arXiv subjects

Lei Shu

Publications and source records attributed to Lei Shu.

At least 37 records · Page 2Linked to original sources

arXiv2024

Evidence of spin density waves in La$_3$Ni$_2$O$_{7-δ}$

The recently discovered superconductivity with critical temperature $T_c$ up to 80 K in the double-layer Nickelate La$_3$Ni$_2$O$_{7-δ}$ under pressure has drawn great attention. Here we report the positive muon spin relaxation ($μ^+$SR) study of polycrystalline La$_3$Ni$_2$O$_{6.92}$ under ambient pressure. Zero-field $μ^+$SR experiments reveal the existence of magnetic order in La$_3$Ni$_2$O$_{6.92}$ with $T_{N}=154\ \rm{K}$. The weak transverse field $μ^+$SR measurements confirms the bulk nature of magnetism. In addition, a small quantity of oxygen deficiencies can greatly broaden the internal magnetic field distribution sensed by muons.

cond-mat.str-el↗

arXiv2024

Two-Dimensional Phase-Fluctuating Superconductivity in Bulk-Crystalline NdO$_{0.5}$F$_{0.5}$BiS$_2$

We present a combined growth and transport study of superconducting single-crystalline NdO$_{0.5}$F$_{0.5}$BiS$_2$. Evidence of two-dimensional superconductivity with significant phase fluctuations of preformed Cooper pairs preceding the superconducting transition is reported. This result is based on three key observations. (1) The resistive superconducting transition temperature $T_c$ (defined by resistivity $ρ\rightarrow 0$) increases with increasing disorder. (2) As $T\rightarrow T_c$, the conductivity diverges significantly faster than what is expected from Gaussian fluctuations in two and three dimensions. (3) Non-Ohmic resistance behavior is observed in the superconducting state. Altogether, our observations are consistent with a temperature regime of phase-fluctuating superconductivity. The crystal structure with magnetic ordering tendencies in the NdO$_{0.5}$F$_{0.5}$ layers and (super)conductivity in the BiS$_2$ layers is likely responsible for the two-dimensional phase fluctuations. As such, NdO$_{0.5}$F$_{0.5}$BiS$_2$ falls into the class of unconventional ``laminar" bulk superconductors that include cuprate materials and 4Hb-TaS$_2$.

cond-mat.supr-con↗

arXiv2024

Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation

Reinforcement learning (RL) can align language models with non-differentiable reward signals, such as human preferences. However, a major challenge arises from the sparsity of these reward signals - typically, there is only a single reward for an entire output. This sparsity of rewards can lead to inefficient and unstable learning. To address this challenge, our paper introduces an novel framework that utilizes the critique capability of Large Language Models (LLMs) to produce intermediate-step rewards during RL training. Our method involves coupling a policy model with a critic language model, which is responsible for providing comprehensive feedback of each part of the output. This feedback is then translated into token or span-level rewards that can be used to guide the RL training process. We investigate this approach under two different settings: one where the policy model is smaller and is paired with a more powerful critic model, and another where a single language model fulfills both roles. We assess our approach on three text generation tasks: sentiment control, language model detoxification, and summarization. Experimental results show that incorporating artificial intrinsic rewards significantly improve both sample efficiency and the overall performance of the policy model, supported by both automatic and human evaluation.

cs.CL↗

arXiv2024

Multi-condensate lengths with degenerate excitation gaps in BaNi$_2$As$_2$ revealed by muon spin relaxation study

