OpenRaiser/Intern-Atlas huggingface dataset:
$DA^3$: A Distribution-Aware Adversarial Attack against Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Language models can be manipulated by adversarial attacks, which introduce subtle perturbations to input data. While recent attack methods can achieve a relatively high attack success rate (ASR), we've observed that the generated adversarial examples have a different data distribution compared with the original examples. Specifically, these adversarial examples exhibit reduced confidence levels and greater divergence from the training data distribution. Consequently, they are easy to detect using straightforward detection methods, diminishing the efficacy of such attacks. To address this issue, we propose a Distribution-Aware Adversarial Attack ($DA^3$) method. $DA^3$ considers the distribution shifts of adversarial examples to improve attacks' effectiveness under detection methods. We further design a novel evaluation metric, the Non-detectable Attack Success Rate (NASR), which integrates both ASR and detectability for the attack task. We conduct experiments on four widely used datasets to validate the attack effectiveness and transferability of adversarial examples generated by $DA^3$ against both the white-box BERT-base and RoBERTa-base models and the black-box LLaMA2-7b model.
Wrapper Boxes: Faithful Attribution of Model Predictions to Training Data
Venue: arXiv cs.LG 2024
Year: 2024

Can we preserve the accuracy of neural models while also providing faithful explanations of model decisions to training data? We propose a "wrapper box'' pipeline: training a neural model as usual and then using its learned feature representation in classic, interpretable models to perform prediction. Across seven language models of varying sizes, including four large language models (LLMs), two datasets at different scales, three classic models, and four evaluation metrics, we first show that the predictive performance of wrapper classic models is largely comparable to the original neural models. Because classic models are transparent, each model decision is determined by a known set of training examples that can be directly shown to users. Our pipeline thus preserves the predictive performance of neural language models while faithfully attributing classic model decisions to training data. Among other use cases, such attribution enables model decisions to be contested based on responsible training instances. Compared to prior work, our approach achieves higher coverage and correctness in identifying which training data to remove to change a model decision. To reproduce findings, our source code is online at: https://github.com/SamSoup/WrapperBox.
On the Calibration of Multilingual Question Answering LLMs
Venue: arXiv cs.CL 2024
Year: 2024

Multilingual pre-trained Large Language Models (LLMs) are incredibly effective at Question Answering (QA), a core task in Natural Language Understanding, achieving high accuracies on several multilingual benchmarks. However, little is known about how well their confidences are calibrated. In this paper, we comprehensively benchmark the calibration of several multilingual LLMs (MLLMs) on a variety of QA tasks. We perform extensive experiments, spanning encoder-only, encoder-decoder, and decoder-only QA models (size varying from 110M to 7B parameters) and diverse languages, including both high- and low-resource ones. We study different dimensions of calibration in in-distribution, out-of-distribution, and cross-lingual transfer settings, and investigate strategies to improve it, including post-hoc methods and regularized fine-tuning. For decoder-only LLMs such as LlaMa2, we additionally find that in-context learning improves confidence calibration on multilingual data. We also conduct several ablation experiments to study the effect of language distances, language corpus size, and model size on calibration, and how multilingual models compare with their monolingual counterparts for diverse tasks and languages. Our experiments suggest that the multilingual QA models are poorly calibrated for languages other than English and incorporating a small set of cheaply translated multilingual samples during fine-tuning/calibration effectively enhances the calibration performance.
OFA: A Framework of Initializing Unseen Subword Embeddings for Efficient Large-scale Multilingual Continued Pretraining
Venue: arXiv cs.CL 2024
Year: 2024

