FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models

Collecting high-quality labeled data for model training is notoriously time-consuming and labor-intensive for various NLP tasks. While copious solutions, such as active learning for small language models (SLMs) and prevalent in-context learning in the era of large language models (LLMs), have been proposed and alleviate the labeling burden to some extent, their performances are still subject to human intervention. It is still underexplored how to reduce the annotation cost in the LLMs era. To bridge this, we revolutionize traditional active learning and propose an innovative collaborative learning framework FreeAL to interactively distill and filter the task-specific knowledge from LLMs. During collaborative training, an LLM serves as an active annotator inculcating its coarse-grained knowledge, while a downstream SLM is incurred as a student to filter out high-quality in-context samples to feedback LLM for the subsequent label refinery. Extensive experiments on eight benchmark datasets demonstrate that FreeAL largely enhances the zero-shot performances for both SLM and LLM without any human supervision. The code is available at https://github.com/Justherozen/FreeAL .Comment: Accepted to EMNLP 2023 (Main conference

Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective

We investigate the problem of learning with noisy labels in real-world annotation scenarios, where noise can be categorized into two types: factual noise and ambiguity noise. To better distinguish these noise types and utilize their semantics, we propose a novel sample selection-based approach for noisy label learning, called Proto-semi. Proto-semi initially divides all samples into the confident and unconfident datasets via warm-up. By leveraging the confident dataset, prototype vectors are constructed to capture class characteristics. Subsequently, the distances between the unconfident samples and the prototype vectors are calculated to facilitate noise classification. Based on these distances, the labels are either corrected or retained, resulting in the refinement of the confident and unconfident datasets. Finally, we introduce a semi-supervised learning method to enhance training. Empirical evaluations on a real-world annotated dataset substantiate the robustness of Proto-semi in handling the problem of learning from noisy labels. Meanwhile, the prototype-based repartitioning strategy is shown to be effective in mitigating the adverse impact of label noise. Our code and data are available at https://github.com/fuxiAIlab/ProtoSemi

Behavioral/Cognitive Acute and Long-Term Suppression of Feeding Behavior by POMC Neurons in the Brainstem and Hypothalamus, Respectively

POMC-derived melanocortins inhibit food intake. In the adult rodent brain, POMC-expressing neurons are located in the arcuate nucleus (ARC) and the nucleus tractus solitarius (NTS), but it remains unclear how POMC neurons in these two brain nuclei regulate feeding behavior and metabolism differentially. Using pharmacogenetic methods to activate or deplete neuron groups in separate brain areas, in the present study, we show that POMC neurons in the ARC and NTS suppress feeding behavior at different time scales. Neurons were activated using the DREADD (designer receptors exclusively activated by designer drugs) method. The evolved human M3-muscarinic receptor was expressed in a selective population of POMC neurons by stereotaxic infusion of Cre-recombinase–dependent, adenoassociated virus vectors into the ARC or NTS of POMC-Cre mice. After injection of the human M3-muscarinic receptor ligand clozapine-N-oxide (1 mg/kg, i.p.), acute activation of NTS POMC neurons produced an immediate inhibition of feeding behavior. In contrast, chronic stimulation was required for ARC POMC neurons to suppress food intake. Using adeno-associated virus delivery of the diphtheria toxin receptor gene, we found that diphtheria toxin–induced ablation of POMC neurons in the ARC but not the NTS, increased food intake, reduced energy expenditure, and ultimately resulted in obesity and metabolic and endocrine disorders. Our results reveal different behavioral functions of POMC neurons in the ARC and NTS, suggesting that POMC neurons regulate feeding and energy homeostasis by integrating long-term adiposity signals from the hypothalamus and short-term satiety signals from the brainstem

Advances in understanding of health-promoting benefits of medicine and food homology using analysis of gut microbiota and metabolomics

