OpenRaiser/Intern-Atlas huggingface dataset:
GEMBA-MQM: Detecting Translation Quality Error Spans with GPT-4
Venue: arXiv cs.CL 2023
Year: 2023

This paper introduces GEMBA-MQM, a GPT-based evaluation metric designed to detect translation quality errors, specifically for the quality estimation setting without the need for human reference translations. Based on the power of large language models (LLM), GEMBA-MQM employs a fixed three-shot prompting technique, querying the GPT-4 model to mark error quality spans. Compared to previous works, our method has language-agnostic prompts, thus avoiding the need for manual prompt preparation for new languages. While preliminary results indicate that GEMBA-MQM achieves state-of-the-art accuracy for system ranking, we advise caution when using it in academic works to demonstrate improvements over other methods due to its dependence on the proprietary, black-box GPT model.
A Novel Information-Theoretic Objective to Disentangle Representations for Fair Classification
Venue: arXiv cs.LG 2023
Year: 2023

One of the pursued objectives of deep learning is to provide tools that learn abstract representations of reality from the observation of multiple contextual situations. More precisely, one wishes to extract disentangled representations which are (i) low dimensional and (ii) whose components are independent and correspond to concepts capturing the essence of the objects under consideration (Locatello et al., 2019b). One step towards this ambitious project consists in learning disentangled representations with respect to a predefined (sensitive) attribute, e.g., the gender or age of the writer. Perhaps one of the main application for such disentangled representations is fair classification. Existing methods extract the last layer of a neural network trained with a loss that is composed of a cross-entropy objective and a disentanglement regularizer. In this work, we adopt an information-theoretic view of this problem which motivates a novel family of regularizers that minimizes the mutual information between the latent representation and the sensitive attribute conditional to the target. The resulting set of losses, called CLINIC, is parameter free and thus, it is easier and faster to train. CLINIC losses are studied through extensive numerical experiments by training over 2k neural networks. We demonstrate that our methods offer a better disentanglement/accuracy trade-off than previous techniques, and generalize better than training with cross-entropy loss solely provided that the disentanglement task is not too constraining.
Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models
Venue: arXiv cs.CL 2023
Year: 2023

Proprietary and closed APIs are becoming increasingly common to process natural language, and are impacting the practical applications of natural language processing, including few-shot classification. Few-shot classification involves training a model to perform a new classification task with a handful of labeled data. This paper presents three contributions. First, we introduce a scenario where the embedding of a pre-trained model is served through a gated API with compute-cost and data-privacy constraints. Second, we propose a transductive inference, a learning paradigm that has been overlooked by the NLP community. Transductive inference, unlike traditional inductive learning, leverages the statistics of unlabeled data. We also introduce a new parameter-free transductive regularizer based on the Fisher-Rao loss, which can be used on top of the gated API embeddings. This method fully utilizes unlabeled data, does not share any label with the third-party API provider and could serve as a baseline for future research. Third, we propose an improved experimental setting and compile a benchmark of eight datasets involving multiclass classification in four different languages, with up to 151 classes. We evaluate our methods using eight backbone models, along with an episodic evaluation over 1,000 episodes, which demonstrate the superiority of transductive inference over the standard inductive setting.
Toward Stronger Textual Attack Detectors
Venue: arXiv cs.CL 2023
Year: 2023

The landscape of available textual adversarial attacks keeps growing, posing severe threats and raising concerns regarding the deep NLP system's integrity. However, the crucial problem of defending against malicious attacks has only drawn the attention of the NLP community. The latter is nonetheless instrumental in developing robust and trustworthy systems. This paper makes two important contributions in this line of search: (i) we introduce LAROUSSE, a new framework to detect textual adversarial attacks and (ii) we introduce STAKEOUT, a new benchmark composed of nine popular attack methods, three datasets, and two pre-trained models. LAROUSSE is ready-to-use in production as it is unsupervised, hyperparameter-free, and non-differentiable, protecting it against gradient-based methods. Our new benchmark STAKEOUT allows for a robust evaluation framework: we conduct extensive numerical experiments which demonstrate that LAROUSSE outperforms previous methods, and which allows to identify interesting factors of detection rate variations.
Fast Diffusion GAN Model for Symbolic Music Generation Controlled by Emotions
Venue: arXiv cs.SD 2023
Year: 2023

