Search

Broad textual understanding and in-context learning require language models that utilize full document contexts. Due to the implementation challenges associated with directly training long-context models, many methods have been proposed for extending models to handle long contexts. However, owing to differences in data and model classes, it has been challenging to compare these approaches, leading to uncertainty as to how to evaluate long-context performance and whether it differs from standard evaluation. We implement a controlled protocol for extension methods with a standardized evaluation, utilizing consistent base models and extension data. Our study yields several insights into long-context behavior. First, we reaffirm the critical role of perplexity as a general-purpose performance indicator even in longer-context tasks. Second, we find that current approximate attention methods systematically underperform across long-context tasks. Finally, we confirm that exact fine-tuning based methods are generally effective within the range of their extension, whereas extrapolation remains challenging. All codebases, models, and checkpoints will be made available open-source, promoting transparency and facilitating further research in this critical area of AI development.

The current bottleneck in continuous sign language recognition (CSLR) research lies in the fact that most publicly available datasets are limited to laboratory environments or television program recordings, resulting in a single background environment with uniform lighting, which significantly deviates from the diversity and complexity found in real-life scenarios. To address this challenge, we have constructed a new, large-scale dataset for Chinese continuous sign language (CSL) based on complex environments, termed the complex environment - chinese sign language dataset (CE-CSL). This dataset encompasses 5,988 continuous CSL video clips collected from daily life scenes, featuring more than 70 different complex backgrounds to ensure representativeness and generalization capability. To tackle the impact of complex backgrounds on CSLR performance, we propose a time-frequency network (TFNet) model for continuous sign language recognition. This model extracts frame-level features and then utilizes both temporal and spectral information to separately derive sequence features before fusion, aiming to achieve efficient and accurate CSLR. Experimental results demonstrate that our approach achieves significant performance improvements on the CE-CSL, validating its effectiveness under complex background conditions. Additionally, our proposed method has also yielded highly competitive results when applied to three publicly available CSL datasets., Comment: 11 pages, 3 figures

Large Language Models (LLMs) demonstrate remarkable translation capabilities in high-resource language tasks, yet their performance in low-resource languages is hindered by insufficient multilingual data during pre-training. To address this, we conduct extensive multilingual continual pre-training on the LLaMA series models, enabling translation support across more than 100 languages. Through a comprehensive analysis of training strategies, such as vocabulary expansion and data augmentation, we develop LLaMAX. Remarkably, without sacrificing its generalization ability, LLaMAX achieves significantly higher translation performance compared to existing open-source LLMs (by more than 10 spBLEU points) and performs on-par with specialized translation model (M2M-100-12B) on the Flores-101 benchmark. Extensive experiments indicate that LLaMAX can serve as a robust multilingual foundation model. The code \footnote{\url{https://github.com/CONE-MT/LLaMAX/.}} and the models \footnote{\url{https://huggingface.co/LLaMAX/.}} are publicly available., Comment: EMNLP 2024 findings

Reasoning capabilities are crucial for Large Language Models (LLMs), yet a notable gap exists between English and non-English languages. To bridge this disparity, some works fine-tune LLMs to relearn reasoning capabilities in non-English languages, while others replace non-English inputs with an external model's outputs such as English translation text to circumvent the challenge of LLM understanding non-English. Unfortunately, these methods often underutilize the built-in skilled reasoning and useful language understanding capabilities of LLMs. In order to better utilize the minds of reasoning and language understanding in LLMs, we propose a new method, namely MindMerger, which merges LLMs with the external language understanding capabilities from multilingual models to boost the multilingual reasoning performance. Furthermore, a two-step training scheme is introduced to first train to embeded the external capabilities into LLMs and then train the collaborative utilization of the external capabilities and the built-in capabilities in LLMs. Experiments on three multilingual reasoning datasets and a language understanding dataset demonstrate that MindMerger consistently outperforms all baselines, especially in low-resource languages. Without updating the parameters of LLMs, the average accuracy improved by 6.7% and 8.0% across all languages and low-resource languages on the MGSM dataset, respectively.