The recently discovered (Ba,Sr)Ni$_2$As$_2$ family provides an ideal platform for investigating the interaction between electronic nematicity and superconductivity. Here we report the muon spin relaxation ($μ$SR) measurements on BaNi$_2$As$_2$. Transverse-field $μ$SR experiments indicate that the temperature dependence of superfluid density is best fitted with a single-band $s$-wave model. On the other hand, the magnetic penetration depth $λ$ shows magnetic field dependence, which contradicts with the single-band fully-gapped scenario. Zero-field $μ$SR experiments indicate the absence of spontaneous magnetic field in the superconducting state, showing the preservation of time-reversal symmetry in the superconducting state. Our $μ$SR experiments suggest that BaNi$_2$As$_2$ is a fully-gapped multiband superconductor. The superconducting gap amplitudes of each band are nearly the same while different bands exhibit different coherence lengths. The present work helps to elucidate the controversial superconducting property of this parent compound, paving the way for further research on doping the system with Sr to enhance superconductivity.

cond-mat.supr-con↗

arXiv2023

RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting

Large Language Models (LLMs) have demonstrated impressive capabilities in creative tasks such as storytelling and E-mail generation. However, as LLMs are primarily trained on final text results rather than intermediate revisions, it might be challenging for them to perform text rewriting tasks. Most studies in the rewriting tasks focus on a particular transformation type within the boundaries of single sentences. In this work, we develop new strategies for instruction tuning and reinforcement learning to better align LLMs for cross-sentence rewriting tasks using diverse wording and structures expressed through natural languages including 1) generating rewriting instruction data from Wiki edits and public corpus through instruction generation and chain-of-thought prompting; 2) collecting comparison data for reward model training through a new ranking function. To facilitate this research, we introduce OpenRewriteEval, a novel benchmark covers a wide variety of rewriting types expressed through natural language instructions. Our results show significant improvements over a variety of baselines. The public repository is available on GitHub under Google Research (https://github.com/google-research/google-research/tree/master/rewritelm).

cs.CL↗

arXiv2023

Spatially Adaptive Cloth Regression with Implicit Neural Representations

The accurate representation of fine-detailed cloth wrinkles poses significant challenges in computer graphics. The inherently non-uniform structure of cloth wrinkles mandates the employment of intricate discretization strategies, which are frequently characterized by high computational demands and complex methodologies. Addressing this, the research introduced in this paper elucidates a novel anisotropic cloth regression technique that capitalizes on the potential of implicit neural representations of surfaces. Our first core contribution is an innovative mesh-free sampling approach, crafted to reduce the reliance on traditional mesh structures, thereby offering greater flexibility and accuracy in capturing fine cloth details. Our second contribution is a novel adversarial training scheme, which is designed meticulously to strike a harmonious balance between the sampling and simulation objectives. The adversarial approach ensures that the wrinkles are represented with high fidelity, while also maintaining computational efficiency. Our results showcase through various cloth-object interaction scenarios that our method, given the same memory constraints, consistently surpasses traditional discrete representations, particularly when modelling highly-detailed localized wrinkles.

cs.CV↗

arXiv2023

SiRA: Sparse Mixture of Low Rank Adaptation

Parameter Efficient Tuning has been an prominent approach to adapt the Large Language Model to downstream tasks. Most previous works considers adding the dense trainable parameters, where all parameters are used to adapt certain task. We found this less effective empirically using the example of LoRA that introducing more trainable parameters does not help. Motivated by this we investigate the importance of leveraging "sparse" computation and propose SiRA: sparse mixture of low rank adaption. SiRA leverages the Sparse Mixture of Expert(SMoE) to boost the performance of LoRA. Specifically it enforces the top $k$ experts routing with a capacity limit restricting the maximum number of tokens each expert can process. We propose a novel and simple expert dropout on top of gating network to reduce the over-fitting issue. Through extensive experiments, we verify SiRA performs better than LoRA and other mixture of expert approaches across different single tasks and multitask settings.