Diffusion models have shown promising results for a wide range of generative tasks with continuous data, such as image and audio synthesis. However, little progress has been made on using diffusion models to generate discrete symbolic music because this new class of generative models are not well suited for discrete data while its iterative sampling process is computationally expensive. In this work, we propose a diffusion model combined with a Generative Adversarial Network, aiming to (i) alleviate one of the remaining challenges in algorithmic music generation which is the control of generation towards a target emotion, and (ii) mitigate the slow sampling drawback of diffusion models applied to symbolic music generation. We first used a trained Variational Autoencoder to obtain embeddings of a symbolic music dataset with emotion labels and then used those to train a diffusion model. Our results demonstrate the successful control of our diffusion model to generate symbolic music with a desired emotion. Our model achieves several orders of magnitude improvement in computational cost, requiring merely four time steps to denoise while the steps required by current state-of-the-art diffusion models for symbolic music generation is in the order of thousands.
CTMaaS: An innovative platform for C-ITS-enabled dynamic Traffic and Fleet Management as a Service
Venue: arXiv cs.OH 2023
Year: 2023

Fleet management systems have been one of the most important research fields in transportation science. Nowadays the enhancement of fleet management systems with technologies such as the Cooperative Intelligent Transport System (CITS) that allows fleets to communicate with their environment, with other vehicles or with the road infrastructure, resulting in safer and more efficient road travel. This paper aims to present the CTMaaS platform, a tool which integrates CITS services and traffic management processes to manage vehicle fleets. Starting with a literature review, the paper presents various fleet management systems, that have been developed in the last years, and the most typical CITS services. The next chapters present the CTMaaS platform, use cases, and methodology.
An Offer you Cannot Refuse? Trends in the Coerciveness of Amazon Book Recommendations
Venue: arXiv cs.CY 2023
Year: 2023

Recommender systems can be a helpful tool for recommending content but they can also influence users' preferences. One sociological theory for this influence is that companies are incentivised to influence preferences to make users easier to predict and thus more profitable by making it harder to change preferences. This paper seeks to test that theory empirically. We use \textit{Barrier-to-Exit}, a metric for how difficult it is for users to change preferences, to analyse a large dataset of Amazon Book Ratings from 1998 to 2018. We focus the analysis on users who have changed preferences according to Barrier-to-Exit. To assess the growth of Barrier-to-Exit over time, we developed a linear mixed-effects model with crossed random effects for users and categories. Our findings indicate a highly significant growth of Barrier-to-Exit over time, suggesting that it has become more difficult for the analysed subset of users to change their preferences. However, it should be noted that these findings come with several statistical and methodological caveats including sample bias and construct validity issues related to Barrier-to-Exit. We discuss the strengths and limitations of our approach and its implications. Additionally, we highlight the challenges of creating context-sensitive and generalisable measures for complex socio-technical concepts such as "difficulty to change preferences." We conclude with a call for further research: to curb the potential threats of preference manipulation, we need more measures that allow us to compare commercial as well as non-commercial systems.
On the Neural Tangent Kernel of Equilibrium Models
Venue: arXiv cs.LG 2023
Year: 2023

This work studies the neural tangent kernel (NTK) of the deep equilibrium (DEQ) model, a practical ``infinite-depth'' architecture which directly computes the infinite-depth limit of a weight-tied network via root-finding. Even though the NTK of a fully-connected neural network can be stochastic if its width and depth both tend to infinity simultaneously, we show that contrarily a DEQ model still enjoys a deterministic NTK despite its width and depth going to infinity at the same time under mild conditions. Moreover, this deterministic NTK can be found efficiently via root-finding.
Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots
Venue: arXiv cs.RO 2023
Year: 2023