Bridging the significant gap between large language model's English and non-English performance presents a great challenge. While some previous studies attempt to mitigate this gap with translated training data, the recently proposed question alignment framework leverages the model's English expertise to improve multilingual performance with minimum usage of expensive, error-prone translation. In this paper, we explore how broadly this method can be applied by examining its effects in reasoning with and without chain-of-thought, as well as with program-of-thought. We also explore applying this framework to extremely large language models in an efficient manner, such as through proxy-tuning. Experiment results on multilingual reasoning benchmarks mGSM, mSVAMP, xCSQA and xNLI demonstrate that we can extend question alignment framework to boost multilingual performance across diverse reasoning scenarios, model families, and sizes. For instance, when applied to the LLaMA2 models, it brings an average accuracy improvements of 12.2% on mGSM even with the 70B model. To understand the mechanism of its success, we analyze representation space, generated response and data scales, and reveal how question translation training strengthens language alignment within LLMs and shapes their working patterns.

Neural Code Intelligence -- leveraging deep learning to understand, generate, and optimize code -- holds immense potential for transformative impacts on the whole society. Bridging the gap between Natural Language and Programming Language, this domain has drawn significant attention from researchers in both research communities over the past few years. This survey presents a systematic and chronological review of the advancements in code intelligence, encompassing over 50 representative models and their variants, more than 20 categories of tasks, and an extensive coverage of over 680 related works. We follow the historical progression to trace the paradigm shifts across different research phases (e.g., from modeling code with recurrent neural networks to the era of Large Language Models). Concurrently, we highlight the major technical transitions in models, tasks, and evaluations spanning through different stages. For applications, we also observe a co-evolving shift. It spans from initial endeavors to tackling specific scenarios, through exploring a diverse array of tasks during its rapid expansion, to currently focusing on tackling increasingly complex and varied real-world challenges. Building on our examination of the developmental trajectories, we further investigate the emerging synergies between code intelligence and broader machine intelligence, uncovering new cross-domain opportunities and illustrating the substantial influence of code intelligence across various domains. Finally, we delve into both the opportunities and challenges associated with this field, alongside elucidating our insights on the most promising research directions. An ongoing, dynamically updated project and resources associated with this survey have been released at https://github.com/QiushiSun/Awesome-Code-Intelligence., Comment: 65 pages, 6 figures, 10 tables, 701 references

Changes in facial expression, head movement, body movement and gesture movement are remarkable cues in sign language recognition, and most of the current continuous sign language recognition(CSLR) research methods mainly focus on static images in video sequences at the frame-level feature extraction stage, while ignoring the dynamic changes in the images. In this paper, we propose a novel motor attention mechanism to capture the distorted changes in local motion regions during sign language expression, and obtain a dynamic representation of image changes. And for the first time, we apply the self-distillation method to frame-level feature extraction for continuous sign language, which improves the feature expression without increasing the computational resources by self-distilling the features of adjacent stages and using the higher-order features as teachers to guide the lower-order features. The combination of the two constitutes our proposed holistic model of CSLR Based on motor attention mechanism and frame-level Self-Distillation (MAM-FSD), which improves the inference ability and robustness of the model. We conduct experiments on three publicly available datasets, and the experimental results show that our proposed method can effectively extract the sign language motion information in videos, improve the accuracy of CSLR and reach the state-of-the-art level., Comment: 10 pages, 7 figures

The lottery ticket hypothesis posits the existence of ``winning tickets'' within a randomly initialized neural network. Do winning tickets exist for LLMs in fine-tuning scenarios? How can we find such winning tickets? In this paper, we propose KS-Lottery, a method to identify a small subset of LLM parameters highly effective in multilingual fine-tuning. Our key idea is to use Kolmogorov-Smirnov Test to analyze the distribution shift of parameters before and after fine-tuning. We further theoretically prove that KS-Lottery can find the certified winning tickets in the embedding layer, fine-tuning on the found parameters is guaranteed to perform as well as full fine-tuning. Comparing KS-Lottery with other parameter-efficient tuning algorithms on translation tasks, the experimental results show that KS-Lottery finds a much smaller set of parameters for fine-tuning while achieving the comparable performance as full fine-tuning LLM. Surprisingly, we find that fine-tuning 18 tokens' embedding of LLaMA suffices to reach the fine-tuning translation performance~\footnote{https://github.com/CONE-MT/KS-Lottery.}.

