pwc-archive/papers-with-abstracts huggingface dataset:
Are LLMs good pragmatic speakers?
Authors: Mingyue Jian, Siddharth Narayanaswamy
Date: 2024-11-03 00:00:00

Large language models (LLMs) are trained on data assumed to include natural language pragmatics, but do they actually behave like pragmatic speakers? We attempt to answer this question using the Rational Speech Act (RSA) framework, which models pragmatic reasoning in human communication. Using the paradigm of a reference game constructed from the TUNA corpus, we score candidate referential utterances in both a state-of-the-art LLM (Llama3-8B-Instruct) and in the RSA model, comparing and contrasting these scores. Given that RSA requires defining alternative utterances and a truth-conditional meaning function, we explore such comparison for different choices of each of these requirements. We find that while scores from the LLM have some positive correlation with those from RSA, there isn't sufficient evidence to claim that it behaves like a pragmatic speaker. This initial study paves way for further targeted efforts exploring different models and settings, including human-subject evaluation, to see if LLMs truly can, or be made to, behave like pragmatic speakers.
Learning to Communicate Through Implicit Communication Channels
Authors: Han Wang, Binbin Chen, Tieying Zhang, Baoxiang Wang
Date: 2024-11-03 00:00:00

Effective communication is an essential component in collaborative multi-agent systems. Situations where explicit messaging is not feasible have been common in human society throughout history, which motivate the study of implicit communication. Previous works on learning implicit communication mostly rely on theory of mind (ToM), where agents infer the mental states and intentions of others by interpreting their actions. However, ToM-based methods become less effective in making accurate inferences in complex tasks. In this work, we propose the Implicit Channel Protocol (ICP) framework, which allows agents to communicate through implicit communication channels similar to the explicit ones. ICP leverages a subset of actions, denoted as the scouting actions, and a mapping between information and these scouting actions that encodes and decodes the messages. We propose training algorithms for agents to message and act, including learning with a randomly initialized information map and with a delayed information map. The efficacy of ICP has been tested on the tasks of Guessing Numbers, Revealing Goals, and Hanabi, where ICP significantly outperforms baseline methods through more efficient information transmission.
Customized Subgraph Selection and Encoding for Drug-drug Interaction Prediction
Authors: Haotong Du, Quanming Yao, Juzheng Zhang, Yang Liu, Zhen Wang
Date: 2024-11-03 00:00:00
Tasks: Neural Architecture Search

Subgraph-based methods have proven to be effective and interpretable in predicting drug-drug interactions (DDIs), which are essential for medical practice and drug development. Subgraph selection and encoding are critical stages in these methods, yet customizing these components remains underexplored due to the high cost of manual adjustments. In this study, inspired by the success of neural architecture search (NAS), we propose a method to search for data-specific components within subgraph-based frameworks. Specifically, we introduce extensive subgraph selection and encoding spaces that account for the diverse contexts of drug interactions in DDI prediction. To address the challenge of large search spaces and high sampling costs, we design a relaxation mechanism that uses an approximation strategy to efficiently explore optimal subgraph configurations. This approach allows for robust exploration of the search space. Extensive experiments demonstrate the effectiveness and superiority of the proposed method, with the discovered subgraphs and encoding functions highlighting the model's adaptability.
Enhancing LLM Evaluations: The Garbling Trick
Authors: William F. Bradley
Date: 2024-11-03 00:00:00
Tasks: Multiple-choice

As large language models (LLMs) become increasingly powerful, traditional evaluation metrics tend to saturate, making it challenging to distinguish between models. We propose a general method to transform existing LLM evaluations into a series of progressively more difficult tasks. These enhanced evaluations emphasize reasoning capabilities and can reveal relative performance differences that are not apparent in the original assessments. To demonstrate the effectiveness of our approach, we create a new multiple-choice test corpus, extend it into a family of evaluations, and assess a collection of LLMs. Our results offer insights into the comparative abilities of these models, particularly highlighting the differences between base LLMs and more recent "reasoning" models.
SinaTools: Open Source Toolkit for Arabic Natural Language Processing
Authors: Tymaa Hammouda, Mustafa Jarrar, Mohammed Khalilia
Date: 2024-11-03 00:00:00
Tasks: Benchmarking, Lemmatization, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Nested Named Entity Recognition, Part-Of-Speech Tagging, POS, POS Tagging, Word Sense Disambiguation

