pwc-archive/papers-with-abstracts huggingface dataset:
Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Merging
Authors: Joel Jang, Seungone Kim, Bill Yuchen Lin, Yizhong Wang, Jack Hessel, Luke Zettlemoyer, Hannaneh Hajishirzi, Yejin Choi, Prithviraj Ammanabrolu
Date: 2023-10-17 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Multi-Objective Reinforcement Learning, reinforcement-learning, Reinforcement Learning

While Reinforcement Learning from Human Feedback (RLHF) aligns Large Language Models (LLMs) with general, aggregate human preferences, it is suboptimal for learning diverse, individual perspectives. In this work, we study Reinforcement Learning from Personalized Human Feedback (RLPHF) problem, wherein LLMs are aligned to multiple (sometimes conflicting) preferences by modeling alignment as a Multi-Objective Reinforcement Learning (MORL) problem. Compared to strong single-objective baselines, we show that we can achieve personalized alignment by decomposing preferences into multiple dimensions. These dimensions are defined based on personalizations that are declared as desirable by the user. In this work, we show that they can be efficiently trained independently in a distributed manner and combined effectively post-hoc through parameter merging. The code is available at https://github.com/joeljang/RLPHF.
Multi-stage Large Language Model Correction for Speech Recognition
Authors: Jie Pu, Thai-Son Nguyen, Sebastian Stüker
Date: 2023-10-17 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, speech-recognition, Speech Recognition

In this paper, we investigate the usage of large language models (LLMs) to improve the performance of competitive speech recognition systems. Different from previous LLM-based ASR error correction methods, we propose a novel multi-stage approach that utilizes uncertainty estimation of ASR outputs and reasoning capability of LLMs. Specifically, the proposed approach has two stages: the first stage is about ASR uncertainty estimation and exploits N-best list hypotheses to identify less reliable transcriptions; The second stage works on these identified transcriptions and performs LLM-based corrections. This correction task is formulated as a multi-step rule-based LLM reasoning process, which uses explicitly written rules in prompts to decompose the task into concrete reasoning steps. Our experimental results demonstrate the effectiveness of the proposed method by showing 10% ~ 20% relative improvement in WER over competitive ASR systems -- across multiple test domains and in zero-shot settings.
Online Estimation with Rolling Validation: Adaptive Nonparametric Estimation with Streaming Data
Authors: Tianyu Zhang, Jing Lei
Date: 2023-10-18 00:00:00
Tasks: Model Selection

Online nonparametric estimators are gaining popularity due to their efficient computation and competitive generalization abilities. An important example includes variants of stochastic gradient descent. These algorithms often take one sample point at a time and incrementally update the parameter estimate of interest. In this work, we consider model selection/hyperparameter tuning for such online algorithms. We propose a weighted rolling validation procedure, an online variant of leave-one-out cross-validation, that costs minimal extra computation for many typical stochastic gradient descent estimators and maintains their online nature. Similar to batch cross-validation, it can boost base estimators to achieve better heuristic performance and adaptive convergence rate. Our analysis is straightforward, relying mainly on some general statistical stability assumptions. The simulation study underscores the significance of diverging weights in practice and demonstrates its favorable sensitivity even when there is only a slim difference between candidate estimators.
A connection between Tempering and Entropic Mirror Descent
Authors: Nicolas Chopin, Francesca R. Crucinio, Anna Korba
Date: 2023-10-18 00:00:00

This paper explores the connections between tempering (for Sequential Monte Carlo; SMC) and entropic mirror descent to sample from a target probability distribution whose unnormalized density is known. We establish that tempering SMC corresponds to entropic mirror descent applied to the reverse Kullback-Leibler (KL) divergence and obtain convergence rates for the tempering iterates. Our result motivates the tempering iterates from an optimization point of view, showing that tempering can be seen as a descent scheme of the KL divergence with respect to the Fisher-Rao geometry, in contrast to Langevin dynamics that perform descent of the KL with respect to the Wasserstein-2 geometry. We exploit the connection between tempering and mirror descent iterates to justify common practices in SMC and derive adaptive tempering rules that improve over other alternative benchmarks in the literature.
Layered Models can "Automatically" Regularize and Discover Low-Dimensional Structures via Feature Learning
Authors: Yunlu Chen, Yang Li, Keli Liu, Feng Ruan
Date: 2023-10-18 00:00:00
Tasks: Dimensionality Reduction, regression

