG-ReS2 nanosheets as therapeutic nanoagents for cancer therapy

Insights into Promoted Adsorption Capability of Layered BiOCl Nanostructures Decorated with TiO₂ Nanoparticles

Rational design of excellent adsorbents with remarkable adsorption capability and renewable properties is of significant importance to practical applications. Herein, an adsorbent of BiOCl nanoplates decorated with TiO₂ nanoparticles was successfully prepared from porous BiOCl microspheres and tetrabutoxytitanium. Through TiO₂ nanoparticle decoration, the tuned morphology, surface charge property, Brunauer–Emmett–Teller (BET) surface area, and hydrophilic property of the BiOCl microsphere were favorable for achieving optimization of adsorption capability toward Congo red removal with a maximum adsorption amount of 254.7 mg/g. A possible adsorption mechanism model involving the BET surface area, electrostatic interactions, and competitive adsorption between solvent and solute was proposed to account for the enhancement of adsorption capability of BiOCl-TiO₂ nanocomposites compared to those of pure BiOCl and TiO₂. Furthermore, the BiOCl-TiO₂ nanocomposite adsorbent can be simply regenerated by a photocatalysis technique and efficiently reused for adsorption repeatedly. In addition, TiO₂ nanoparticle-based decoration could be a general strategy to boost the adsorption capacity of other Bi-based nanostructures, which is confirmed by Bi₂O₂CO₃-TiO₂, Bi₂S₃-TiO₂, Bi₂MoO₆-TiO₂, and Bi₂O₃-TiO₂ nanocomposites