GenAI4ELab/papercli-papers huggingface dataset:
Sparse MoE with Language Guided Routing for Multilingual Machine Translation
Authors: Xinyu Zhao; Xuxi Chen; Yu Cheng; Tianlong Chen
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Sparse Mixture-of-Experts (SMoE) has gained increasing popularity as a promising framework for scaling up multilingual machine translation (MMT) models with negligible extra computational overheads. However, current SMoE solutions neglect the intrinsic structures of the MMT problem: ($a$) $\textit{Linguistics Hierarchy.}$ Languages are naturally grouped according to their lingual properties like genetic families, phonological characteristics, etc; ($b$) $\textit{Language Complexity.}$ The learning difficulties are varied for diverse languages due to their grammar complexity, available resources, etc. Therefore, routing a fixed number of experts (e.g., $1$ or $2$ experts in usual) only at the word level leads to inferior performance. To fill in the missing puzzle, we propose $\textbf{\texttt{Lingual-SMoE}}$ by equipping the SMoE with adaptive and linguistic-guided routing policies. Specifically, it ($1$) extracts language representations to incorporate linguistic knowledge and uses them to allocate experts into different groups; ($2$) determines the number of activated experts for each target language in an adaptive and automatic manner, according to their translation difficulties, which aims to mitigate the potential over-/under-fitting issues of learning simple/challenges translations. Sufficient experimental studies on MMT benchmarks with {$16$, $50$, $100$} language pairs and various network architectures, consistently validate the superior performance of our proposals. For instance, $\texttt{Lingual-SMoE}$ outperforms its dense counterpart by over $5\%$ BLEU scores on $\texttt{OPUS-100}$ dataset.
Detecting Pretraining Data from Large Language Models
Authors: Weijia Shi; Anirudh Ajith; Mengzhou Xia; Yangsibo Huang; Daogao Liu; Terra Blevins; Danqi Chen; Luke Zettlemoyer
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Although large language models (LLMs) are widely deployed, the data used to train them is rarely disclosed. Given the incredible scale of this data, up to trillions of tokens, it is all but certain that it includes potentially problematic text such as copyrighted materials, personally identifiable information, and test data for widely reported reference benchmarks. However, we currently have no way to know which data of these types is included or in what proportions. In this paper, we study the pretraining data detection problem: given a piece of text and black-box access to an LLM without knowing the pretraining data, can we determine if the model was trained on the provided text? To facilitate this study, we introduce a dynamic benchmark WIKIMIA that uses data created before and after model training to support gold truth detection. We also introduce a new detection method MIN-K PROB based on a simple hypothesis: an unseen example is likely to contain a few outlier words with low probabilities under the LLM, while a seen example is less likely to have words with such low probabilities. MIN-K PROB can be applied without any knowledge about the pretrainig corpus or any additional training, departing from previous detection methods that require training a reference model on data that is similar to the pretraining data. Moreover, our experiments demonstrate that MIN-K PROB achieves a 7.4% improvement on WIKIMIA over these previous methods. We apply MIN-K PROB to two real-world scenarios, copyrighted book detection and contaminated downstream example detection, and find that it to be a consistently effective solution.
Don't Trust: Verify -- Grounding LLM Quantitative Reasoning with Autoformalization
Authors: Jin Peng Zhou; Charles E Staats; Wenda Li; Christian Szegedy; Kilian Q Weinberger; Yuhuai Wu
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Large language models (LLM), such as Google's Minerva and OpenAI's GPT families, are becoming increasingly capable of solving mathematical quantitative reasoning problems. However, they still make unjustified logical and computational errors in their reasoning steps and answers. In this paper, we leverage the fact that if the training corpus of LLMs contained sufficiently many examples of formal mathematics (e.g. in Isabelle, a formal theorem proving environment), they can be prompted to translate i.e. autoformalize informal mathematical statements into formal Isabelle code --- which can be verified automatically for internal consistency. This provides a mechanism to automatically reject solutions whose formalized versions are inconsistent within themselves or with the formalized problem statement. We evaluate our method on GSM8K, MATH and MultiArith datasets and demonstrate that our approach provides a consistently better heuristic than vanilla majority voting --- the previously best method to identify correct answers, by more than 12\% on GSM8K. In our experiments it improves results consistently across all datasets and LLM model sizes. The code can be found at https://github.com/jinpz/dtv.