Large language models show compelling performance on reasoning tasks but they tend to perform much worse in languages other than English. This is unsurprising given that their training data largely consists of English text and instructions. A typical solution is to translate instruction data into all languages of interest, and then train on the resulting multilingual data, which is called translate-training. This approach not only incurs high cost, but also results in poorly translated data due to the non-standard formatting of mathematical chain-of-thought. In this paper, we explore the benefits of question alignment, where we train the model to translate reasoning questions into English by finetuning on X-English parallel question data. In this way we perform targeted, in-domain language alignment which makes best use of English instruction data to unlock the LLMs' multilingual reasoning abilities. Experimental results on LLaMA2-13B show that question alignment leads to consistent improvements over the translate-training approach: an average improvement of 11.3% and 16.1% accuracy across ten languages on the MGSM and MSVAMP multilingual reasoning benchmarks. The project will be available at: https://github.com/NJUNLP/QAlign., Comment: Accepted to Findings of ACL 2024

Although Large Language Models (LLMs) demonstrate remarkable ability in processing and generating human-like text, they do have limitations when it comes to comprehending and expressing world knowledge that extends beyond the boundaries of natural language(e.g., chemical molecular formula). Injecting a collection of symbolic data directly into the training of LLMs can be problematic, as it disregards the synergies among different symbolic families and overlooks the need for a balanced mixture of natural and symbolic data. In this work, we tackle these challenges from both a data and framework perspective and introduce Symbol-LLM series models. First, we curated a data collection consisting of 34 tasks and incorporating approximately 20 distinct symbolic families, intending to capture the interrelations and foster synergies between symbols. Then, a two-stage tuning framework succeeds in injecting symbolic knowledge without loss of the generality ability. Extensive experiments on both symbol- and NL-centric tasks demonstrate the balanced and superior performances of Symbol-LLM series models. The project page is https://xufangzhi.github.io/symbol-llm-page/., Comment: 23 pages, 13 figures

Large Language Models (LLMs), often show strong performance on English tasks, while exhibiting limitations on other languages. What is an LLM's multilingual capability when it is trained only on certain languages? The underlying mechanism remains unclear. This study endeavors to examine the multilingual capability of LLMs from the vocabulary sharing perspective by conducting an exhaustive analysis across 101 languages. Through the investigation of the performance gap before and after embedding fine-tuning, we discovered four distinct quadrants. By delving into each quadrant we provide actionable and efficient guidelines for tuning these languages. Extensive experiments reveal that existing LLMs possess multilingual capabilities that surpass our expectations, and we can significantly improve the multilingual performance of LLMs based on these attributes of each quadrant~\footnote{\url{https://github.com/CONE-MT/Vocabulary-Sharing-Facilitates-Multilingualism}.}., Comment: ACL-2024 Findings

The roles of the fine-structure intramanifold resonances to the Zeeman shifts caused by the blackbody radiation (BBRz shifts) in the optical clock transitions are analyzed. The clock frequency measurement in the $^1S_0-^3P_0$ clock transition of the singly charged aluminium ion (Al$^+$) has already been reached the $10^{-19}$ level at which the BBRz effect can be significant in determining the uncertainty. In view of this, we probe first the BBRz shift in this transition rigorously and demonstrate the importance of the contributions from the intramanifold resonances explicitly. To carry out the analysis, we determine the dynamic magnetic dipole (M1) polarizabilities of the clock states over a wide range of angular frequencies by employing two variants of relativistic many-body methods. This showed the BBRz shift is highly suppressed due to blue-detuning of the BBR spectrum to the $^3P_0-^3P_1$ fine-structure intramanifold resonance in Al$^+$ and it fails to follow the usually assumed static M1 polarizability limit in the estimation of the BBRz shift. The resonance also leads to a reversal behavior of the temperature dependence and a cancellation in the shift. After learning this behavior, we extended our analyses to other optical clocks and found that these shifts are of the order of micro-hertz leading to fractional shifts in the clock transitions at the $10^{-20}$ level or below.

