pwc-archive/papers-with-abstracts huggingface dataset:
Mitigating Exaggerated Safety in Large Language Models
Authors: Ruchira Ray, Ruchi Bhalani
Date: 2024-05-08 00:00:00
Tasks: Decision Making, Navigate

As the popularity of Large Language Models (LLMs) grow, combining model safety with utility becomes increasingly important. The challenge is making sure that LLMs can recognize and decline dangerous prompts without sacrificing their ability to be helpful. The problem of "exaggerated safety" demonstrates how difficult this can be. To reduce excessive safety behaviours -- which was discovered to be 26.1% of safe prompts being misclassified as dangerous and refused -- we use a combination of XSTest dataset prompts as well as interactive, contextual, and few-shot prompting to examine the decision bounds of LLMs such as Llama2, Gemma Command R+, and Phi-3. We find that few-shot prompting works best for Llama2, interactive prompting works best Gemma, and contextual prompting works best for Command R+ and Phi-3. Using a combination of these prompting strategies, we are able to mitigate exaggerated safety behaviors by an overall 92.9% across all LLMs. Our work presents a multiple prompting strategies to jailbreak LLMs' decision-making processes, allowing them to navigate the tight line between refusing unsafe prompts and remaining helpful.
Fishing for Magikarp: Automatically Detecting Under-trained Tokens in Large Language Models
Authors: Sander Land, Max Bartolo
Date: 2024-05-08 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

The disconnect between tokenizer creation and model training in language models allows for specific inputs, such as the infamous SolidGoldMagikarp token, to induce unwanted model behaviour. Although such `glitch tokens', tokens present in the tokenizer vocabulary but that are nearly or entirely absent during model training, have been observed across various models, a reliable method to identify and address them has been missing. We present a comprehensive analysis of Large Language Model tokenizers, specifically targeting this issue of detecting under-trained tokens. Through a combination of tokenizer analysis, model weight-based indicators, and prompting techniques, we develop novel and effective methods for automatically detecting these problematic tokens. Our findings demonstrate the prevalence of such tokens across a diverse set of models and provide insights into improving the efficiency and safety of language models.
Kreyòl-MT: Building MT for Latin American, Caribbean and Colonial African Creole Languages
Authors: Nathaniel R. Robinson, Raj Dabre, Ammon Shurtz, Rasul Dent, Onenamiyi Onesi, Claire Bizon Monroc, Loïc Grobol, Hasan Muhammad, Ashi Garg, Naome A. Etori, Vijay Murari Tiyyala, Olanrewaju Samuel, Matthew Dean Stutzman, Bismarck Bamfo Odoom, Sanjeev Khudanpur, Stephen D. Richardson, Kenton Murray
Date: 2024-05-08 00:00:00
Tasks: Diversity, Machine Translation, Translation

A majority of language technologies are tailored for a small number of high-resource languages, while relatively many low-resource languages are neglected. One such group, Creole languages, have long been marginalized in academic study, though their speakers could benefit from machine translation (MT). These languages are predominantly used in much of Latin America, Africa and the Caribbean. We present the largest cumulative dataset to date for Creole language MT, including 14.5M unique Creole sentences with parallel translations -- 11.6M of which we release publicly, and the largest bitexts gathered to date for 41 languages -- the first ever for 21. In addition, we provide MT models supporting all 41 Creole languages in 172 translation directions. Given our diverse dataset, we produce a model for Creole language MT exposed to more genre diversity than ever before, which outperforms a genre-specific Creole MT model on its own benchmark for 26 of 34 translation directions.
QuaLLM: An LLM-based Framework to Extract Quantitative Insights from Online Forums
Authors: Varun Nagaraj Rao, Eesha Agarwal, Samantha Dalal, Dan Calacci, Andrés Monroy-Hernández
Date: 2024-05-08 00:00:00

Online discussion forums provide crucial data to understand the concerns of a wide range of real-world communities. However, the typical qualitative and quantitative methodologies used to analyze those data, such as thematic analysis and topic modeling, are infeasible to scale or require significant human effort to translate outputs to human readable forms. This study introduces QuaLLM, a novel LLM-based framework to analyze and extract quantitative insights from text data on online forums. The framework consists of a novel prompting and human evaluation methodology. We applied this framework to analyze over one million comments from two of Reddit's rideshare worker communities, marking the largest study of its type. We uncover significant worker concerns regarding AI and algorithmic platform decisions, responding to regulatory calls about worker insights. In short, our work sets a new precedent for AI-assisted quantitative data analysis to surface concerns from online forums.
Adaptability and Homeostasis in the Game of Life interacting with the evolved Cellular Automata
Authors: Keisuke Suzuki, Takashi Ikegami
Date: 2024-05-09 00:00:00