On Diffusion Modeling for Anomaly Detection
Authors: Victor Livernoche; Vineet Jain; Yashar Hezaveh; Siamak Ravanbakhsh
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: Known for their impressive performance in generative modeling, diffusion models are attractive candidates for density-based anomaly detection. This paper investigates different variations of diffusion modeling for unsupervised and semi-supervised anomaly detection. In particular, we find that Denoising Diffusion Probability Models (DDPM) are performant on anomaly detection benchmarks yet computationally expensive. By simplifying DDPM in application to anomaly detection, we are naturally led to an alternative approach called Diffusion Time Estimation (DTE). DTE estimates the distribution over diffusion time for a given input and uses the mode or mean of this distribution as the anomaly score. We derive an analytical form for this density and leverage a deep neural network to improve inference efficiency. Through empirical evaluations on the ADBench benchmark, we demonstrate that all diffusion-based anomaly detection methods perform competitively for both semi-supervised and unsupervised settings. Notably, DTE achieves orders of magnitude faster inference time than DDPM, while outperforming it on this benchmark. These results establish diffusion-based anomaly detection as a scalable alternative to traditional methods and recent deep-learning techniques for standard unsupervised and semi-supervised anomaly detection settings.
PubDef: Defending Against Transfer Attacks From Public Models
Authors: Chawin Sitawarin; Jaewon Chang; David Huang; Wesson Altoyan; David Wagner
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Adversarial attacks have been a looming and unaddressed threat in the industry. However, through a decade-long history of the robustness evaluation literature, we have learned that mounting a strong or optimal attack is challenging. It requires both machine learning and domain expertise. In other words, the white-box threat model, religiously assumed by a large majority of the past literature, is unrealistic. In this paper, we propose a new practical threat model where the adversary relies on transfer attacks through publicly available surrogate models. We argue that this setting will become the most prevalent for security-sensitive applications in the future. We evaluate the transfer attacks in this setting and propose a specialized defense method based on a game-theoretic perspective. The defenses are evaluated under 24 public models and 11 attack algorithms across three datasets (CIFAR-10, CIFAR-100, and ImageNet). Under this threat model, our defense, PubDef, outperforms the state-of-the-art white-box adversarial training by a large margin with almost no loss in the normal accuracy. For instance, on ImageNet, our defense achieves 62% accuracy under the strongest transfer attack vs only 36% of the best adversarially trained model. Its accuracy when not under attack is only 2% lower than that of an undefended model (78% vs 80%). We release our code at https://github.com/wagner-group/pubdef.
Social Reward: Evaluating and Enhancing Generative AI through Million-User Feedback from an Online Creative Community
Authors: Arman Isajanyan; Artur Shatveryan; David Kocharian; Zhangyang Wang; Humphrey Shi
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: Social reward as a form of community recognition provides a strong source of
motivation for users of online platforms to actively engage and contribute with
content to accumulate peers approval. In the realm of text-conditioned image
synthesis, the recent surge in progress has ushered in a collaborative era where
users and AI systems coalesce to refine visual creations. This co-creative pro-
cess in the landscape of online social networks empowers users to craft original
visual artworks seeking for community validation. Nevertheless, assessing these
models in the context of collective community preference introduces distinct chal-
lenges. Existing evaluation methods predominantly center on limited size user
studies guided by image quality and alignment with prompts. This work pio-
neers a paradigm shift, unveiling Social Reward - an innovative reward modeling
framework that leverages implicit feedback from social network users engaged
in creative editing of generated images. We embark on an extensive journey of
dataset curation and refinement, drawing from Picsart: an online visual creation
and editing platform, yielding a first million-user-scale dataset of implicit human
preferences for user-generated visual art named Picsart Image-Social. Our anal-
ysis exposes the shortcomings of current metrics in modeling community creative