Existing large language models show disparate capability across different languages, due to the imbalance in the training data. Their performances on English tasks are often stronger than on tasks of other languages. In this paper, we empower pre-trained LLMs on non-English languages by building semantic alignment across languages. We start from targeting individual languages by performing cross-lingual instruction-tuning (CoIT) on LLaMA, i.e. tuning it with translation task data and cross-lingual general task data to obtain cross-lingual models (x-LLaMAs), and formulate underlying scaling laws to investigate the advantages of using scalable translation data. Then we perform multilingual instruction-tuning (MuIT) with mixed resources to build multilingual m-LLaMA. We also illustrate how we leverage the scaling laws to optimize data allocation in a resource-constrained setting. Experiment results on cross-lingual benchmarks XQUAD and MLQA show that x-LLaMAs surpass the English instruction-tuned counterpart (Alpaca) by an average of 27.83% across six non-English languages. Evaluation results on translation dataset Flores-101 show that x-LLaMAs outperform previous LLaMA-based models by an average of 18.89%. Encouragingly, m-LLaMA achieves comparable performance to x-LLaMAs on individual languages and demonstrates the ability to follow multilingual instructions. Further analysis on response content and representation space reveals the alignment of the multilingual semantic space within the middle layers of m-LLaMA.

This article presents the development and validation of a hybrid multi-sphere discrete element framework - Rigid3D, for the simulation of granular systems with arbitrarily shaped particles in 3D space. In this DEM framework, a non-spherical particle is approximated by three different geometric models: (1) multi-sphere model with overlapping spheres (MS model), (2) particle surface with triangle mesh (surface model), and (3) discretized particle body with polyhedral cells (cell model). The multi-sphere approach will be the "engine" for efficient DEM simulations, while the particle's mesh and cell models will be updated simultaneously according to the position and orientation of their associated MS model, for use in particle-related inter-phase couplings in a multi-phase flow. In this sense, Rigid3D tries to combine the best of both worlds in multi-sphere and polyhedral DEMs: multi-sphere method for the efficiency and acceptable accuracy in the DEM simulation of granular flows, while the surface and cell models for the couplings between particles and other phases (continuous or dispersed phases) without affecting the performance of DEM simulations., Comment: Manuscript for submission to Springer Journal - Computational Particle Mechanics

It is typically understood that the training of modern neural networks is a process of fitting the probability distribution of desired output. However, recent paradoxical observations in a number of language generation tasks let one wonder if this canonical probability-based explanation can really account for the empirical success of deep learning. To resolve this issue, we propose an alternative utility-based explanation to the standard supervised learning procedure in deep learning. The basic idea is to interpret the learned neural network not as a probability model but as an ordinal utility function that encodes the preference revealed in training data. In this perspective, training of the neural network corresponds to a utility learning process. Specifically, we show that for all neural networks with softmax outputs, the SGD learning dynamic of maximum likelihood estimation (MLE) can be seen as an iteration process that optimizes the neural network toward an optimal utility function. This utility-based interpretation can explain several otherwise-paradoxical observations about the neural networks thus trained. Moreover, our utility-based theory also entails an equation that can transform the learned utility values back to a new kind of probability estimation with which probability-compatible decision rules enjoy dramatic (double-digits) performance improvements. These evidences collectively reveal a phenomenon of utility-probability duality in terms of what modern neural networks are (truly) modeling: We thought they are one thing (probabilities), until the unexplainable showed up; changing mindset and treating them as another thing (utility values) largely reconcile the theory, despite remaining subtleties regarding its original (probabilistic) identity.