In this paper we study the emergence of homeostasis in a two-layer system of the Game of Life, in which the Game of Life in the first layer couples with another system of cellular automata in the second layer. Homeostasis is defined here as a space-time dynamic that regulates the number of cells in state-1 in the Game of Life layer. A genetic algorithm is used to evolve the rules of the second layer to control the pattern of the Game of Life. We discovered that there are two antagonistic attractors that control the numbers of cells in state-1 in the first layer. The homeostasis sustained by these attractors are compared with the homeostatic dynamics observed in Daisy World.
Large Language Model-Aided Evolutionary Search for Constrained Multiobjective Optimization
Authors: Zeyi Wang, Songbai Liu, Jianyong Chen, Kay Chen Tan
Date: 2024-05-09 00:00:00
Tasks: Evolutionary Algorithms, Language Modeling, Language Modelling, Large Language Model, Multiobjective Optimization, Prompt Engineering

Evolutionary algorithms excel in solving complex optimization problems, especially those with multiple objectives. However, their stochastic nature can sometimes hinder rapid convergence to the global optima, particularly in scenarios involving constraints. In this study, we employ a large language model (LLM) to enhance evolutionary search for solving constrained multi-objective optimization problems. Our aim is to speed up the convergence of the evolutionary population. To achieve this, we finetune the LLM through tailored prompt engineering, integrating information concerning both objective values and constraint violations of solutions. This process enables the LLM to grasp the relationship between well-performing and poorly performing solutions based on the provided input data. Solution's quality is assessed based on their constraint violations and objective-based performance. By leveraging the refined LLM, it can be used as a search operator to generate superior-quality solutions. Experimental evaluations across various test benchmarks illustrate that LLM-aided evolutionary search can significantly accelerate the population's convergence speed and stands out competitively against cutting-edge evolutionary algorithms.
A Newton Method for Hausdorff Approximations of the Pareto Front within Multi-objective Evolutionary Algorithms
Authors: Hao Wang, Angel E. Rodriguez-Fernandez, Lourdes Uribe, André Deutz, Oziel Cortés-Piña, Oliver Schütze
Date: 2024-05-09 00:00:00
Tasks: Evolutionary Algorithms

A common goal in evolutionary multi-objective optimization is to find suitable finite-size approximations of the Pareto front of a given multi-objective optimization problem. While many multi-objective evolutionary algorithms have proven to be very efficient in finding good Pareto front approximations, they may need quite a few resources or may even fail to obtain optimal or nearly approximations. Hereby, optimality is implicitly defined by the chosen performance indicator. In this work, we propose a set-based Newton method for Hausdorff approximations of the Pareto front to be used within multi-objective evolutionary algorithms. To this end, we first generalize the previously proposed Newton step for the performance indicator for the treatment of constrained problems for general reference sets. To approximate the target Pareto front, we propose a particular strategy for generating the reference set that utilizes the data gathered by the evolutionary algorithm during its run. Finally, we show the benefit of the Newton method as a post-processing step on several benchmark test functions and different base evolutionary algorithms.
Non-asymptotic estimates for accelerated high order Langevin Monte Carlo algorithms
Authors: Ariel Neufeld, Ying Zhang
Date: 2024-05-09 00:00:00

In this paper, we propose two new algorithms, namely aHOLA and aHOLLA, to sample from high-dimensional target distributions with possibly super-linearly growing potentials. We establish non-asymptotic convergence bounds for aHOLA in Wasserstein-1 and Wasserstein-2 distances with rates of convergence equal to $1+q/2$ and $1/2+q/4$, respectively, under a local H\"{o}lder condition with exponent $q\in(0,1]$ and a convexity at infinity condition on the potential of the target distribution. Similar results are obtained for aHOLLA under certain global continuity conditions and a dissipativity condition. Crucially, we achieve state-of-the-art rates of convergence of the proposed algorithms in the non-convex setting which are higher than those of the existing algorithms. Numerical experiments are conducted to sample from several distributions and the results support our main findings.
Review-based Recommender Systems: A Survey of Approaches, Challenges and Future Perspectives
Authors: Emrul Hasan, Mizanur Rahman, Chen Ding, Jimmy Xiangji Huang, Shaina Raza
Date: 2024-05-09 00:00:00
Tasks: Navigate, Recommendation Systems