The health-promoting benefits of medicine and food homology (MFH) are known for thousands of years in China. However, active compounds and biological mechanisms are unclear, greatly limiting clinical practice of MFH. The advent of gut microbiota analysis and metabolomics emerge as key tools to discover functional compounds, therapeutic targets, and mechanisms of benefits of MFH. Such studies hold great promise to promote and optimize functional efficacy and development of MFH-based products, for example, foods for daily dietary supplements or for special medical purposes. In this review, we summarized pharmacological effects of 109 species of MFH approved by the Health and Fitness Commission in 2015. Recent studies applying genome sequencing of gut microbiota and metabolomics to explain the activity of MFH in prevention and management of health consequences were extensively reviewed. We discussed the potentiality in future to decipher functional activities of MFH by applying metabolomics-based polypharmacokinetic strategy and multiomics technologies. The needs for personalized MFH recommendations and comprehensive databases have also been highlighted. This review emphasizes current achievements and challenges of the analysis of gut microbiota and metabolomics as a new avenue to understand MFH

Towards Long-term Annotators: A Supervised Label Aggregation Baseline

Relying on crowdsourced workers, data crowdsourcing platforms are able to efficiently provide vast amounts of labeled data. Due to the variability in the annotation quality of crowd workers, modern techniques resort to redundant annotations and subsequent label aggregation to infer true labels. However, these methods require model updating during the inference, posing challenges in real-world implementation. Meanwhile, in recent years, many data labeling tasks have begun to require skilled and experienced annotators, leading to an increasing demand for long-term annotators. These annotators could leave substantial historical annotation records on the crowdsourcing platforms, which can benefit label aggregation, but are ignored by previous works. Hereby, in this paper, we propose a novel label aggregation technique, which does not need any model updating during inference and can extensively explore the historical annotation records. We call it SuperLA, a Supervised Label Aggregation method. Inside this model, we design three types of input features and a straightforward neural network structure to merge all the information together and subsequently produce aggregated labels. Based on comparison experiments conducted on 22 public datasets and 11 baseline methods, we find that SuperLA not only outperforms all those baselines in inference performance but also offers significant advantages in terms of efficiency

Determination of emodin, chrysophanol, and physcion by HPLC in the chinese medicine rumex japonicus houtt.

A high performance liquid chromatography (HPLC) method for determination of emodin, chrysophanol and physcion in Rumex japonicus Houtt. from different places in Zhejiang Province (China) was established. The samples were separated on a Agilent Zorbax SB-C18 column (2.1 × 150 mm, 5 μm) at at a flow rate of 0.4 mL/min using acetonitrile-water as the mobile phase, with a gradient elution. The column oven temperature was 30 °C and the wavelength of detection used was 285 nm. The three anthraquinones were well separated by this HPLC method. Linearities of emodin, chrysophanol, and physcion were good in the ranges of 0.23-46 (r = 0.998), 0.2-50 (r = 0.9999), and 0.5-50 (r = 0.9997) μg/mL, respectively. The average recoveries were 99.01 % for emodin, 100.73 % for chrysophanol, 101.12 % for physcion, with RSD 1.23, 1.09 and 3.42 %, respectively. The method is simple, accurate, reproducible, and it can be used to quality control of Rumex japonicus Houtt.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Curdlan Prevents the Cognitive Deficits Induced by a High-Fat Diet in Mice via the Gut-Brain Axis

A high-fat (HF) diet is a major predisposing factor of neuroinflammation and cognitive deficits. Recently, changes in the gut microbiota have been associated with neuroinflammation and cognitive impairment, through the gut-brain axis. Curdlan, a bacterial polysaccharide widely used as food additive, has the potential to alter the composition of the microbiota and improve the gut-brain axis. However, the effects of curdlan against HF diet-induced neuroinflammation and cognitive decline have not been investigated. We aimed to evaluate the neuroprotective effect and mechanism of dietary curdlan supplementation against the obesity-associated cognitive decline observed in mice fed a HF diet. C57Bl/6J male mice were fed with either a control, HF, or HF with curdlan supplementation diets for 7 days (acute) or 15 weeks (chronic). We found that acute curdlan supplementation prevented the gut microbial composition shift induced by HF diet. Chronic curdlan supplementation prevented cognitive declines induced by HF diet. In addition, curdlan protected against the HF diet-induced abnormities in colonic permeability, hyperendotoxemia, and colonic inflammation. Furthermore, in the prefrontal cortex (PFC) and hippocampus, curdlan mitigated microgliosis, neuroinflammation, and synaptic impairments induced by a HF diet. Thus, curdlan-as a food additive and prebiotic-can prevent cognitive deficits induced by HF diet via the colon-brain axis