Multilingual understanding models (or encoder-based), pre-trained via masked language modeling, have achieved promising results on many language understanding tasks (e.g., mBERT). However, these non-autoregressive (NAR) models still struggle to generate high-quality texts compared with autoregressive (AR) models. Considering that encoder-based models have the advantage of efficient generation and self-correction abilities, this paper explores methods to empower multilingual understanding models the generation abilities to get a unified model. Specifically, we start from a multilingual encoder (XLM-R) and propose a \textbf{S}emantic-\textbf{G}uided \textbf{A}lignment-then-Denoising (SGA) approach to adapt an encoder to a multilingual generator with a small number of new parameters. Experiments show that the proposed approach is an effective adaption method, outperforming widely-used initialization-based methods with gains of 9.4 BLEU on machine translation, 8.1 Rouge-L on question generation, and 5.5 METEOR on story generation on XLM-R$_{large}$. On the other hand, we observe that XLM-R is still inferior to mBART in supervised settings despite better results on zero-shot settings, indicating that more exploration is required to make understanding models strong generators.

The goal of continuous sign language recognition(CSLR) research is to apply CSLR models as a communication tool in real life, and the real-time requirement of the models is important. In this paper, we address the model real-time problem through cross-resolution knowledge distillation. In our study, we found that keeping the frame-level feature scales consistent between the output of the student network and the teacher network is better than recovering the frame-level feature sizes for feature distillation. Based on this finding, we propose a new frame-level feature extractor that keeps the output frame-level features at the same scale as the output of by the teacher network. We further combined with the TSCM+2D hybrid convolution proposed in our previous study to form a new lightweight end-to-end CSLR network-Low resolution input net(LRINet). It is then used to combine cross-resolution knowledge distillation and traditional knowledge distillation methods to form a CSLR model based on cross-resolution knowledge distillation (CRKD). The CRKD uses high-resolution frames as input to the teacher network for training, locks the weights after training, and then uses low-resolution frames as input to the student network LRINet to perform knowledge distillation on frame-level features and classification features respectively. Experiments on two large-scale continuous sign language datasets have proved the effectiveness of CRKD. Compared with the model with high-resolution data as input, the calculation amount, parameter amount and inference time of the model have been significantly reduced under the same experimental conditions, while ensuring the accuracy of the model, and has achieved very competitive results in comparison with other advanced methods., Comment: 11 pages, 7 figures

Multilingual neural machine translation (MNMT) aims to build a unified model for many language directions. Existing monolithic models for MNMT encounter two challenges: parameter interference among languages and inefficient inference for large models. In this paper, we revisit the classic multi-way structures and develop a detachable model by assigning each language (or group of languages) to an individual branch that supports plug-and-play training and inference. To address the needs of learning representations for all languages in a unified space, we propose a novel efficient training recipe, upon which we build an effective detachable model, Lego-MT. For a fair comparison, we collect data from OPUS and build a translation benchmark covering 433 languages and 1.3B parallel data. Experiments show that Lego-MT with 1.2B parameters brings an average gain of 3.2 spBLEU. It even outperforms M2M-100 with 12B parameters. The proposed training recipe brings a 28.2$\times$ speedup over the conventional multi-way training method.\footnote{ \url{https://github.com/CONE-MT/Lego-MT}.}, Comment: ACL 2023 Findings

The ultimate goal of continuous sign language recognition(CSLR) is to facilitate the communication between special people and normal people, which requires a certain degree of real-time and deploy-ability of the model. However, in the previous research on CSLR, little attention has been paid to the real-time and deploy-ability. In order to improve the real-time and deploy-ability of the model, this paper proposes a zero parameter, zero computation temporal superposition crossover module(TSCM), and combines it with 2D convolution to form a "TSCM+2D convolution" hybrid convolution, which enables 2D convolution to have strong spatial-temporal modelling capability with zero parameter increase and lower deployment cost compared with other spatial-temporal convolutions. The overall CSLR model based on TSCM is built on the improved ResBlockT network in this paper. The hybrid convolution of "TSCM+2D convolution" is applied to the ResBlock of the ResNet network to form the new ResBlockT, and random gradient stop and multi-level CTC loss are introduced to train the model, which reduces the final recognition WER while reducing the training memory usage, and extends the ResNet network from image classification task to video recognition task. In addition, this study is the first in CSLR to use only 2D convolution extraction of sign language video temporal-spatial features for end-to-end learning for recognition. Experiments on two large-scale continuous sign language datasets demonstrate the effectiveness of the proposed method and achieve highly competitive results., Comment: 10 pages, 7 figures