We introduce SinaTools, an open-source Python package for Arabic natural language processing and understanding. SinaTools is a unified package allowing people to integrate it into their system workflow, offering solutions for various tasks such as flat and nested Named Entity Recognition (NER), fully-flagged Word Sense Disambiguation (WSD), Semantic Relatedness, Synonymy Extractions and Evaluation, Lemmatization, Part-of-speech Tagging, Root Tagging, and additional helper utilities such as corpus processing, text stripping methods, and diacritic-aware word matching. This paper presents SinaTools and its benchmarking results, demonstrating that SinaTools outperforms all similar tools on the aforementioned tasks, such as Flat NER (87.33%), Nested NER (89.42%), WSD (82.63%), Semantic Relatedness (0.49 Spearman rank), Lemmatization (90.5%), POS tagging (97.5%), among others. SinaTools can be downloaded from (https://sina.birzeit.edu/sinatools).
Diversity Progress for Goal Selection in Discriminability-Motivated RL
Authors: Erik M. Lintunen, Nadia M. Ady, Christian Guckelsberger
Date: 2024-11-03 00:00:00
Tasks: Diversity, Reinforcement Learning (RL)

Non-uniform goal selection has the potential to improve the reinforcement learning (RL) of skills over uniform-random selection. In this paper, we introduce a method for learning a goal-selection policy in intrinsically-motivated goal-conditioned RL: "Diversity Progress" (DP). The learner forms a curriculum based on observed improvement in discriminability over its set of goals. Our proposed method is applicable to the class of discriminability-motivated agents, where the intrinsic reward is computed as a function of the agent's certainty of following the true goal being pursued. This reward can motivate the agent to learn a set of diverse skills without extrinsic rewards. We demonstrate empirically that a DP-motivated agent can learn a set of distinguishable skills faster than previous approaches, and do so without suffering from a collapse of the goal distribution -- a known issue with some prior approaches. We end with plans to take this proof-of-concept forward.
FaceDig: Automated tool for placing landmarks on facial portraits for geometric morphometrics users
Authors: Karel Kleisner, Jaroslav Trnka, Petr Turecek
Date: 2024-11-03 00:00:00
Tasks: Morphological Analysis

Landmark digitization is essential in geometric morphometrics, enabling the quantification of biological shapes, such as facial structures, for in-depth morphological analysis. Traditional landmarking, which identifies specific anatomical points, can be complemented by semilandmarks when precise locations are challenging to define. However, manual placement of numerous landmarks is time-consuming and prone to human error, leading to inconsistencies across studies. To address this, we introduce FaceDig, an AI-powered tool designed to automate landmark placement with human-level precision, focusing on anatomically sound facial points. FaceDig is open-source and integrates seamlessly with analytical platforms like R and Python. It was trained using one of the largest and most ethnically diverse face datasets, applying a landmark configuration optimized for 2D enface photographs. Our results demonstrate that FaceDig provides reliable landmark coordinates, comparable to those placed manually by experts. The tool's output is compatible with the widely-used TpsDig2 software, facilitating adoption and ensuring consistency across studies. Users are advised to work with standardized facial images and visually inspect the results for potential corrections. Despite the growing preference for 3D morphometrics, 2D facial photographs remain valuable due to their cultural and practical significance. Future enhancements to FaceDig will include support for profile views, further expanding its utility. By offering a standardized approach to landmark placement, FaceDig promotes reproducibility in facial morphology research and provides a robust alternative to existing 2D tools.
Sample-Efficient Alignment for LLMs
Authors: Zichen Liu, Changyu Chen, Chao Du, Wee Sun Lee, Min Lin
Date: 2024-11-03 00:00:00
Tasks: Thompson Sampling

