TIM: Teaching Large Language Models to Translate with Comparison

Open-sourced large language models (LLMs) have demonstrated remarkable efficacy in various tasks with instruction tuning. However, these models can sometimes struggle with tasks that require more specialized knowledge such as translation. One possible reason for such deficiency is that instruction tuning aims to generate fluent and coherent text that continues from a given instruction without being constrained by any task-specific requirements. Moreover, it can be more challenging for tuning smaller LLMs with lower-quality training data. To address this issue, we propose a novel framework using examples in comparison to teach LLMs to learn translation. Our approach involves presenting the model with examples of correct and incorrect translations and using a preference loss to guide the model's learning. We evaluate our method on WMT2022 test sets and show that it outperforms existing methods. Our findings offer a new perspective on fine-tuning LLMs for translation tasks and provide a promising solution for generating high-quality translations. Please refer to Github for more details: https://github.com/lemon0830/TIM

Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding

Contrastive learning has become a new paradigm for unsupervised sentence embeddings. Previous studies focus on instance-wise contrastive learning, attempting to construct positive pairs with textual data augmentation. In this paper, we propose a novel Contrastive learning method with Prompt-derived Virtual semantic Prototypes (ConPVP). Specifically, with the help of prompts, we construct virtual semantic prototypes to each instance, and derive negative prototypes by using the negative form of the prompts. Using a prototypical contrastive loss, we enforce the anchor sentence embedding to be close to its corresponding semantic prototypes, and far apart from the negative prototypes as well as the prototypes of other sentences. Extensive experimental results on semantic textual similarity, transfer, and clustering tasks demonstrate the effectiveness of our proposed model compared to strong baselines. Code is available at https://github.com/lemon0830/promptCSE.Comment: Findings of EMNLP 202

19.2% Efficient InP Heterojunction Solar Cell with Electron-Selective TiO2 Contact.

We demonstrate an InP heterojunction solar cell employing an ultrathin layer (∼10 nm) of amorphous TiO2 deposited at 120 °C by atomic layer deposition as the transparent electron-selective contact. The TiO2 film selectively extracts minority electrons from the conduction band of p-type InP while blocking the majority holes due to the large valence band offset, enabling a high maximum open-circuit voltage of 785 mV. A hydrogen plasma treatment of the InP surface drastically improves the long-wavelength response of the device, resulting in a high short-circuit current density of 30.5 mA/cm2 and a high power conversion efficiency of 19.2%

Soft Language Clustering for Multilingual Model Pre-training

Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language has distant typology from source languages or when pre-training data is limited in size. In this paper, we propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods. On the tasks of XTREME including text classification, sequence labeling, question answering, and sentence retrieval, both base- and large-size language models pre-trained with our proposed method exhibit consistent performance improvement. Furthermore, it provides substantial advantages for low-resource languages in unsupervised sentence retrieval and for target languages that differ greatly from the source language in cross-lingual transfer

Periodic Mechanical Stress Induces Extracellular Matrix Expression and Migration of Rat Nucleus Pulposus Cells Through Src-GIT1-ERK1/2 Signaling Pathway

Background/Aims: Periodic mechanical stress has been shown to promote extracellular matrix (ECM) synthesis and cell migration of nucleus pulposus (NP) cells, however, the mechanisms need to be fully elucidated. The present study aimed to investigate the signal transduction pathway in the regulation of NP cells under periodic mechanical stress. Methods: Primary rat NP cells were isolated and seeded on glass slides, and then treated in our self-developed periodic stress field culture system. To further explore the mechanisms, data were analyzed by scratch-healing assay, quantitative reverse transcription polymerase chain reaction (RT-qPCR) analysis, western blotting, and co-immunoprecipitation assay. Results: Under periodic mechanical stress, the mRNA expression of ECM collagen 2A1 (Col2A1) and aggrecan, and migration of NP cells were significantly increased (P < 0.05 for each), associating with increases in the phosphorylation of Src, GIT1, and ERK1/2 (P < 0.05 for each). Pretreatment with the Src inhibitor PP2 reduced periodic mechanical stress-induced ECM synthesis and cell migration of NP cells (P < 0.05 for each), while the phosphorylation of GIT1 and ERK1/2 were inhibited. ECM synthesis, cell migration, and phosphorylation of ERK1/2 were inhibited after pretreatment with the small interfering RNA for GIT1 in NP cells under periodic mechanical stress (P < 0.05 for each), whereas the phosphorylation of Src was not affected. Pretreatment with the ERK1/2 inhibitor PD98059 reduced periodic mechanical stress-induced ECM synthesis and cell migration of NP cells (P < 0.05 for each). Co-immunoprecipitation assay showed that there was a direct interaction between Src and GIT1 and between GIT1 and ERK1/2. Conclusion: In conclusion, periodic mechanical stress induced ECM expression and migration of NP cells via Src-GIT1-ERK1/2 signaling pathway, playing an important role in regulation of NP cells