Aiming at the problem that the spatial-temporal hierarchical continuous sign language recognition model based on deep learning has a large amount of computation, which limits the real-time application of the model, this paper proposes a temporal super-resolution network(TSRNet). The data is reconstructed into a dense feature sequence to reduce the overall model computation while keeping the final recognition accuracy loss to a minimum. The continuous sign language recognition model(CSLR) via TSRNet mainly consists of three parts: frame-level feature extraction, time series feature extraction and TSRNet, where TSRNet is located between frame-level feature extraction and time-series feature extraction, which mainly includes two branches: detail descriptor and rough descriptor. The sparse frame-level features are fused through the features obtained by the two designed branches as the reconstructed dense frame-level feature sequence, and the connectionist temporal classification(CTC) loss is used for training and optimization after the time-series feature extraction part. To better recover semantic-level information, the overall model is trained with the self-generating adversarial training method proposed in this paper to reduce the model error rate. The training method regards the TSRNet as the generator, and the frame-level processing part and the temporal processing part as the discriminator. In addition, in order to unify the evaluation criteria of model accuracy loss under different benchmarks, this paper proposes word error rate deviation(WERD), which takes the error rate between the estimated word error rate (WER) and the reference WER obtained by the reconstructed frame-level feature sequence and the complete original frame-level feature sequence as the WERD. Experiments on two large-scale sign language datasets demonstrate the effectiveness of the proposed model., Comment: 13 pages, 11 figures

In this paper, the surface of revolution discrete element method (SR-DEM) is introduced to simulate systems of particles with closed surfaces of revolution. Due to the cylindrical symmetry of a surface of revolution, the geometry of any cross-section about the axis of rotation remains the same. Taking advantage of this geometric feature, a node-to-cross-section contact algorithm is proposed for efficient contact detection between particles with a surface of revolution. In our SR-DEM framework, the contact algorithm is realized in a master-slave fashion: the master particle is approximated by its surface nodes, while the slave particle is represented by a signed distance field (SDF) of the cross-section about the axis of rotation. This hybrid formulation in both 2D and 3D space allows a very efficient contact calculation yet relatively simple code implementation. We then apply SR-DEM to simulate particle-particle, particle-wall impact, granular packing in a cylindrical container, and tablets in a rotating drum, to demonstrate SR-DEM's ability to predict the post-impact velocities, packing porosity, and dynamic angle of repose, respectively. Finally, we suggest a simple approach to find an optimal surface resolution, by increasing the number of surface nodes until some of the bulk properties that could characterize the system converge., Comment: Draft for Powder Tech

Continuous Sign Language Recognition (CSLR) is a challenging research task due to the lack of accurate annotation on the temporal sequence of sign language data. The recent popular usage is a hybrid model based on "CNN + RNN" for CSLR. However, when extracting temporal features in these works, most of the methods using a fixed temporal receptive field and cannot extract the temporal features well for each sign language word. In order to obtain more accurate temporal features, this paper proposes a multi-scale temporal network (MSTNet). The network mainly consists of three parts. The Resnet and two fully connected (FC) layers constitute the frame-wise feature extraction part. The time-wise feature extraction part performs temporal feature learning by first extracting temporal receptive field features of different scales using the proposed multi-scale temporal block (MST-block) to improve the temporal modeling capability, and then further encoding the temporal features of different scales by the transformers module to obtain more accurate temporal features. Finally, the proposed multi-level Connectionist Temporal Classification (CTC) loss part is used for training to obtain recognition results. The multi-level CTC loss enables better learning and updating of the shallow network parameters in CNN, and the method has no parameter increase and can be flexibly embedded in other models. Experimental results on two publicly available datasets demonstrate that our method can effectively extract sign language features in an end-to-end manner without any prior knowledge, improving the accuracy of CSLR and achieving competitive results., Comment: 10 pages, 7 figures