Tracking of inventory and rearrangement of misplaced items are some of the most labor-intensive tasks in a retail environment. While there have been attempts at using vision-based techniques for these tasks, they mostly use planogram compliance for detection of any anomalies, a technique that has been found lacking in robustness and scalability. Moreover, existing systems rely on human intervention to perform corrective actions after detection. In this paper, we present Co-AD, a Concept-based Anomaly Detection approach using a Vision Transformer (ViT) that is able to flag misplaced objects without using a prior knowledge base such as a planogram. It uses an auto-encoder architecture followed by outlier detection in the latent space. Co-AD has a peak success rate of 89.90% on anomaly detection image sets of retail objects drawn from the RP2K dataset, compared to 80.81% on the best-performing baseline of a standard ViT auto-encoder. To demonstrate its utility, we describe a robotic mobile manipulation pipeline to autonomously correct the anomalies flagged by Co-AD. This work is ultimately aimed towards developing autonomous mobile robot solutions that reduce the need for human intervention in retail store management.
POVNav: A Pareto-Optimal Mapless Visual Navigator
Venue: arXiv cs.RO 2023
Year: 2023

Mapless navigation has emerged as a promising approach for enabling autonomous robots to navigate in environments where pre-existing maps may be inaccurate, outdated, or unavailable. In this work, we propose an image-based local representation of the environment immediately around a robot to parse navigability. We further develop a local planning and control framework, a Pareto-optimal mapless visual navigator (POVNav), to use this representation and enable autonomous navigation in various challenging and real-world environments. In POVNav, we choose a Pareto-optimal sub-goal in the image by evaluating all the navigable pixels, finding a safe visual path, and generating actions to follow the path using visual servo control. In addition to providing collision-free motion, our approach enables selective navigation behavior, such as restricting navigation to select terrain types, by only changing the navigability definition in the local representation. The ability of POVNav to navigate a robot to the goal using only a monocular camera without relying on a map makes it computationally light and easy to implement on various robotic platforms. Real-world experiments in diverse challenging environments, ranging from structured indoor environments to unstructured outdoor environments such as forest trails and roads after a heavy snowfall, using various image segmentation techniques demonstrate the remarkable efficacy of our proposed framework.
Leveraging Knowledge Graphs for Orphan Entity Allocation in Resume Processing
Venue: arXiv cs.CL 2023
Year: 2023

Significant challenges are posed in talent acquisition and recruitment by processing and analyzing unstructured data, particularly resumes. This research presents a novel approach for orphan entity allocation in resume processing using knowledge graphs. Techniques of association mining, concept extraction, external knowledge linking, named entity recognition, and knowledge graph construction are integrated into our pipeline. By leveraging these techniques, the aim is to automate and enhance the efficiency of the job screening process by successfully bucketing orphan entities within resumes. This allows for more effective matching between candidates and job positions, streamlining the resume screening process, and enhancing the accuracy of candidate-job matching. The approach's exceptional effectiveness and resilience are highlighted through extensive experimentation and evaluation, ensuring that alternative measures can be relied upon for seamless processing and orphan entity allocation in case of any component failure. The capabilities of knowledge graphs in generating valuable insights through intelligent information extraction and representation, specifically in the domain of categorizing orphan entities, are highlighted by the results of our research.
A Student-Dominant View of the Readiness to use Metaverse in Education: The TRI-F Framework
Venue: arXiv cs.CY 2023
Year: 2023

This paper reports on students' readiness for using Metaverse for education in a university in a developing country facing infrastructure and poverty challenges. Covid-19 forced many universities to adopt a hybrid approach to teaching and supervision. While online meeting technologies have become commonplace, there is a lack of the connectedness of face-to-face meetings, for which Metaverse is promoted as a solution. We pose the question as to the level of readiness of students to use Metaverse technologies. Thematic analysis of students' self-reflections on their experience of supervision in a 2D virtual world revealed the usefulness of the technology readiness index model, from which an extension to the model was proposed to include facilitators for the application of the technology that may mediate the motivators and inhibitors when assessing readiness to use Metaverse in education settings.
Sorting from Crowdsourced Comparisons using Expert Verifications
Venue: arXiv cs.DS 2023
Year: 2023

