pwc-archive/papers-with-abstracts huggingface dataset:
STEER: Unified Style Transfer with Expert Reinforcement
Authors: Skyler Hallinan, Faeze Brahman, Ximing Lu, JaeHun Jung, Sean Welleck, Yejin Choi
Date: 2023-11-13 00:00:00
Tasks: Style Transfer, Text Style Transfer

While text style transfer has many applications across natural language processing, the core premise of transferring from a single source style is unrealistic in a real-world setting. In this work, we focus on arbitrary style transfer: rewriting a text from an arbitrary, unknown style to a target style. We propose STEER: Unified Style Transfer with Expert Reinforcement, a unified frame-work developed to overcome the challenge of limited parallel data for style transfer. STEER involves automatically generating a corpus of style-transfer pairs using a product of experts during decoding. The generated offline data is then used to pre-train an initial policy before switching to online, off-policy reinforcement learning for further improvements via fine-grained reward signals. STEER is unified and can transfer to multiple target styles from an arbitrary, unknown source style, making it particularly flexible and efficient. Experimental results on a challenging dataset with text from a diverse set of styles demonstrate state-of-the-art results compared to competitive baselines. Remarkably, STEER outperforms the 175B parameter instruction-tuned GPT-3 on overall style transfer quality, despite being 226 times smaller in size. We also show STEER is robust, maintaining its style transfer capabilities on out-of-domain data, and surpassing nearly all baselines across various styles. The success of our method highlights the potential of RL algorithms when augmented with controllable decoding to overcome the challenge of limited data supervision.
Pruning random resistive memory for optimizing analogue AI
Authors: Yi Li, Songqi Wang, Yaping Zhao, Shaocong Wang, Woyu Zhang, Yangu He, Ning Lin, Binbin Cui, Xi Chen, Shiming Zhang, Hao Jiang, Peng Lin, Xumeng Zhang, Xiaojuan Qi, Zhongrui Wang, Xiaoxin Xu, Dashan Shang, Qi Liu, Kwang-Ting Cheng, Ming Liu
Date: 2023-11-13 00:00:00
Tasks: Audio Classification, Image Segmentation, Semantic Segmentation

The rapid advancement of artificial intelligence (AI) has been marked by the large language models exhibiting human-like intelligence. However, these models also present unprecedented challenges to energy consumption and environmental sustainability. One promising solution is to revisit analogue computing, a technique that predates digital computing and exploits emerging analogue electronic devices, such as resistive memory, which features in-memory computing, high scalability, and nonvolatility. However, analogue computing still faces the same challenges as before: programming nonidealities and expensive programming due to the underlying devices physics. Here, we report a universal solution, software-hardware co-design using structural plasticity-inspired edge pruning to optimize the topology of a randomly weighted analogue resistive memory neural network. Software-wise, the topology of a randomly weighted neural network is optimized by pruning connections rather than precisely tuning resistive memory weights. Hardware-wise, we reveal the physical origin of the programming stochasticity using transmission electron microscopy, which is leveraged for large-scale and low-cost implementation of an overparameterized random neural network containing high-performance sub-networks. We implemented the co-design on a 40nm 256K resistive memory macro, observing 17.3% and 19.9% accuracy improvements in image and audio classification on FashionMNIST and Spoken digits datasets, as well as 9.8% (2%) improvement in PR (ROC) in image segmentation on DRIVE datasets, respectively. This is accompanied by 82.1%, 51.2%, and 99.8% improvement in energy efficiency thanks to analogue in-memory computing. By embracing the intrinsic stochasticity and in-memory computing, this work may solve the biggest obstacle of analogue computing systems and thus unleash their immense potential for next-generation AI hardware.
Enhancing Lightweight Neural Networks for Small Object Detection in IoT Applications
Authors: Liam Boyle, Nicolas Baumann, Seonyeong Heo, Michele Magno
Date: 2023-11-13 00:00:00
Tasks: Object, object-detection, Object Detection, Small Object Detection