preference of text-to-image models’ outputs, compelling us to introduce a novel
predictive model explicitly tailored to address these limitations. Rigorous quan-
titative experiments and user study show that our Social Reward model aligns
better with social popularity than existing metrics. Furthermore, we utilize So-
cial Reward to fine-tune text-to-image models, yielding images that are more fa-
vored by not only Social Reward, but also other established metrics. These find-
ings highlight the relevance and effectiveness of Social Reward in assessing com-
munity appreciation for AI-generated artworks, establishing a closer alignment
with users’ creative goals: creating popular visual art. Codes can be accessed at
https://github.com/Picsart-AI-Research/Social-Reward
AutomaTikZ: Text-Guided Synthesis of Scientific Vector Graphics with TikZ
Authors: Jonas Belouadi; Anne Lauscher; Steffen Eger
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Generating bitmap graphics from text has gained considerable attention, yet for scientific figures, vector graphics are often preferred. Given that vector graphics are typically encoded using low-level graphics primitives, generating them directly is difficult. To address this, we propose the use of TikZ, a well-known abstract graphics language that can be compiled to vector graphics, as an intermediate representation of scientific figures. TikZ offers human-oriented, high-level commands, thereby facilitating conditional language modeling with any large language model. To this end, we introduce DaTikZ the first large-scale TikZ dataset, consisting of 120k TikZ drawings aligned with captions. We fine-tune LLaMA on DaTikZ, as well as our new model CLiMA, which augments LLaMA with multimodal CLIP embeddings. In both human and automatic evaluation, CLiMA and LLaMA outperform commercial GPT-4 and Claude 2 in terms of similarity to human-created figures, with CLiMA additionally improving text-image alignment. Our detailed analysis shows that all models generalize well and are not susceptible to memorization. GPT-4 and Claude 2, however, tend to generate more simplistic figures compared to both humans and our models. We make our framework, AutomaTikZ, along with model weights and datasets, publicly available.
Sharpness-Aware Minimization Enhances Feature Quality via Balanced Learning
Authors: Jacob Mitchell Springer; Vaishnavh Nagarajan; Aditi Raghunathan
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Sharpness-Aware Minimization (SAM) has emerged as a promising alternative optimizer to stochastic gradient descent (SGD). The originally-proposed motivation behind SAM was to bias neural networks towards flatter minima that are believed to generalize better. However, recent studies have shown conflicting evidence on the relationship between flatness and generalization, suggesting that flatness does fully explain SAM's success. Sidestepping this debate, we identify an orthogonal effect of SAM that is beneficial out-of-distribution: we argue that SAM implicitly balances the quality of diverse features. SAM achieves this effect by adaptively suppressing well-learned features which gives remaining features opportunity to be learned. We show that this mechanism is beneficial in datasets that contain redundant or spurious features where SGD falls for the simplicity bias and would not otherwise learn all available features. Our insights are supported by experiments on real data: we demonstrate that SAM improves the quality of features in datasets containing redundant or spurious features, including CelebA, Waterbirds, CIFAR-MNIST, and DomainBed.
Can LLM-Generated Misinformation Be Detected?
Authors: Canyu Chen; Kai Shu
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: The advent of Large Language Models (LLMs) has made a transformative impact. However, the potential that LLMs such as ChatGPT can be exploited to generate misinformation has posed a serious concern to online safety and public trust. A fundamental research question is: will LLM-generated misinformation cause more harm than human-written misinformation? We propose to tackle this question from the perspective of detection difficulty. We first build a taxonomy of LLM-generated misinformation. Then we categorize and validate the potential real-world methods for generating misinformation with LLMs. Then, through extensive empirical investigation, we discover that LLM-generated misinformation can be harder to detect for humans and detectors compared to human-written misinformation with the same semantics, which suggests it can have more deceptive styles and potentially cause more harm. We also discuss the implications of our discovery on combating misinformation in the age of LLMs and the countermeasures.