We introduce a novel noisy sorting model motivated by the Just Noticeable Difference (JND) model from experimental psychology. The goal of our model is to capture the low quality of the data that are collected from crowdsourcing environments. Compared to other celebrated models of noisy sorting, our model does not rely on precise data-generation assumptions and captures crowdsourced tasks' varying levels of difficulty that can lead to different amounts of noise in the data. To handle this challenging task, we assume we can verify some of the collected data using expert advice. This verification procedure is costly; hence, we aim to minimize the number of verifications we use. We propose a new efficient algorithm called CandidateSort, which we prove uses the optimal number of verifications in the noisy sorting models we consider. We characterize this optimal number of verifications by showing that it is linear in a parameter $k$, which intuitively measures the maximum number of comparisons that are wrong but not inconsistent in the crowdsourcing data.
Note on dissecting power of regular languages
Venue: arXiv cs.FL 2023
Year: 2023

Let $c>1$ be a real constant. We say that a language $L$ is $c$-\emph{constantly growing} if for every word $u\in L$ there is a word $v\in L$ with $\vert u\vert<\vert v\vert\leq c+\vert u\vert$. We say that a language $L$ is $c$-\emph{geometrically growing} if for every word $u\in L$ there is a word $v\in L$ with $\vert u\vert<\vert v\vert\leq c\vert u\vert$. Given a language $L$, we say that $L$ is $REG$-\emph{dissectible} if there is a regular language $R$ such that $\vert L\setminus R\vert=\infty$ and $\vert L\cap R\vert=\infty$. In 2013, it was shown that every $c$-constantly growing language $L$ is $REG$-dissectible. In 2023, the following open question has been presented: "Is the family of geometrically growing languages $REG$-dissectible?" We construct a $c$-geometrically growing language $L$ that is not $REG$-dissectible. Hence we answer negatively to the open question.
Prompt-based Grouping Transformer for Nucleus Detection and Classification
Venue: arXiv cs.CV 2023
Year: 2023

Automatic nuclei detection and classification can produce effective information for disease diagnosis. Most existing methods classify nuclei independently or do not make full use of the semantic similarity between nuclei and their grouping features. In this paper, we propose a novel end-to-end nuclei detection and classification framework based on a grouping transformer-based classifier. The nuclei classifier learns and updates the representations of nuclei groups and categories via hierarchically grouping the nucleus embeddings. Then the cell types are predicted with the pairwise correlations between categorical embeddings and nucleus features. For the efficiency of the fully transformer-based framework, we take the nucleus group embeddings as the input prompts of backbone, which helps harvest grouping guided features by tuning only the prompts instead of the whole backbone. Experimental results show that the proposed method significantly outperforms the existing models on three datasets.
Nonlinear Codes with Low Redundancy
Venue: arXiv cs.IT 2023
Year: 2023

Determining the largest size, or equivalently finding the lowest redundancy, of q-ary codes for given length and minimum distance is one of the central and fundamental problems in coding theory. Inspired by the construction of Varshamov-Tenengolts (VT for short) codes via check-sums, we provide an explicit construction of nonlinear codes with lower redundancy than linear codes under the same length and minimum distance. Similar to the VT codes, our construction works well for small distance (or even constant distance). Furthermore, we design quasi-linear time decoding algorithms for both erasure and adversary errors.
The Importance of Anti-Aliasing in Tiny Object Detection
Venue: arXiv cs.CV 2023
Year: 2023