Layered models like neural networks appear to extract key features from data through empirical risk minimization, yet the theoretical understanding for this process remains unclear. Motivated by these observations, we study a two-layer nonparametric regression model where the input undergoes a linear transformation followed by a nonlinear mapping to predict the output, mirroring the structure of two-layer neural networks. In our model, both layers are optimized jointly through empirical risk minimization, with the nonlinear layer modeled by a reproducing kernel Hilbert space induced by a rotation and translation invariant kernel, regularized by a ridge penalty. Our main result shows that the two-layer model can "automatically" induce regularization and facilitate feature learning. Specifically, the two-layer model promotes dimensionality reduction in the linear layer and identifies a parsimonious subspace of relevant features -- even without applying any norm penalty on the linear layer. Notably, this regularization effect arises directly from the model's layered structure, independent of optimization dynamics. More precisely, assuming the covariates have nonzero explanatory power for the response only through a low dimensional subspace (central mean subspace), the linear layer consistently estimates both the subspace and its dimension. This demonstrates that layered models can inherently discover low-complexity solutions relevant for prediction, without relying on conventional regularization methods. Real-world data experiments further demonstrate the persistence of this phenomenon in practice.
Automated Attribute Extraction from Legal Proceedings
Authors: Subinay Adhikary, Sagnik Das, Sagnik Saha, Procheta Sen, Dwaipayan Roy, Kripabandhu Ghosh
Date: 2023-10-18 00:00:00
Tasks: Attribute, Attribute Extraction

The escalating number of pending cases is a growing concern world-wide. Recent advancements in digitization have opened up possibilities for leveraging artificial intelligence (AI) tools in the processing of legal documents. Adopting a structured representation for legal documents, as opposed to a mere bag-of-words flat text representation, can significantly enhance processing capabilities. With the aim of achieving this objective, we put forward a set of diverse attributes for criminal case proceedings. We use a state-of-the-art sequence labeling framework to automatically extract attributes from the legal documents. Moreover, we demonstrate the efficacy of the extracted attributes in a downstream task, namely legal judgment prediction.
Simulating Users in Interactive Web Table Retrieval
Authors: Björn Engelmann, Timo Breuer, Philipp Schaer
Date: 2023-10-18 00:00:00
Tasks: Retrieval, Table Retrieval

Considering the multimodal signals of search items is beneficial for retrieval effectiveness. Especially in web table retrieval (WTR) experiments, accounting for multimodal properties of tables boosts effectiveness. However, it still remains an open question how the single modalities affect user experience in particular. Previous work analyzed WTR performance in ad-hoc retrieval benchmarks, which neglects interactive search behavior and limits the conclusion about the implications for real-world user environments. To this end, this work presents an in-depth evaluation of simulated interactive WTR search sessions as a more cost-efficient and reproducible alternative to real user studies. As a first of its kind, we introduce interactive query reformulation strategies based on Doc2Query, incorporating cognitive states of simulated user knowledge. Our evaluations include two perspectives on user effectiveness by considering different cost paradigms, namely query-wise and time-oriented measures of effort. Our multi-perspective evaluation scheme reveals new insights about query strategies, the impact of modalities, and different user types in simulated WTR search sessions.
CIR at the NTCIR-17 ULTRE-2 Task
Authors: Lulu Yu, Keping Bi, Jiafeng Guo, Xueqi Cheng
Date: 2023-10-18 00:00:00
Tasks: Information Retrieval, Position, Retrieval