cs.CL↗

arXiv2023

Critique Ability of Large Language Models

Critical thinking is essential for rational decision-making and problem-solving. This skill hinges on the ability to provide precise and reasoned critiques and is a hallmark of human intelligence. In the era of large language models (LLMs), this study explores the ability of LLMs to deliver accurate critiques across various tasks. We are interested in this topic as a capable critic model could not only serve as a reliable evaluator, but also as a source of supervised signals for model tuning. Particularly, if a model can self-critique, it has the potential for autonomous self-improvement. To examine this, we introduce a unified evaluation framework for assessing the critique abilities of LLMs. We develop a benchmark called CriticBench, which comprises 3K high-quality natural language queries and corresponding model responses; and annotate the correctness of these responses. The benchmark cover tasks such as math problem-solving, code completion, and question answering. We evaluate multiple LLMs on the collected dataset and our analysis reveals several noteworthy insights: (1) Critique is generally challenging for most LLMs, and this capability often emerges only when models are sufficiently large. (2) In particular, self-critique is especially difficult. Even top-performing LLMs struggle to achieve satisfactory performance. (3) Models tend to have lower critique accuracy on problems where they are most uncertain. To this end, we introduce a simple yet effective baseline named self-check, which leverages self-critique to improve task performance for various models. We hope this study serves as an initial exploration into understanding the critique abilities of LLMs, and aims to inform future research, including the development of more proficient critic models and the application of critiques across diverse tasks.

cs.LG↗

arXiv2023

Muon Spin Relaxation Study of frustrated Tm$_3$Sb$_3$Mg$_2$O$_{14}$ with kagomé lattice

The structure and magnetic properties of rare-earth ions Tm$^{3+}$ kagomé lattice Tm$_3$Sb$_3$Mg$_2$O$_{14}$ are studied by X-ray diffraction, magnetic susceptibility and muon spin relaxation ($μ$SR) experiments. The existence of a small amount of Tm/Mg site-mixing disorder is revealed. DC magnetic susceptibility measurement shows that Tm$^{3+}$ magnetic moments are antiferromagnetically correlated with a negative Curie-Weiss temperature of -26.3 K. Neither long-range magnetic order nor spin-glass transition is observed by DC and AC magnetic susceptibility, and confirmed by $μ$SR experiment down to 0.1 K. However, the emergence of short-range magnetic order is indicated by the zero-field $μ$SR experiments, and the absence of spin dynamics at low temperatures is evidenced by the longitudinal-field $μ$SR technique. Compared with the results of Tm$_3$Sb$_3$Zn$_2$O$_{14}$, another Tm-based kagomé lattice with much more site-mixing disorder, the gapless spin liquid like behaviors in Tm$_3$Sb$_3$Zn$_2$O$_{14}$ can be induced by disorder effect. Samples with perfect geometrical frustration are in urgent demand to establish whether QSL exits in this kind of materials with rare-earth kagomé lattice.

cond-mat.str-el↗

arXiv2023

Superconducting Properties of La$_2$(Cu$_{1-x}$Ni_x)$_5$As$_3$O$_2$: A $\rm μ$SR Study

We report the results of muon spin rotation and relaxation ($\rm μ$SR) measurements on the recently discovered layered Cu-based superconducting material La$_{2}($Cu$_{1-x}$Ni$_{x}$)$_{5}$As$_{3}$O$_{2}$ ($x =$ 0.40, 0.45). Transverse-field $\rm μ$SR experiments on both samples show that the temperature dependence of superfluid density is best described by a two-band model. The absolute values of zero-temperature magnetic penetration depth $λ_{\rm ab}(0)$ were found to be 427(1.7) nm and 422(1.5) nm for $x =$ 0.40 and 0.45, respectively. Both compounds are located between the unconventional and the standard BCS superconductors in the Uemura plot. No evidence of time-reversal symmetry (TRS) breaking in the superconducting state is suggested by zero-field $\rm μ$SR measurements.