Tiny object detection has gained considerable attention in the research community owing to the frequent occurrence of tiny objects in numerous critical real-world scenarios. However, convolutional neural networks (CNNs) used as the backbone for object detection architectures typically neglect Nyquist's sampling theorem during down-sampling operations, resulting in aliasing and degraded performance. This is likely to be a particular issue for tiny objects that occupy very few pixels and therefore have high spatial frequency features. This paper applied an existing approach WaveCNet for anti-aliasing to tiny object detection. WaveCNet addresses aliasing by replacing standard down-sampling processes in CNNs with Wavelet Pooling (WaveletPool) layers, effectively suppressing aliasing. We modify the original WaveCNet to apply WaveletPool in a consistent way in both pathways of the residual blocks in ResNets. Additionally, we also propose a bottom-heavy version of the backbone, which further improves the performance of tiny object detection while also reducing the required number of parameters by almost half. Experimental results on the TinyPerson, WiderFace, and DOTA datasets demonstrate the importance of anti-aliasing in tiny object detection and the effectiveness of the proposed method which achieves new state-of-the-art results on all three datasets. Codes and experiment results are released at https://github.com/freshn/Anti-aliasing-Tiny-Object-Detection.git.
Language Model Unalignment: Parametric Red-Teaming to Expose Hidden Harms and Biases
Venue: arXiv cs.CL 2023
Year: 2023

Red-teaming has been a widely adopted way to evaluate the harmfulness of Large Language Models (LLMs). It aims to jailbreak a model's safety behavior to make it act as a helpful agent disregarding the harmfulness of the query. Existing methods are primarily based on input text-based red-teaming such as adversarial prompts, low-resource prompts, or contextualized prompts to condition the model in a way to bypass its safe behavior. Bypassing the guardrails uncovers hidden harmful information and biases in the model that are left untreated or newly introduced by its safety training. However, prompt-based attacks fail to provide such a diagnosis owing to their low attack success rate, and applicability to specific models. In this paper, we present a new perspective on LLM safety research i.e., parametric red-teaming through Unalignment. It simply (instruction) tunes the model parameters to break model guardrails that are not deeply rooted in the model's behavior. Unalignment using as few as 100 examples can significantly bypass commonly referred to as CHATGPT, to the point where it responds with an 88% success rate to harmful queries on two safety benchmark datasets. On open-source models such as VICUNA-7B and LLAMA-2-CHAT 7B AND 13B, it shows an attack success rate of more than 91%. On bias evaluations, Unalignment exposes inherent biases in safety-aligned models such as CHATGPT and LLAMA- 2-CHAT where the model's responses are strongly biased and opinionated 64% of the time.
CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural Text
Venue: arXiv cs.CL 2023
Year: 2023

In this paper, we propose CLMSM, a domain-specific, continual pre-training framework, that learns from a large set of procedural recipes. CLMSM uses a Multi-Task Learning Framework to optimize two objectives - a) Contrastive Learning using hard triplets to learn fine-grained differences across entities in the procedures, and b) a novel Mask-Step Modelling objective to learn step-wise context of a procedure. We test the performance of CLMSM on the downstream tasks of tracking entities and aligning actions between two procedures on three datasets, one of which is an open-domain dataset not conforming with the pre-training dataset. We show that CLMSM not only outperforms baselines on recipes (in-domain) but is also able to generalize to open-domain procedural NLP tasks.
The Law and NLP: Bridging Disciplinary Disconnects
Venue: arXiv cs.CL 2023
Year: 2023

Legal practice is intrinsically rooted in the fabric of language, yet legal practitioners and scholars have been slow to adopt tools from natural language processing (NLP). At the same time, the legal system is experiencing an access to justice crisis, which could be partially alleviated with NLP. In this position paper, we argue that the slow uptake of NLP in legal practice is exacerbated by a disconnect between the needs of the legal community and the focus of NLP researchers. In a review of recent trends in the legal NLP literature, we find limited overlap between the legal NLP community and legal academia. Our interpretation is that some of the most popular legal NLP tasks fail to address the needs of legal practitioners. We discuss examples of legal NLP tasks that promise to bridge disciplinary disconnects and highlight interesting areas for legal NLP research that remain underexplored.