A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis
Authors: DIPANJYOTI PAUL; Arpita Chowdhury; Xinqi Xiong; Feng-Ju Chang; David Edward Carlyn; Samuel Stevens; Kaiya L Provost; Anuj Karpatne; Bryan Carstens; Daniel Rubenstein; Charles Stewart; Tanya Berger-Wolf; Yu Su; Wei-Lun Chao
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: We present a novel usage of Transformers to make image classification interpretable. Unlike mainstream classifiers that wait until the last fully connected layer to incorporate class information to make predictions, we investigate a proactive approach, asking each class to search for itself in an image. We realize this idea via a Transformer encoder-decoder inspired by DEtection TRansformer (DETR). We learn ''class-specific'' queries (one for each class) as input to the decoder, enabling each class to localize its patterns in an image via cross-attention. We name our approach INterpretable TRansformer (INTR), which is fairly easy to implement and exhibits several compelling properties. We show that INTR intrinsically encourages each class to attend distinctively; the cross-attention weights thus provide a faithful interpretation of the prediction. Interestingly, via ''multi-head'' cross-attention, INTR could identify different ''attributes'' of a class, making it particularly suitable for fine-grained classification and analysis, which we demonstrate on eight datasets. Our code and pre-trained models are publicly accessible at the Imageomics Institute GitHub site: https://github.com/Imageomics/INTR.
Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!
Authors: Xiangyu Qi; Yi Zeng; Tinghao Xie; Pin-Yu Chen; Ruoxi Jia; Prateek Mittal; Peter Henderson
Venue: ICLR 2024 (ICLR 2024 oral)

Abstract: Optimizing large language models (LLMs) for downstream use cases often involves the customization of pre-trained LLMs through further fine-tuning. Meta's open-source release of Llama models and OpenAI's APIs for fine-tuning GPT-3.5 Turbo on customized datasets accelerate this trend. But, what are the safety costs associated with such customized fine-tuning? While existing safety alignment techniques restrict harmful behaviors of LLMs at inference time, they do not cover safety risks when fine-tuning privileges are extended to end-users. Our red teaming studies find that the safety alignment of LLMs can be compromised by fine-tuning with only a few adversarially designed training examples. For instance, we jailbreak GPT-3.5 Turbo's safety guardrails by fine-tuning it on only 10 such examples at a cost of less than $0.20 via OpenAI's APIs, making the model responsive to nearly any harmful instructions. Disconcertingly, our research also reveals that, even without malicious intent, simply fine-tuning with benign and commonly used datasets can also inadvertently degrade the safety alignment of LLMs, though to a lesser extent. These findings suggest that fine-tuning aligned LLMs introduces new safety risks that current safety infrastructures fall short of addressing --- even if a model's initial safety alignment is impeccable, how can it be maintained after customized fine-tuning? We outline and critically analyze potential mitigations and advocate for further research efforts toward reinforcing safety protocols for the customized fine-tuning of aligned LLMs.  (This paper contains red-teaming data and model-generated content that can be offensive in nature.)
One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models
Authors: Sheng-Jun Huang; Yi Li; Yiming Sun; Ying-Peng Tang
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Active learning (AL) for multiple target models aims to reduce labeled data querying while effectively training multiple models concurrently. Existing AL algorithms often rely on iterative model training, which can be computationally expensive, particularly for deep models. In this paper, we propose a one-shot AL method to address this challenge, which performs all label queries without repeated model training. Specifically, we extract different representations of the same dataset using distinct network backbones, and actively learn the linear prediction layer on each representation via an $\ell_p$-regression formulation. The regression problems are solved approximately by 
sampling and reweighting the unlabeled instances based on their maximum Lewis weights across the representations. An upper bound on the number of samples needed is provided with a rigorous analysis for $p\in [1, +\infty)$. Experimental results on 11 benchmarks show that our one-shot approach achieves competitive performances with the state-of-the-art AL methods for multiple target models.