Instead of pretraining multilingual language models from scratch, a more efficient method is to adapt existing pretrained language models (PLMs) to new languages via vocabulary extension and continued pretraining. However, this method usually randomly initializes the embeddings of new subwords and introduces substantially more embedding parameters to the model, thus weakening the efficiency. To address these issues, we propose a novel framework: $\textbf{O}$ne $\textbf{F}$or $\textbf{A}$ll ($\textbf{OFA}$), which wisely initializes the embeddings of unseen subwords and thus can adapt a PLM to multiple languages efficiently and effectively. OFA takes advantage of external well-aligned multilingual static word vectors and injects the alignment knowledge into the subword embeddings. In addition, OFA applies matrix factorization and replaces the cumbersome embeddings with two lower-dimensional matrices, which largely reduces the number of parameters. We show OFA accelerates the convergence of continued pretraining, which is environmentally friendly as much fewer carbon footprints are generated. Through extensive experiments, we demonstrate OFA can achieve competitive or better performance than default continued pretraining baselines on a wide range of crosslingual downstream tasks. We make our code and models publicly available.
Distilling Rule-based Knowledge into Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Large language models (LLMs) have shown incredible performance in completing various real-world tasks. The current paradigm of knowledge learning for LLMs is mainly based on learning from examples, in which LLMs learn the internal rule implicitly from a certain number of supervised examples. However, this learning paradigm may not well learn those complicated rules, especially when the training examples are limited. We are inspired that humans can learn the new tasks or knowledge in another way by learning from rules. That is, humans can learn new tasks or grasp new knowledge quickly and generalize well given only a detailed rule and a few optional examples. Therefore, in this paper, we aim to explore the feasibility of this new learning paradigm, which targets on encoding rule-based knowledge into LLMs. We further propose rule distillation, which first uses the strong in-context abilities of LLMs to extract the knowledge from the textual rules, and then explicitly encode the knowledge into the parameters of LLMs by learning from the above in-context signals produced inside the model. Our experiments show that making LLMs learn from rules by our method is much more efficient than example-based learning in both the sample size and generalization ability. Warning: This paper may contain examples with offensive content.
Homomorphic Polynomial Public Key Cryptography for Quantum-secure Digital Signature
Venue: arXiv cs.CR 2024
Year: 2024

In their 2022 study, Kuang et al. introduced Multivariable Polynomial Public Key (MPPK) cryptography, leveraging the inversion relationship between multiplication and division for quantum-safe public key systems. They extended MPPK into Homomorphic Polynomial Public Key (HPPK), employing homomorphic encryption for large hidden ring operations. Originally designed for key encapsulation (KEM), HPPK's security relies on homomorphic encryption of public polynomials. This paper expands HPPK KEM to a digital signature scheme, facing challenges due to the distinct nature of verification compared to decryption. To adapt HPPK KEM to digital signatures, the authors introduce an extension of the Barrett reduction algorithm, transforming modular multiplications into divisions in the verification equation over a prime field. The extended algorithm non-linearly embeds the signature into public polynomial coefficients, addressing vulnerabilities in earlier MPPK DS schemes. Security analysis demonstrates exponential complexity for private key recovery and forged signature attacks, considering ring bit length twice that of the prime field size.
6G Non-Terrestrial Networks Enabled Low-Altitude Economy: Opportunities and Challenges
Venue: arXiv cs.IT 2024
Year: 2024

The unprecedented development of non-terrestrial networks (NTN) utilizes the low-altitude airspace for commercial and social flying activities. The integration of NTN and terrestrial networks leads to the emergence of low-altitude economy (LAE). A series of LAE application scenarios are enabled by the sensing, communication, and transportation functionalities of the aircrafts. The prerequisite technologies supporting LAE are introduced in this paper, including the network coverage and aircrafts detection. The LAE functionalities assisted by aircrafts with respect to sensing and communication are then summarized, including the terrestrial and non-terrestrial targets sensing, ubiquitous coverage, relaying, and traffic offloading. Finally, several future directions are identified, including aircrafts collaboration, energy efficiency, and artificial intelligence enabled LAE.
Network-Level Integrated Sensing and Communication: Interference Management and BS Coordination Using Stochastic Geometry
Venue: arXiv cs.IT 2024
Year: 2024

In this work, we study integrated sensing and communication (ISAC) networks with the aim of effectively balancing sensing and communication (S&C) performance at the network level. Focusing on monostatic sensing, the tool of stochastic geometry is exploited to capture the S&C performance, which facilitates us to illuminate key cooperative dependencies in the ISAC network and optimize key network-level parameters. Based on the derived tractable expression of area spectral efficiency (ASE), we formulate the optimization problem to maximize the network performance from the view point of two joint S&C metrics. Towards this end, we further jointly optimize the cooperative BS cluster sizes for S&C and the serving/probing numbers of users/targets to achieve a flexible tradeoff between S&C at the network level. It is verified that interference nulling can effectively improve the average data rate and radar information rate. Surprisingly, the optimal communication tradeoff for the case of the ASE maximization tends to employ all spacial resources towards multiplexing and diversity gain, without interference nulling. By contrast, for the sensing objectives, resource allocation tends to eliminate certain interference especially when the antenna resources are sufficient, because the inter-cell interference becomes a more dominant factor affecting sensing performance. Furthermore, we prove that the ratio of the optimal number of users and the number of transmit antennas is a constant value when the communication performance is optimal. Simulation results demonstrate that the proposed cooperative ISAC scheme achieves a substantial gain in S&C performance at the network level.
Identifying Self-Disclosures of Use, Misuse and Addiction in Community-based Social Media Posts
Venue: arXiv cs.CL 2024
Year: 2024