cond-mat.supr-con↗

arXiv2023

Towards an On-device Agent for Text Rewriting

Large Language Models (LLMs) have demonstrated impressive capabilities for text rewriting. Nonetheless, the large sizes of these models make them impractical for on-device inference, which would otherwise allow for enhanced privacy and economical inference. Creating a smaller yet potent language model for text rewriting presents a formidable challenge because it requires balancing the need for a small size with the need to retain the emergent capabilities of the LLM, that requires costly data collection. To address the above challenge, we introduce a new instruction tuning approach for building a mobile-centric text rewriting model. Our strategies enable the generation of high quality training data without any human labeling. In addition, we propose a heuristic reinforcement learning framework which substantially enhances performance without requiring preference data. To further bridge the performance gap with the larger server-side model, we propose an effective approach that combines the mobile rewrite agent with the server model using a cascade. To tailor the text rewriting tasks to mobile scenarios, we introduce MessageRewriteEval, a benchmark that focuses on text rewriting for messages through natural language instructions. Through empirical experiments, we demonstrate that our on-device model surpasses the current state-of-the-art LLMs in text rewriting while maintaining a significantly reduced model size. Notably, we show that our proposed cascading approach improves model performance.

cs.CL↗

arXiv2023

Adapting a Language Model While Preserving its General Knowledge

Domain-adaptive pre-training (or DA-training for short), also known as post-training, aims to train a pre-trained general-purpose language model (LM) using an unlabeled corpus of a particular domain to adapt the LM so that end-tasks in the domain can give improved performances. However, existing DA-training methods are in some sense blind as they do not explicitly identify what knowledge in the LM should be preserved and what should be changed by the domain corpus. This paper shows that the existing methods are suboptimal and proposes a novel method to perform a more informed adaptation of the knowledge in the LM by (1) soft-masking the attention heads based on their importance to best preserve the general knowledge in the LM and (2) contrasting the representations of the general and the full (both general and domain knowledge) to learn an integrated representation with both general and domain-specific knowledge. Experimental results will demonstrate the effectiveness of the proposed approach.

cs.CL↗

arXiv2022

Continual Training of Language Models for Few-Shot Learning

Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications. Adapting or posttraining an LM using an unlabeled domain corpus can produce even better performance for end-tasks in the domain. This paper proposes the problem of continually extending an LM by incrementally post-train the LM with a sequence of unlabeled domain corpora to expand its knowledge without forgetting its previous skills. The goal is to improve the few-shot end-task learning in these domains. The resulting system is called CPT (Continual PostTraining), which to our knowledge, is the first continual post-training system. Experimental results verify its effectiveness.

cs.CL↗

arXiv2022

Measuring and Reducing Model Update Regression in Structured Prediction for NLP

Recent advance in deep learning has led to the rapid adoption of machine learning-based NLP models in a wide range of applications. Despite the continuous gain in accuracy, backward compatibility is also an important aspect for industrial applications, yet it received little research attention. Backward compatibility requires that the new model does not regress on cases that were correctly handled by its predecessor. This work studies model update regression in structured prediction tasks. We choose syntactic dependency parsing and conversational semantic parsing as representative examples of structured prediction tasks in NLP. First, we measure and analyze model update regression in different model update settings. Next, we explore and benchmark existing techniques for reducing model update regression including model ensemble and knowledge distillation. We further propose a simple and effective method, Backward-Congruent Re-ranking (BCR), by taking into account the characteristics of structured prediction. Experiments show that BCR can better mitigate model update regression than model ensemble and knowledge distillation approaches.

cs.CL↗

arXiv2022

Open-set Recognition via Augmentation-based Similarity Learning

The primary assumption of conventional supervised learning or classification is that the test samples are drawn from the same distribution as the training samples, which is called closed set learning or classification. In many practical scenarios, this is not the case because there are unknowns or unseen class samples in the test data, which is called the open set scenario, and the unknowns need to be detected. This problem is referred to as the open set recognition problem and is important in safety-critical applications. We propose to detect unknowns (or unseen class samples) through learning pairwise similarities. The proposed method works in two steps. It first learns a closed set classifier using the seen classes that have appeared in training and then learns how to compare seen classes with pseudo-unseen (automatically generated unseen class samples). The pseudo-unseen generation is carried out by performing distribution shifting augmentations on the seen or training samples. We call our method OPG (Open set recognition based on Pseudo unseen data Generation). The experimental evaluation shows that the learned similarity-based features can successfully distinguish seen from unseen in benchmark datasets for open set recognition.