Advances in lightweight neural networks have revolutionized computer vision in a broad range of IoT applications, encompassing remote monitoring and process automation. However, the detection of small objects, which is crucial for many of these applications, remains an underexplored area in current computer vision research, particularly for embedded devices. To address this gap, the paper proposes a novel adaptive tiling method that can be used on top of any existing object detector including the popular FOMO network for object detection on microcontrollers. Our experimental results show that the proposed tiling method can boost the F1-score by up to 225% while reducing the average object count error by up to 76%. Furthermore, the findings of this work suggest that using a soft F1 loss over the popular binary cross-entropy loss can significantly reduce the negative impact of imbalanced data. Finally, we validate our approach by conducting experiments on the Sony Spresense microcontroller, showcasing the proposed method's ability to strike a balance between detection performance, low latency, and minimal memory consumption.
Interaction is all You Need? A Study of Robots Ability to Understand and Execute
Authors: Kushal Koshti, Nidhir Bhavsar
Date: 2023-11-13 00:00:00
Tasks: All

This paper aims to address a critical challenge in robotics, which is enabling them to operate seamlessly in human environments through natural language interactions. Our primary focus is to equip robots with the ability to understand and execute complex instructions in coherent dialogs to facilitate intricate task-solving scenarios. To explore this, we build upon the Execution from Dialog History (EDH) task from the Teach benchmark. We employ a multi-transformer model with BART LM. We observe that our best configuration outperforms the baseline with a success rate score of 8.85 and a goal-conditioned success rate score of 14.02. In addition, we suggest an alternative methodology for completing this task. Moreover, we introduce a new task by expanding the EDH task and making predictions about game plans instead of individual actions. We have evaluated multiple BART models and an LLaMA2 LLM, which has achieved a ROGUE-L score of 46.77 for this task.
Analyzing and Predicting Low-Listenership Trends in a Large-Scale Mobile Health Program: A Preliminary Investigation
Authors: Arshika Lalan, Shresth Verma, Kumar Madhu Sudan, Amrita Mahale, Aparna Hegde, Milind Tambe, Aparna Taneja
Date: 2023-11-13 00:00:00
Tasks: Time Series, Time Series Prediction

Mobile health programs are becoming an increasingly popular medium for dissemination of health information among beneficiaries in less privileged communities. Kilkari is one of the world's largest mobile health programs which delivers time sensitive audio-messages to pregnant women and new mothers. We have been collaborating with ARMMAN, a non-profit in India which operates the Kilkari program, to identify bottlenecks to improve the efficiency of the program. In particular, we provide an initial analysis of the trajectories of beneficiaries' interaction with the mHealth program and examine elements of the program that can be potentially enhanced to boost its success. We cluster the cohort into different buckets based on listenership so as to analyze listenership patterns for each group that could help boost program success. We also demonstrate preliminary results on using historical data in a time-series prediction to identify beneficiary dropouts and enable NGOs in devising timely interventions to strengthen beneficiary retention.
Understanding Path Planning Explanations
Authors: Amar Halilovic, Senka Krivic
Date: 2023-11-13 00:00:00

Navigation is a must-have skill for any mobile robot. A core challenge in navigation is the need to account for an ample number of possible configurations of environment and navigation contexts. We claim that a mobile robot should be able to explain its navigational choices making its decisions understandable to humans. In this paper, we briefly present our approach to explaining navigational decisions of a robot through visual and textual explanations. We propose a user study to test the understandability and simplicity of the robot explanations and outline our further research agenda.
Multi-agent Attacks for Black-box Social Recommendations
Authors: Shijie Wang, Wenqi Fan, Xiao-Yong Wei, Xiaowei Mei, Shanru Lin, Qing Li
Date: 2023-11-13 00:00:00
Tasks: Decision Making, Multi-agent Reinforcement Learning, Recommendation Systems