In the last decade, the United States has lost more than 500,000 people from an overdose involving prescription and illicit opioids making it a national public health emergency (USDHHS, 2017). Medical practitioners require robust and timely tools that can effectively identify at-risk patients. Community-based social media platforms such as Reddit allow self-disclosure for users to discuss otherwise sensitive drug-related behaviors. We present a moderate size corpus of 2500 opioid-related posts from various subreddits labeled with six different phases of opioid use: Medical Use, Misuse, Addiction, Recovery, Relapse, Not Using. For every post, we annotate span-level extractive explanations and crucially study their role both in annotation quality and model development. We evaluate several state-of-the-art models in a supervised, few-shot, or zero-shot setting. Experimental results and error analysis show that identifying the phases of opioid use disorder is highly contextual and challenging. However, we find that using explanations during modeling leads to a significant boost in classification accuracy demonstrating their beneficial role in a high-stakes domain such as studying the opioid use disorder continuum.
How Vocabulary Sharing Facilitates Multilingualism in LLaMA?
Venue: arXiv cs.CL 2024
Year: 2024

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}.}.
Ever: Mitigating Hallucination in Large Language Models through Real-Time Verification and Rectification
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) have demonstrated remarkable proficiency in generating fluent text. However, they often encounter the challenge of generating inaccurate or hallucinated content. This issue is common in both non-retrieval-based generation and retrieval-augmented generation approaches, and existing post-hoc rectification methods may not address the accumulated hallucination errors that may be caused by the "snowballing" issue, especially in reasoning tasks. To tackle these challenges, we introduce a novel approach called Real-time Verification and Rectification (Ever). Instead of waiting until the end of the generation process to rectify hallucinations, Ever employs a real-time, step-wise generation and hallucination rectification strategy. The primary objective is to detect and rectify hallucinations as they occur during the text generation process. When compared to both retrieval-based and non-retrieval-based baselines, Ever demonstrates a significant improvement in generating trustworthy and factually accurate text across a diverse range of tasks, including short-form QA, biography generation, and multi-hop reasoning.
Fusion-Eval: Integrating Assistant Evaluators with LLMs
Venue: arXiv cs.CL 2024
Year: 2024

Evaluating natural language systems poses significant challenges, particularly in the realms of natural language understanding and high-level reasoning. In this paper, we introduce 'Fusion-Eval', an innovative approach that leverages Large Language Models (LLMs) to integrate insights from various assistant evaluators. The LLM is given the example to evaluate along with scores from the assistant evaluators. Each of these evaluators specializes in assessing distinct aspects of responses. Fusion-Eval achieves a 0.962 system-level Kendall-Tau correlation with humans on SummEval and a 0.744 turn-level Spearman correlation on TopicalChat, which is significantly higher than baseline methods. These results highlight Fusion-Eval's significant potential in the realm of natural language system evaluation.
Predicting Scientific Impact Through Diffusion, Conformity, and Contribution Disentanglement
Venue: arXiv cs.SI 2024
Year: 2024

The scientific impact of academic papers is influenced by intricate factors such as dynamic popularity and inherent contribution. Existing models typically rely on static graphs for citation count estimation, failing to differentiate among its sources. In contrast, we propose distinguishing effects derived from various factors and predicting citation increments as estimated potential impacts within the dynamic context. In this research, we introduce a novel model, DPPDCC, which Disentangles the Potential impacts of Papers into Diffusion, Conformity, and Contribution values. It encodes temporal and structural features within dynamic heterogeneous graphs derived from the citation networks and applies various auxiliary tasks for disentanglement. By emphasizing comparative and co-cited/citing information and aggregating snapshots evolutionarily, DPPDCC captures knowledge flow within the citation network. Afterwards, popularity is outlined by contrasting augmented graphs to extract the essence of citation diffusion and predicting citation accumulation bins for quantitative conformity modeling. Orthogonal constraints ensure distinct modeling of each perspective, preserving the contribution value. To gauge generalization across publication times and replicate the realistic dynamic context, we partition data based on specific time points and retain all samples without strict filtering. Extensive experiments on three datasets validate DPPDCC's superiority over baselines for papers published previously, freshly, and immediately, with further analyses confirming its robustness. Our codes and supplementary materials can be found at https://github.com/ECNU-Text-Computing/DPPDCC.
Auto-ICL: In-Context Learning without Human Supervision
Venue: arXiv cs.LG 2024
Year: 2024