The Chinese academy of sciences Information Retrieval team (CIR) has participated in the NTCIR-17 ULTRE-2 task. This paper describes our approaches and reports our results on the ULTRE-2 task. We recognize the issue of false negatives in the Baidu search data in this competition is very severe, much more severe than position bias. Hence, we adopt the Dual Learning Algorithm (DLA) to address the position bias and use it as an auxiliary model to study how to alleviate the false negative issue. We approach the problem from two perspectives: 1) correcting the labels for non-clicked items by a relevance judgment model trained from DLA, and learn a new ranker that is initialized from DLA; 2) including random documents as true negatives and documents that have partial matching as hard negatives. Both methods can enhance the model performance and our best method has achieved nDCG@10 of 0.5355, which is 2.66% better than the best score from the organizer.
DCRNN: A Deep Cross approach based on RNN for Partial Parameter Sharing in Multi-task Learning
Authors: Jie zhou, Qian Yu
Date: 2023-10-18 00:00:00
Tasks: Multi-Task Learning, Recommendation Systems

In recent years, DL has developed rapidly, and personalized services are exploring using DL algorithms to improve the performance of the recommendation system. For personalized services, a successful recommendation consists of two parts: attracting users to click the item and users being willing to consume the item. If both tasks need to be predicted at the same time, traditional recommendation systems generally train two independent models. This approach is cumbersome and does not effectively model the relationship between the two subtasks of "click-consumption". Therefore, in order to improve the success rate of recommendation and reduce computational costs, researchers are trying to model multi-task learning. At present, existing multi-task learning models generally adopt hard parameter sharing or soft parameter sharing architecture, but these two architectures each have certain problems. Therefore, in this work, we propose a novel recommendation model based on real recommendation scenarios, Deep Cross network based on RNN for partial parameter sharing (DCRNN). The model has three innovations: 1) It adopts the idea of cross network and uses RNN network to cross-process the features, thereby effectively improves the expressive ability of the model; 2) It innovatively proposes the structure of partial parameter sharing; 3) It can effectively capture the potential correlation between different tasks to optimize the efficiency and methods for learning different tasks.
From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification
Authors: Hengran Zhang, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yixing Fan, Xueqi Cheng
Date: 2023-10-18 00:00:00
Tasks: Fact Verification, Retrieval

Retrieval-enhanced methods have become a primary approach in fact verification (FV); it requires reasoning over multiple retrieved pieces of evidence to verify the integrity of a claim. To retrieve evidence, existing work often employs off-the-shelf retrieval models whose design is based on the probability ranking principle. We argue that, rather than relevance, for FV we need to focus on the utility that a claim verifier derives from the retrieved evidence. We introduce the feedback-based evidence retriever(FER) that optimizes the evidence retrieval process by incorporating feedback from the claim verifier. As a feedback signal we use the divergence in utility between how effectively the verifier utilizes the retrieved evidence and the ground-truth evidence to produce the final claim label. Empirical studies demonstrate the superiority of FER over prevailing baselines.
Open Information Extraction: A Review of Baseline Techniques, Approaches, and Applications
Authors: Serafina Kamp, Morteza Fayazi, Zineb Benameur-El, Shuyan Yu, Ronald Dreslinski
Date: 2023-10-18 00:00:00
Tasks: Open Information Extraction, Question Answering, Relation, Relation Extraction, Text Summarization

With the abundant amount of available online and offline text data, there arises a crucial need to extract the relation between phrases and summarize the main content of each document in a few words. For this purpose, there have been many studies recently in Open Information Extraction (OIE). OIE improves upon relation extraction techniques by analyzing relations across different domains and avoids requiring hand-labeling pre-specified relations in sentences. This paper surveys recent approaches of OIE and its applications on Knowledge Graph (KG), text summarization, and Question Answering (QA). Moreover, the paper describes OIE basis methods in relation extraction. It briefly discusses the main approaches and the pros and cons of each method. Finally, it gives an overview about challenges, open issues, and future work opportunities for OIE, relation extraction, and OIE applications.
A Consensus-Based Generalized Multi-Population Aggregative Game with Application to Charging Coordination of Electric Vehicles
Authors: Mahsa Ghavami, Babak Ghaffarzadeh Bakhshayesh, Mohammad Haeri, Giacomo Como, Hamed Kebriaei
Date: 2023-10-18 00:00:00