We study methods for efficiently aligning large language models (LLMs) with human preferences given budgeted online feedback. We first formulate the LLM alignment problem in the frame of contextual dueling bandits. This formulation, subsuming recent paradigms such as online RLHF and online DPO, inherently quests for sample-efficient algorithms that incorporate online active exploration. Leveraging insights from bandit theory, we introduce a unified algorithm based on Thompson sampling and highlight its applications in two distinct LLM alignment scenarios. The practical agent that efficiently implements this algorithm, named SEA (Sample-Efficient Alignment), is empirically validated through extensive experiments across three model scales (1B, 2.8B, 6.9B) and three preference learning algorithms (DPO, IPO, SLiC). The results demonstrate that SEA achieves highly sample-efficient alignment with oracle's preferences, outperforming recent active exploration methods for LLMs. Additionally, we release the implementation of SEA together with an efficient codebase designed for online alignment of LLMs, aiming to accelerate future research in this field.
Capsule Vision Challenge 2024: Multi-Class Abnormality Classification for Video Capsule Endoscopy
Authors: Aakarsh Bansal, Bhuvanesh Singla, Raajan Rajesh Wankhade, Nagamma Patil
Date: 2024-11-03 00:00:00

This study presents an approach to developing a model for classifying abnormalities in video capsule endoscopy (VCE) frames. Given the challenges of data imbalance, we implemented a tiered augmentation strategy using the albumentations library to enhance minority class representation. Additionally, we addressed learning complexities by progressively structuring training tasks, allowing the model to differentiate between normal and abnormal cases and then gradually adding more specific classes based on data availability. Our pipeline, developed in PyTorch, employs a flexible architecture enabling seamless adjustments to classification complexity. We tested our approach using ResNet50 and a custom ViT-CNN hybrid model, with training conducted on the Kaggle platform. This work demonstrates a scalable approach to abnormality classification in VCE.
Adaptive Domain Learning for Cross-domain Image Denoising
Authors: Zian Qian, Chenyang Qi, Ka Lung Law, Hao Fu, Chenyang Lei, Qifeng Chen
Date: 2024-11-03 00:00:00
Tasks: Denoising, Image Denoising

Different camera sensors have different noise patterns, and thus an image denoising model trained on one sensor often does not generalize well to a different sensor. One plausible solution is to collect a large dataset for each sensor for training or fine-tuning, which is inevitably time-consuming. To address this cross-domain challenge, we present a novel adaptive domain learning (ADL) scheme for cross-domain RAW image denoising by utilizing existing data from different sensors (source domain) plus a small amount of data from the new sensor (target domain). The ADL training scheme automatically removes the data in the source domain that are harmful to fine-tuning a model for the target domain (some data are harmful as adding them during training lowers the performance due to domain gaps). Also, we introduce a modulation module to adopt sensor-specific information (sensor type and ISO) to understand input data for image denoising. We conduct extensive experiments on public datasets with various smartphone and DSLR cameras, which show our proposed model outperforms prior work on cross-domain image denoising, given a small amount of image data from the target domain sensor.
Two-Timescale Model Caching and Resource Allocation for Edge-Enabled AI-Generated Content Services
Authors: Zhang Liu, Hongyang Du, Xiangwang Hou, Lianfen Huang, Seyyedali Hosseinalipour, Dusit Niyato, Khaled Ben Letaief
Date: 2024-11-03 00:00:00
Tasks: Deep Reinforcement Learning