With in-context learning ability, the performance of large language models can be significantly boosted when provided with appropriate context. However, existing in-context learning methods mainly rely on human-provided contexts, such as labeled examples and explicit instructions. Writing context by humans is labor-intensive on various tasks and limits the model to tasks manageable by humans. To overcome these limitations, we propose Automatic In-Context Learning framework that enables the model to autonomously generate examples and instructions for problem-solving. With experiments across various models and datasets, results show that model-generated contexts outperform human-annotated contexts, including Few-Shot and Few-Shot-CoT methods, and surpass existing self-generated context methods like Zero-CoT and Auto-CoT.
A Survey on Online User Aggression: Content Detection and Behavioral Analysis on Social Media
Venue: arXiv cs.CL 2024
Year: 2024

The rise of social media platforms has led to an increase in cyber-aggressive behavior, encompassing a broad spectrum of hostile behavior, including cyberbullying, online harassment, and the dissemination of offensive and hate speech. These behaviors have been associated with significant societal consequences, ranging from online anonymity to real-world outcomes such as depression, suicidal tendencies, and, in some instances, offline violence. Recognizing the societal risks associated with unchecked aggressive content, this paper delves into the field of Aggression Content Detection and Behavioral Analysis of Aggressive Users, aiming to bridge the gap between disparate studies. In this paper, we analyzed the diversity of definitions and proposed a unified cyber-aggression definition. We examine the comprehensive process of Aggression Content Detection, spanning from dataset creation, feature selection and extraction, and detection algorithm development. Further, we review studies on Behavioral Analysis of Aggression that explore the influencing factors, consequences, and patterns associated with cyber-aggressive behavior. This systematic literature review is a cross-examination of content detection and behavioral analysis in the realm of cyber-aggression. The integrated investigation reveals the effectiveness of incorporating sociological insights into computational techniques for preventing cyber-aggressive behavior. Finally, the paper concludes by identifying research gaps and encouraging further progress in the unified domain of socio-computational aggressive behavior analysis.
Taypsi: Static Enforcement of Privacy Policies for Policy-Agnostic Oblivious Computation
Venue: arXiv cs.PL 2024
Year: 2024

Secure multiparty computation (MPC) techniques enable multiple parties to compute joint functions over their private data without sharing that data with other parties, typically by employing powerful cryptographic protocols to protect individual's data. One challenge when writing such functions is that most MPC languages force users to intermix programmatic and privacy concerns in a single application, making it difficult to change or audit a program's underlying privacy policy. Prior policy-agnostic MPC languages relied on dynamic enforcement to decouple privacy requirements from program logic. Unfortunately, the resulting overhead makes it difficult to scale MPC applications that manipulate structured data. This work proposes to eliminate this overhead by instead transforming programs into semantically equivalent versions that statically enforce user-provided privacy policies. We have implemented this approach in a new MPC language, called Taypsi; our experimental evaluation demonstrates that the resulting system features considerable performance improvements on a variety of MPC applications involving structured data and complex privacy policies.
Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text Generation
Venue: arXiv cs.CL 2024
Year: 2024

Knowledge-to-text generators often struggle to faithfully generate descriptions for the input facts: they may produce hallucinations that contradict the input, or describe facts not present in the input. To reduce hallucinations, we propose a decoding-only method, TWEAK (Think While Effectively Articulating Knowledge), which can be integrated with any generator without retraining. TWEAK treats the generated sequences at each decoding step and its future sequences as hypotheses, and ranks each generation candidate based on the extent to which their hypotheses are supported by the input facts using a Hypothesis Verification Model (HVM). We first demonstrate the effectiveness of TWEAK by using a Natural Language Inference (NLI) model as the HVM and report improved faithfulness with a minimal impact on the quality. We then replace the NLI model with a task-specific HVM trained with a first-of-a-kind dataset, FATE (Fact-Aligned Textual Entailment), which pairs input facts with their original and perturbed descriptions. We test TWEAK with two generators, and the best TWEAK variants improve on average for the two models by 2.24/7.17 points in faithfulness (FactKB) in in/out-of-distribution evaluations, respectively, and with only a 0.14/0.32-point decline in quality (BERTScore).