The rise of online social networks has facilitated the evolution of social recommender systems, which incorporate social relations to enhance users' decision-making process. With the great success of Graph Neural Networks (GNNs) in learning node representations, GNN-based social recommendations have been widely studied to model user-item interactions and user-user social relations simultaneously. Despite their great successes, recent studies have shown that these advanced recommender systems are highly vulnerable to adversarial attacks, in which attackers can inject well-designed fake user profiles to disrupt recommendation performances. While most existing studies mainly focus on argeted attacks to promote target items on vanilla recommender systems, untargeted attacks to degrade the overall prediction performance are less explored on social recommendations under a black-box scenario. To perform untargeted attacks on social recommender systems, attackers can construct malicious social relationships for fake users to enhance the attack performance. However, the coordination of social relations and item profiles is challenging for attacking black-box social recommendations. To address this limitation, we first conduct several preliminary studies to demonstrate the effectiveness of cross-community connections and cold-start items in degrading recommendations performance. Specifically, we propose a novel framework MultiAttack based on multi-agent reinforcement learning to coordinate the generation of cold-start item profiles and cross-community social relations for conducting untargeted attacks on black-box social recommendations. Comprehensive experiments on various real-world datasets demonstrate the effectiveness of our proposed attacking framework under the black-box setting.
Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning
Authors: Yue Yu, Jiaming Shen, Tianqi Liu, Zhen Qin, Jing Nathan Yan, Jialu Liu, Chao Zhang, Michael Bendersky
Date: 2023-11-13 00:00:00
Tasks: In-Context Learning, Language Modeling, Language Modelling, Large Language Model, Natural Language Understanding

Large language models (LLMs) have shown remarkable capabilities in various natural language understanding tasks. With only a few demonstration examples, these LLMs can quickly adapt to target tasks without expensive gradient updates. Common strategies to boost such 'in-context' learning ability are to ensemble multiple model decoded results and require the model to generate an explanation along with the prediction. However, these models often treat different class predictions equally and neglect the potential discrepancy between the explanations and predictions. To fully unleash the power of explanations, we propose EASE, an Explanation-Aware Soft Ensemble framework to empower in-context learning with LLMs. We design two techniques, explanation-guided ensemble, and soft probability aggregation, to mitigate the effect of unreliable explanations and improve the consistency between explanations and final predictions. Experiments on seven natural language understanding tasks and four varying-size LLMs demonstrate the effectiveness of our proposed framework.
To Tell The Truth: Language of Deception and Language Models
Authors: Sanchaita Hazra, Bodhisattwa Prasad Majumder
Date: 2023-11-13 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Misinformation

Text-based misinformation permeates online discourses, yet evidence of people's ability to discern truth from such deceptive textual content is scarce. We analyze a novel TV game show data where conversations in a high-stake environment between individuals with conflicting objectives result in lies. We investigate the manifestation of potentially verifiable language cues of deception in the presence of objective truth, a distinguishing feature absent in previous text-based deception datasets. We show that there exists a class of detectors (algorithms) that have similar truth detection performance compared to human subjects, even when the former accesses only the language cues while the latter engages in conversations with complete access to all potential sources of cues (language and audio-visual). Our model, built on a large language model, employs a bottleneck framework to learn discernible cues to determine truth, an act of reasoning in which human subjects often perform poorly, even with incentives. Our model detects novel but accurate language cues in many cases where humans failed to detect deception, opening up the possibility of humans collaborating with algorithms and ameliorating their ability to detect the truth.
Sample Dominance Aware Framework via Non-Parametric Estimation for Spontaneous Brain-Computer Interface
Authors: Byeong-Hoo Lee, Byoung-Hee Kwon, Seong-Whan Lee
Date: 2023-11-13 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG)