Generative AI (GenAI) has emerged as a transformative technology, enabling customized and personalized AI-generated content (AIGC) services. In this paper, we address challenges of edge-enabled AIGC service provisioning, which remain underexplored in the literature. These services require executing GenAI models with billions of parameters, posing significant obstacles to resource-limited wireless edge. We subsequently introduce the formulation of joint model caching and resource allocation for AIGC services to balance a trade-off between AIGC quality and latency metrics. We obtain mathematical relationships of these metrics with the computational resources required by GenAI models via experimentation. Afterward, we decompose the formulation into a model caching subproblem on a long-timescale and a resource allocation subproblem on a short-timescale. Since the variables to be solved are discrete and continuous, respectively, we leverage a double deep Q-network (DDQN) algorithm to solve the former subproblem and propose a diffusion-based deep deterministic policy gradient (D3PG) algorithm to solve the latter. The proposed D3PG algorithm makes an innovative use of diffusion models as the actor network to determine optimal resource allocation decisions. Consequently, we integrate these two learning methods within the overarching two-timescale deep reinforcement learning (T2DRL) algorithm, the performance of which is studied through comparative numerical simulations.
Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis
Authors: Xishun Liao, Qinhua Jiang, Brian Yueshuai He, Yifan Liu, Chenchen Kuai, Jiaqi Ma
Date: 2024-05-24 00:00:00
Tasks: Deep Learning

Human mobility plays a crucial role in transportation, urban planning, and public health. Advances in deep learning and the availability of diverse mobility data have transformed mobility modeling. However, existing deep learning models often focus on spatio-temporal patterns and struggle to capture the semantic interdependencies among activities, while also being limited by specific data sources. These challenges reduce their realism and adaptability. Traditional activity-based models (ABMs) face issues as well, relying on rigid assumptions and requiring extensive data, making them costly and difficult to adapt to new regions, especially those with limited conventional travel data. To address these limitations, we develop a novel generative deep learning approach for human mobility modeling and synthesis that incorporates both activity patterns and location trajectories using open-source data. The model can be fine-tuned with local data, allowing it to adapt to and accurately represent mobility patterns across diverse regions. The model is evaluated on a nationwide dataset of the United States, where it demonstrates superior performance in generating activity-location chains that closely follow ground truth distributions. Further tests using state- or city-specific datasets from California, Washington, and Mexico City confirm its transferability. This innovative approach offers substantial potential to advance mobility modeling research, particularly in generating synthetic human mobility data. This can provide urban planners and policymakers with enhanced tools for simulating mobility in diverse regions and better informing decisions related to transportation, urban development, and public health.
Denoising Fisher Training For Neural Implicit Samplers
Authors: Weijian Luo, Wei Deng
Date: 2024-11-03 00:00:00
Tasks: Denoising

Efficient sampling from un-normalized target distributions is pivotal in scientific computing and machine learning. While neural samplers have demonstrated potential with a special emphasis on sampling efficiency, existing neural implicit samplers still have issues such as poor mode covering behavior, unstable training dynamics, and sub-optimal performances. To tackle these issues, in this paper, we introduce Denoising Fisher Training (DFT), a novel training approach for neural implicit samplers with theoretical guarantees. We frame the training problem as an objective of minimizing the Fisher divergence by deriving a tractable yet equivalent loss function, which marks a unique theoretical contribution to assessing the intractable Fisher divergences. DFT is empirically validated across diverse sampling benchmarks, including two-dimensional synthetic distribution, Bayesian logistic regression, and high-dimensional energy-based models (EBMs). Notably, in experiments with high-dimensional EBMs, our best one-step DFT neural sampler achieves results on par with MCMC methods with up to 200 sampling steps, leading to a substantially greater efficiency over 100 times higher. This result not only demonstrates the superior performance of DFT in handling complex high-dimensional sampling but also sheds light on efficient sampling methodologies across broader applications.
Online Relational Inference for Evolving Multi-agent Interacting Systems
Authors: Beomseok Kang, Priyabrata Saha, Sudarshan Sharma, Biswadeep Chakraborty, Saibal Mukhopadhyay
Date: 2024-11-03 00:00:00
Tasks: Data Augmentation, Decoder