Isolation, Structural Characterization and Macrophage Activation Activity of an Acidic Polysaccharide from Raspberry Pulp

The discovery of safe and effective plant polysaccharides with immunomodulatory effects has become a research hotspot. Raspberry is an essential commercial fruit and is widely distributed, cultivated, and consumed worldwide. In the present study, a homogeneous acidic polysaccharide (RPP-2a), with a weight-average molecular weight (Mw) of 55582 Da, was isolated from the pulp of raspberries through DEAE-Sepharose Fast Flow and Sephadex G-200 chromatography. RPP-2a consisted of rhamnose, arabinose, galactose, glucose, xylose, galacturonic acid and glucuronic acid, with a molar ratio of 15.4:9.6:7.6:3.2:9.1:54.3:0.8. The results of Fourier transform infrared spectroscopy (FT-IR), gas chromatography-mass spectrometer (GC-MS), 1D-, and 2D-nuclear magnetic resonance (NMR) analyses suggested that the backbone of RPP-2a was primarily composed of →2)-α-L-Rhap-(1→, →2,4)-α-L-Rhap-(1→, →4)-α-D-GalAp-(1→, and →3,4)-α-D-Glcp-(1→ sugar moieties, with side chains of α-L-Araf-(1→, α-L-Arap-(1→, and β-D-Galp-(1→3)-β-D-Galp-(1→ residues linked to the O-4 band of rhamnose and O-3 band of glucose residues. Furthermore, RPP-2a exhibited significant macrophage activation activity by increasing the production of nitric oxide (NO), tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), interleukin-1β (IL-1β), and the expression of inducible nitric oxide synthase (iNOS) and cytokines at the transcriptional level in RAW264.7 cells. Overall, the results indicate that RPP-2a can be utilized as a potential natural immune-enhancing agent

Research on defect detection of bottle cap interior based on low-angle and large divergence angle vision system.

During the machine vision inspection of the inner section of bottle caps within pharmaceutical packaging, the unique conca bottom and convex side walls often create obstructions to the illumination. Consequently, this results in challenges such as irregular background and diminished feature contrast in the image, ultimately leading to the misidentification of defects. As a solution, a vision system characterized by a Low-Angle and Large Divergence Angle (LALDA) is presented in this paper. Using the large divergence angle of LED, combined with low-angle illumination, a uniform image of the side wall region with bright-field characteristics and a uniform image of inner circle region at the bottom with dark-field characteristics are obtained, thus solving the problems of light being obscured and brightness overexposure of the background. Based on the imaging characteristics of LALDA, a multi-channel segmentation (MCS) algorithm is designed. The HSV color space has been transformed, and the image is automatically segmented into multiple sub-regions by mutual calculation of different channels. Further, image homogenization and enhancement are used to eliminate fluctuations in the background and to enhance the contrast of defects. In addition, a variety of defect extraction methods are designed based on the imaging characteristics of different sub-regions, which can avoid the problem of over-segmentation in detection. In this paper, the LALDA is applied to the defect detection inside the cap of capsule medicine bottle, the detection speed is better than 400 pcs/min and the detection accuracy is better than 95%, which can meet the actual production line capacity and detection requirements