Deep learning has shown promise in decoding brain signals, such as electroencephalogram (EEG), in the field of brain-computer interfaces (BCIs). However, the non-stationary characteristics of EEG signals pose challenges for training neural networks to acquire appropriate knowledge. Inconsistent EEG signals resulting from these non-stationary characteristics can lead to poor performance. Therefore, it is crucial to investigate and address sample inconsistency to ensure robust performance in spontaneous BCIs. In this study, we introduce the concept of sample dominance as a measure of EEG signal inconsistency and propose a method to modulate its effect on network training. We present a two-stage dominance score estimation technique that compensates for performance degradation caused by sample inconsistencies. Our proposed method utilizes non-parametric estimation to infer sample inconsistency and assigns each sample a dominance score. This score is then aggregated with the loss function during training to modulate the impact of sample inconsistency. Furthermore, we design a curriculum learning approach that gradually increases the influence of inconsistent signals during training to improve overall performance. We evaluate our proposed method using public spontaneous BCI dataset. The experimental results confirm that our findings highlight the importance of addressing sample dominance for achieving robust performance in spontaneous BCIs.
The Impact of Generative Artificial Intelligence on Market Equilibrium: Evidence from a Natural Experiment
Authors: Kaichen Zhang, Zixuan Yuan, Hui Xiong
Date: 2023-11-13 00:00:00
Tasks: Causal Inference

Generative artificial intelligence (AI) exhibits the capability to generate creative content akin to human output with greater efficiency and reduced costs. This groundbreaking capability, however, has ignited a debate regarding its potential to displace human creators. In light of these discussions, this paper empirically investigates the impact of generative AI on market equilibrium, in the context of China's leading art outsourcing platform. We overcome the challenge of causal inference by identifying an unanticipated and sudden leak of an advanced image-generative AI as a natural experiment. This leak precipitated a notable reduction in the production costs of anime-style images compared to other genres, thereby providing a unique opportunity for difference-in-differences comparisons. Our analysis shows that the advent of generative AI led to a 64% reduction in average prices, yet it simultaneously spurred a 121% increase in order volume and a 56% increase in overall revenue. This growth is primarily driven by the rising demand for "low-end" personal orders, rather than commercial orders. Moreover, incumbent creators retain the majority of the market share and reap the most benefits of generative AI. Our research highlights the potential of generative AI to benefit all stakeholders across the platform economy, yielding both scholarly contributions and practical implications.
On non-approximability of zero loss global ${\mathcal L}^2$ minimizers by gradient descent in Deep Learning
Authors: Thomas Chen, Patricia Muñoz Ewald
Date: 2023-11-13 00:00:00
Tasks: Clustering

We analyze geometric aspects of the gradient descent algorithm in Deep Learning (DL), and give a detailed discussion of the circumstance that in underparametrized DL networks, zero loss minimization can generically not be attained. As a consequence, we conclude that the distribution of training inputs must necessarily be non-generic in order to produce zero loss minimizers, both for the method constructed in [Chen-Munoz Ewald 2023, 2024], or for gradient descent [Chen 2025] (which assume clustering of training data).
Towards the Law of Capacity Gap in Distilling Language Models
Authors: Chen Zhang, Dawei Song, Zheyu Ye, Yan Gao
Date: 2023-11-13 00:00:00
Tasks: Language Modelling

Language model (LM) distillation is a trending area that aims to distil the knowledge residing in a large teacher LM to a small student one. While various methods have been proposed to maximize the effectiveness of the distillation, significant challenges persist, particularly when there is a substantial capacity gap between the teacher and student LMs. This issue, often referred to as the \textit{curse} of capacity gap, suggests that a larger teacher does not necessarily result in a superior student compared to one distilled from a smaller teacher. In other words, there is likely an optimal teacher yielding the best student along the scaling course of the teacher. However, the curse of capacity gap can not be tackled without notable compute overhead, as indicated in previous studies. In the context of large LMs (LLMs), previously viable approaches become much less meaningful, as it is an impossible triangle to distill an expected student from an optimal teacher student with small compute overhead. Fortunately, the impossible triangle can fortunately be possible provided an inducted \textit{law} of capacity gap. In this paper, we take the spirits of scaling law and reveal that the optimal teacher scale almost consistently follows a linear scaling with the student scale across different model architectures and data scales. The law later guides us to distil a 3B student LM (termed \textsc{MiniMA}) from LLaMA2-7B. \textsc{MiniMA} is demonstrated to outperform a wide range of 3B competitors and could even compete with several 7B models.
Phonological Level wav2vec2-based Mispronunciation Detection and Diagnosis Method
Authors: Mostafa Shahin, Julien Epps, Beena Ahmed
Date: 2023-11-13 00:00:00
Tasks: Attribute, Diagnostic