This paper introduces a consensus-based generalized multi-population aggregative game coordination approach with application to electric vehicles charging under transmission line constraints. The algorithm enables agents to seek an equilibrium solution while considering the limited infrastructure capacities that impose coupling constraints among the users. The Nash-seeking algorithm consists of two interrelated iterations. In the upper layer, population coordinators collaborate for a distributed estimation of the coupling aggregate term in the agents' cost function and the associated Lagrange multiplier of the coupling constraint, transmitting the latest updated values to their population's agents. In the lower layer, each agent updates its best response based on the most recent information received and communicates it back to its population coordinator. For the case when the agents' best response mappings are non-expansive, we prove the algorithm's convergence to the generalized Nash equilibrium point of the game. Simulation results demonstrate the algorithm's effectiveness in achieving equilibrium in the presence of a coupling constraint.
Performance Investigation of an Optimal Control Strategy for Zero-Emission Operations of Shipboard Microgrids
Authors: Fabio D'Agostino, Marco Gallo, Matteo Saviozzi, Federico Silvestro
Date: 2023-10-18 00:00:00
Tasks: Management

This work introduces an efficient power management approach for shipboard microgrids that integrates diesel generators, a fuel cell, and battery energy storage system. This strategy addresses both unit commitment and power dispatch, considering the zero-emission capability of the ship, as well as optimizing the ship's speed. The optimization is done through mixed integer linear programming with the objective of minimizing the operational cost of all the power resources. Evaluations are conducted on a notional all-electric ship, with electrical load simulated using a Markov chain based on actual measurement data. The findings underscore the effectiveness of the proposed strategy in optimizing fuel consumption while ensuring protection against blackout occurrences.
NeuroQuantify -- An Image Analysis Software for Detection and Quantification of Neurons and Neurites using Deep Learning
Authors: Ka My Dang, Yi Jia Zhang, Tianchen Zhang, Chao Wang, Anton Sinner, Piero Coronica, Joyce K. S. Poon
Date: 2023-10-16 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

The segmentation of cells and neurites in microscopy images of neuronal networks provides valuable quantitative information about neuron growth and neuronal differentiation, including the number of cells, neurites, neurite length and neurite orientation. This information is essential for assessing the development of neuronal networks in response to extracellular stimuli, which is useful for studying neuronal structures, for example, the study of neurodegenerative diseases and pharmaceuticals. However, automatic and accurate analysis of neuronal structures from phase contrast images has remained challenging. To address this, we have developed NeuroQuantify, an open-source software that uses deep learning to efficiently and quickly segment cells and neurites in phase contrast microscopy images. NeuroQuantify offers several key features: (i) automatic detection of cells and neurites; (ii) post-processing of the images for the quantitative neurite length measurement based on segmentation of phase contrast microscopy images, and (iii) identification of neurite orientations. The user-friendly NeuroQuantify software can be installed and freely downloaded from GitHub https://github.com/StanleyZ0528/neural-image-segmentation.
US Microelectronics Packaging Ecosystem: Challenges and Opportunities
Authors: Rouhan Noor, Himanandhan Reddy Kottur, Patrick J Craig, Liton Kumar Biswas, M Shafkat M Khan, Nitin Varshney, Hamed Dalir, Elif Akçalı, Bahareh Ghane Motlagh, Charles Woychik, Yong-Kyu Yoon, Navid Asadizanjani
Date: 2023-10-18 00:00:00