We introduce a novel framework, Online Relational Inference (ORI), designed to efficiently identify hidden interaction graphs in evolving multi-agent interacting systems using streaming data. Unlike traditional offline methods that rely on a fixed training set, ORI employs online backpropagation, updating the model with each new data point, thereby allowing it to adapt to changing environments in real-time. A key innovation is the use of an adjacency matrix as a trainable parameter, optimized through a new adaptive learning rate technique called AdaRelation, which adjusts based on the historical sensitivity of the decoder to changes in the interaction graph. Additionally, a data augmentation method named Trajectory Mirror (TM) is introduced to improve generalization by exposing the model to varied trajectory patterns. Experimental results on both synthetic datasets and real-world data (CMU MoCap for human motion) demonstrate that ORI significantly improves the accuracy and adaptability of relational inference in dynamic settings compared to existing methods. This approach is model-agnostic, enabling seamless integration with various neural relational inference (NRI) architectures, and offers a robust solution for real-time applications in complex, evolving systems.
SkyServe: Serving AI Models across Regions and Clouds with Spot Instances
Authors: Ziming Mao, Tian Xia, Zhanghao Wu, Wei-Lin Chiang, Tyler Griggs, Romil Bhardwaj, Zongheng Yang, Scott Shenker, Ion Stoica
Date: 2024-11-03 00:00:00

Recent years have witnessed an explosive growth of AI models. The high cost of hosting AI services on GPUs and their demanding service requirements, make it timely and challenging to lower service costs and guarantee service quality. While spot instances have long been offered with a large discount, spot preemptions have discouraged users from using them to host model replicas when serving AI models. To address this, we propose a simple yet efficient policy, SpotHedge, that leverages spot replicas across different failure domains (e.g., regions and clouds) to ensure availability, lower costs, and high service quality. SpotHedge intelligently spreads spot replicas across different regions and clouds to improve availability and reduce correlated preemptions, overprovisions cheap spot replicas than required as a safeguard against possible preemptions, and dynamically falls back to on-demand replicas when spot replicas become unavailable. We built SkyServe, a system leveraging SpotHedge to efficiently serve AI models over a mixture of spot and on-demand replicas across regions and clouds. We compared SkyServe with both research and production systems on real AI workloads: SkyServe reduces cost by 43% on average while achieving high resource availability compared to using on-demand replicas. Additionally, SkyServe improves P50, P90, and P99 latency by 2.3$\times$, 2.1$\times$, 2.1$\times$ on average compared to other research and production systems.
Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision
Authors: Xiangzhong Luo, Di Liu, Hao Kong, Shuo Huai, Hui Chen, Guochu Xiong, Weichen Liu
Date: 2024-11-03 00:00:00
Tasks: Deep Learning, image-classification, Image Classification, object-detection, Object Detection

Deep neural networks (DNNs) have recently achieved impressive success across a wide range of real-world vision and language processing tasks, spanning from image classification to many other downstream vision tasks, such as object detection, tracking, and segmentation. However, previous well-established DNNs, despite being able to maintain superior accuracy, have also been evolving to be deeper and wider and thus inevitably necessitate prohibitive computational resources for both training and inference. This trend further enlarges the computational gap between computation-intensive DNNs and resource-constrained embedded computing systems, making it challenging to deploy powerful DNNs upon real-world embedded computing systems towards ubiquitous embedded intelligence. To alleviate the above computational gap and enable ubiquitous embedded intelligence, we, in this survey, focus on discussing recent efficient deep learning infrastructures for embedded computing systems, spanning from training to inference, from manual to automated, from convolutional neural networks to transformers, from transformers to vision transformers, from vision models to large language models, from software to hardware, and from algorithms to applications. Specifically, we discuss recent efficient deep learning infrastructures for embedded computing systems from the lens of (1) efficient manual network design for embedded computing systems, (2) efficient automated network design for embedded computing systems, (3) efficient network compression for embedded computing systems, (4) efficient on-device learning for embedded computing systems, (5) efficient large language models for embedded computing systems, (6) efficient deep learning software and hardware for embedded computing systems, and (7) efficient intelligent applications for embedded computing systems.