cs.CV↗

arXiv2022

Intrinsic new properties of a quantum spin liquid

Quantum fluctuations are expected to lead to highly entangled spin-liquid states in certain two-dimensional spin-1/2 compounds. We have synthesized and measured thermodynamic properties and muon spin relaxation rates in the copper-based two-dimensional triangular-lattice spin liquids Lu$_3$Cu$_2$Sb$_3$O$_{14}$ and Lu$_3$CuZnSb$_3$O$_{14}$. The former is the least disordered of this kind discovered to date. Magnetic entropy generation at high temperatures has been ruled out after carefully correcting for the lattice specific heat. Surprisingly, roughly half of the magnetic entropy is missing down to temperatures of O(10$^{-3}$) the exchange energy, independent of magnetic field up to $gμ_B H \gtrsim k_BΘ_W$, where $Θ_W$ is the Weiss temperature. The magnetic specific heat divided by temperature $C_M(T)/T$ and muon spin relaxation rate $λ(T)$ are both temperature-independent at low temperatures, followed by logarithmic decreases with increasing temperature. This behavior can be simply characterized by scale-invariant time-dependent fluctuations with a single parameter. Since no cooperative effects due to impurities are observed, the measured properties are intrinsic. They are evidence that in Lu$_3$Cu$_2$Sb$_3$O$_{14}$ massive quantum fluctuations lead to either a gigantic specific heat peak from singlet excitations at very low temperatures or, perhaps less likely, an extensively degenerate possibly topological singlet ground state.

cond-mat.str-el↗

arXiv2022

Three-dimensional Sandglass Magnet with Non-Kramers ions

Magnetic susceptibility, specific heat, and muon spin relaxation ($μ$SR) measurements have been performed on a newly synthesized three-dimensional sandglass-type lattice Tm$_3$SbO$_7$, where two inequivalent sets of non-Kramers Tm$^{3+}$ ions (Tm$^{3+}_1$ and Tm$^{3+}_2)$ show crystal electrical field effect at different temperature ranges. The existence of an ordered or a glassy state down to 0.1~K in zero field is excluded. The low-energy properties of Tm$_3$SbO$_7$ are dominated by the lowest non-Kramers quasi-doublet of $\rm Tm^{3+}_1$, and the energy splitting is regarded as an intrinsic transverse field. Therefore, the low-temperature paramagnetic phenomenon in Tm$_3$SbO$_7$ is explained by a transverse field Ising model, which is supported by the quantitative simulation of specific heat data. In addition, the perturbation from Tm$^{3+}_2$ may play an important role in accounting for the low temperature spin dynamics behavior observed by $μ$SR.

cond-mat.str-el↗

arXiv2022

TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning

Masked language models (MLMs) such as BERT and RoBERTa have revolutionized the field of Natural Language Understanding in the past few years. However, existing pre-trained MLMs often output an anisotropic distribution of token representations that occupies a narrow subset of the entire representation space. Such token representations are not ideal, especially for tasks that demand discriminative semantic meanings of distinct tokens. In this work, we propose TaCL (Token-aware Contrastive Learning), a novel continual pre-training approach that encourages BERT to learn an isotropic and discriminative distribution of token representations. TaCL is fully unsupervised and requires no additional data. We extensively test our approach on a wide range of English and Chinese benchmarks. The results show that TaCL brings consistent and notable improvements over the original BERT model. Furthermore, we conduct detailed analysis to reveal the merits and inner-workings of our approach.

cs.CL↗