The semiconductor industry is experiencing a significant shift from traditional methods of shrinking devices and reducing costs. Chip designers actively seek new technological solutions to enhance cost-effectiveness while incorporating more features into the silicon footprint. One promising approach is Heterogeneous Integration (HI), which involves advanced packaging techniques to integrate independently designed and manufactured components using the most suitable process technology. However, adopting HI introduces design and security challenges. To enable HI, research and development of advanced packaging is crucial. The existing research raises the possible security threats in the advanced packaging supply chain, as most of the Outsourced Semiconductor Assembly and Test (OSAT) facilities/vendors are offshore. To deal with the increasing demand for semiconductors and to ensure a secure semiconductor supply chain, there are sizable efforts from the United States (US) government to bring semiconductor fabrication facilities onshore. However, the US-based advanced packaging capabilities must also be ramped up to fully realize the vision of establishing a secure, efficient, resilient semiconductor supply chain. Our effort was motivated to identify the possible bottlenecks and weak links in the advanced packaging supply chain based in the US.
Experimental Results of Underwater Sound Speed Profile Inversion by Few-shot Multi-task Learning
Authors: Wei Huang, Fan Gao, Junting Wang, Hao Zhang
Date: 2023-10-18 00:00:00
Tasks: Compressive Sensing, Few-Shot Learning, Multi-Task Learning

Underwater Sound Speed Profile (SSP) distribution has great influence on the propagation mode of acoustic signal, thus the fast and accurate estimation of SSP is of great importance in building underwater observation systems. The state-of-the-art SSP inversion methods include frameworks of matched field processing (MFP), compressive sensing (CS), and feedforeward neural networks (FNN), among which the FNN shows better real-time performance while maintain the same level of accuracy. However, the training of FNN needs quite a lot historical SSP samples, which is diffcult to be satisfied in many ocean areas. This situation is called few-shot learning. To tackle this issue, we propose a multi-task learning (MTL) model with partial parameter sharing among different traning tasks. By MTL, common features could be extracted, thus accelerating the learning process on given tasks, and reducing the demand for reference samples, so as to enhance the generalization ability in few-shot learning. To verify the feasibility and effectiveness of MTL, a deep-ocean experiment was held in April 2023 at the South China Sea. Results shows that MTL outperforms the state-of-the-art methods in terms of accuracy for SSP inversion, while inherits the real-time advantage of FNN during the inversion stage.
Enhancing Binary Code Comment Quality Classification: Integrating Generative AI for Improved Accuracy
Authors: Rohith Arumugam S, Angel Deborah S
Date: 2023-10-14 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

This report focuses on enhancing a binary code comment quality classification model by integrating generated code and comment pairs, to improve model accuracy. The dataset comprises 9048 pairs of code and comments written in the C programming language, each annotated as "Useful" or "Not Useful." Additionally, code and comment pairs are generated using a Large Language Model Architecture, and these generated pairs are labeled to indicate their utility. The outcome of this effort consists of two classification models: one utilizing the original dataset and another incorporating the augmented dataset with the newly generated code comment pairs and labels.
Electromechanical System Design for Self-Balancing Robot
Authors: Md. Abid Al Morshed, Md. Mustakim Hayder, Tayfur Rahman Maruf
Date: 2023-10-18 00:00:00
Tasks: Position

Self-balancing robot is based on the principle of Inverted pendulum, which is a two-wheel vehicle balances itself up in the vertical position with reference to the ground. It consists of both hardware and software implementation. Mechanical model based on the state space design of the cart, pendulum system. To find its stable inverted position, we used a generic feedback controller (i.e., PID controller). According to the situation we have to control both angel of pendulum and position of cart. Mechanical design consists of two dc gear motor with encoder, one Arduino microcontroller, IMU (inertial mass unit) sensor and motor driver as a basic need. IMU sensor which consists of accelerometer and gyroscope gives the reference acceleration and angle with respect to ground (vertical), When encoder which is attached with the motor gives the speed of the motor. These parameters are taken as the system parameter and determine the external force needed to balance the robot up. It will be prevented from falling by giving acceleration to the wheels according to its inclination from the vertical. If the bot tilts by an angle, then in the frame of the wheels; the center of mass of the bot will experience a pseudo force which will apply a torque opposite to the direction of tilt. We used a HC05 Bluetooth module to control the robot via our smartphone.