The automatic identification and analysis of pronunciation errors, known as Mispronunciation Detection and Diagnosis (MDD) plays a crucial role in Computer Aided Pronunciation Learning (CAPL) tools such as Second-Language (L2) learning or speech therapy applications. Existing MDD methods relying on analysing phonemes can only detect categorical errors of phonemes that have an adequate amount of training data to be modelled. With the unpredictable nature of the pronunciation errors of non-native or disordered speakers and the scarcity of training datasets, it is unfeasible to model all types of mispronunciations. Moreover, phoneme-level MDD approaches have a limited ability to provide detailed diagnostic information about the error made. In this paper, we propose a low-level MDD approach based on the detection of speech attribute features. Speech attribute features break down phoneme production into elementary components that are directly related to the articulatory system leading to more formative feedback to the learner. We further propose a multi-label variant of the Connectionist Temporal Classification (CTC) approach to jointly model the non-mutually exclusive speech attributes using a single model. The pre-trained wav2vec2 model was employed as a core model for the speech attribute detector. The proposed method was applied to L2 speech corpora collected from English learners from different native languages. The proposed speech attribute MDD method was further compared to the traditional phoneme-level MDD and achieved a significantly lower False Acceptance Rate (FAR), False Rejection Rate (FRR), and Diagnostic Error Rate (DER) over all speech attributes compared to the phoneme-level equivalent.
ExpNote: Black-box Large Language Models are Better Task Solvers with Experience Notebook
Authors: Wangtao Sun, Xuanqing Yu, Shizhu He, Jun Zhao, Kang Liu
Date: 2023-11-13 00:00:00

Black-box Large Language Models (LLMs) have shown great power in solving various tasks and are considered general problem solvers. However, LLMs still fail in many specific tasks although understand the task instruction. In this paper, we focus on the problem of boosting the ability of black-box LLMs to solve downstream tasks. We propose ExpNote, an automated framework to help LLMs better adapt to unfamiliar tasks through reflecting and noting experiences from training data and retrieving them from external memory during testing. We evaluate ExpNote on multiple tasks and the experimental results demonstrate that the proposed method significantly improves the performance of black-box LLMs. The data and code are available at https://github.com/forangel2014/ExpNote
ViLMA: A Zero-Shot Benchmark for Linguistic and Temporal Grounding in Video-Language Models
Authors: Ilker Kesen, Andrea Pedrotti, Mustafa Dogan, Michele Cafagna, Emre Can Acikgoz, Letitia Parcalabescu, Iacer Calixto, Anette Frank, Albert Gatt, Aykut Erdem, Erkut Erdem
Date: 2023-11-13 00:00:00
Tasks: counterfactual, Language Modeling, Language Modelling

With the ever-increasing popularity of pretrained Video-Language Models (VidLMs), there is a pressing need to develop robust evaluation methodologies that delve deeper into their visio-linguistic capabilities. To address this challenge, we present ViLMA (Video Language Model Assessment), a task-agnostic benchmark that places the assessment of fine-grained capabilities of these models on a firm footing. Task-based evaluations, while valuable, fail to capture the complexities and specific temporal aspects of moving images that VidLMs need to process. Through carefully curated counterfactuals, ViLMA offers a controlled evaluation suite that sheds light on the true potential of these models, as well as their performance gaps compared to human-level understanding. ViLMA also includes proficiency tests, which assess basic capabilities deemed essential to solving the main counterfactual tests. We show that current VidLMs' grounding abilities are no better than those of vision-language models which use static images. This is especially striking once the performance on proficiency tests is factored in. Our benchmark serves as a catalyst for future research on VidLMs, helping to highlight areas that still need to be explored.