Disentangling Time Series Representations via Contrastive Independence-of-Support on l-Variational Inference
Authors: Khalid Oublal; Said Ladjal; David Benhaiem; Emmanuel LE BORGNE; François Roueff
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Learning disentangled representations for time series is a promising path to facilitate reliable generalization to in- and out-of distribution (OOD), offering benefits like feature derivation and improved interpretability and fairness, thereby enhancing downstream tasks. We focus on disentangled representation learning for home appliance electricity usage, enabling users to understand and optimize their consumption for a reduced carbon footprint. Our approach frames the problem as disentangling each attribute's role in total consumption. Unlike existing methods assuming attribute independence which leads to non-identiability, we acknowledge real-world time series attribute correlations, learned up to a smooth bijection using contrastive learning and a single autoencoder. To address this, we propose a Disentanglement under Independence-Of-Support via Contrastive Learning (DIOSC), facilitating representation generalization across diverse correlated scenarios. Our method utilizes innovative \textit{l}-variational inference layers with self-attention, effectively addressing temporal dependencies across bottom-up and top-down networks. We find that DIOSC can enhance the task of representation of time series electricity consumption. We introduce TDS (Time Disentangling Score) to gauge disentanglement quality. TDS reliably reflects disentanglement performance, making it a valuable metric for evaluating time series representations disentanglement. Code available at https://institut-polytechnique-de-paris.github.io/time-disentanglement-lib.
Improved algorithm and bounds for successive projection
Authors: Jiashun Jin; Tracy Ke; Gabriel Moryoussef; Jiajun Tang; Jingming Wang
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Consider a $K$-vertex simplex in a $d$-dimensional  space. We measure $n$ points on the simplex, but due to the measurement noise, 
some of the observed points fall outside the simplex. The interest is vertex hunting (i.e.,  estimating the vertices of the simplex).  The successive projection algorithm (SPA)  is one of the most popular approaches to vertex hunting, but it is vulnerable to noise and outliers, and may perform unsatisfactorily.  We propose pseudo-point SPA  (pp-SPA) as a new approach to vertex hunting. The approach contains 
two novel ideas (a projection step and a denoise step) and generates roughly $n$ pseudo-points, which can be fed in to SPA for vertex hunting. For theory, we first derive an improved non-asymptotic bound for the orthodox SPA, and then use the result to derive the bounds for pp-SPA.  Compared with the orthodox SPA,  pp-SPA has a faster rate and more satisfactory numerical performance in a broad setting.  The analysis is quite delicate: the non-asymptotic bound is hard to derive, and we need precise results on the extreme values of (possibly) high-dimensional random vectors.
Role of Locality and Weight Sharing in Image-Based Tasks: A Sample Complexity Separation between CNNs, LCNs, and FCNs
Authors: Aakash Lahoti; Stefani Karp; Ezra Winston; Aarti Singh; Yuanzhi Li
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: Vision tasks are characterized by the properties of locality and translation invariance. 
    The superior performance of convolutional neural networks (CNNs) on these tasks is widely attributed to the inductive bias of locality and weight sharing baked into their architecture.
    Existing attempts to quantify the statistical benefits of these biases in CNNs over locally connected convolutional neural networks (LCNs) and fully connected neural networks (FCNs) fall into one of the following categories: either they disregard the optimizer and only provide uniform convergence upper bounds with no separating lower bounds, 
    or they consider simplistic tasks that do not truly mirror the locality and translation invariance as found in real-world vision tasks.
    To address these deficiencies, we introduce the Dynamic Signal Distribution (DSD) classification task that models an image as consisting of $k$ patches, each of dimension $d$, and the label is determined by a $d$-sparse signal vector that can freely appear in any one of the $k$ patches. 
    On this task, for any orthogonally equivariant algorithm like gradient descent, we prove that CNNs require $\tilde{O}(k+d)$ samples, whereas LCNs require $\Omega(kd)$ samples, establishing the statistical advantages of weight sharing in translation invariant tasks. 
    Furthermore, LCNs need $\tilde{O}(k(k+d))$ samples, compared to $\Omega(k^2d)$ samples for FCNs, showcasing the benefits of locality in local tasks.
    Additionally, we develop information theoretic tools for analyzing randomized algorithms, which may be of interest for statistical research.