Recommender systems play a pivotal role in helping users navigate an overwhelming selection of products and services. On online platforms, users have the opportunity to share feedback in various modes, including numerical ratings, textual reviews, and likes/dislikes. Traditional recommendation systems rely on users explicit ratings or implicit interactions (e.g. likes, clicks, shares, saves) to learn user preferences and item characteristics. Beyond these numerical ratings, textual reviews provide insights into users fine-grained preferences and item features. Analyzing these reviews is crucial for enhancing the performance and interpretability of personalized recommendation results. In recent years, review-based recommender systems have emerged as a significant sub-field in this domain. In this paper, we provide a comprehensive overview of the developments in review-based recommender systems over recent years, highlighting the importance of reviews in recommender systems, as well as the challenges associated with extracting features from reviews and integrating them into ratings. Specifically, we present a categorization of these systems and summarize the state-of-the-art methods, analyzing their unique features, effectiveness, and limitations. Finally, we propose potential directions for future research, including the integration of multimodal data, multi-criteria rating information, and ethical considerations.
Information Extraction from Historical Well Records Using A Large Language Model
Authors: Zhiwei Ma, Javier E. Santo, Greg Lackey, Hari Viswanathan, Daniel O'Malley
Date: 2024-05-08 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

To reduce environmental risks and impacts from orphaned wells (abandoned oil and gas wells), it is essential to first locate and then plug these wells. Although some historical documents are available, they are often unstructured, not cleaned, and outdated. Additionally, they vary widely by state and type. Manual reading and digitizing this information from historical documents are not feasible, given the high number of wells. Here, we propose a new computational approach for rapidly and cost-effectively locating these wells. Specifically, we leverage the advanced capabilities of large language models (LLMs) to extract vital information including well location and depth from historical records of orphaned wells. In this paper, we present an information extraction workflow based on open-source Llama 2 models and test them on a dataset of 160 well documents. Our results show that the developed workflow achieves excellent accuracy in extracting location and depth from clean, PDF-based reports, with a 100% accuracy rate. However, it struggles with unstructured image-based well records, where accuracy drops to 70%. The workflow provides significant benefits over manual human digitization, including reduced labor and increased automation. In general, more detailed prompting leads to improved information extraction, and those LLMs with more parameters typically perform better. We provided a detailed discussion of the current challenges and the corresponding opportunities/approaches to address them. Additionally, a vast amount of geoscientific information is locked up in old documents, and this work demonstrates that recent breakthroughs in LLMs enable us to unlock this information more broadly.
Learning label-label correlations in Extreme Multi-label Classification via Label Features
Authors: Siddhant Kharbanda, Devaansh Gupta, Erik Schultheis, Atmadeep Banerjee, Cho-Jui Hsieh, Rohit Babbar
Date: 2024-05-03 00:00:00
Tasks: Extreme Multi-Label Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi Label Text Classification, Multi-Label Text Classification, Product Recommendation, text-classification, Text Classification

Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices. Recent works in this domain have increasingly focused on a symmetric problem setting where both input instances and label features are short-text in nature. Short-text XMC with label features has found numerous applications in areas such as query-to-ad-phrase matching in search ads, title-based product recommendation, prediction of related searches. In this paper, we propose Gandalf, a novel approach which makes use of a label co-occurrence graph to leverage label features as additional data points to supplement the training distribution. By exploiting the characteristics of the short-text XMC problem, it leverages the label features to construct valid training instances, and uses the label graph for generating the corresponding soft-label targets, hence effectively capturing the label-label correlations. Surprisingly, models trained on these new training instances, although being less than half of the original dataset, can outperform models trained on the original dataset, particularly on the PSP@k metric for tail labels. With this insight, we aim to train existing XMC algorithms on both, the original and new training instances, leading to an average 5% relative improvements for 6 state-of-the-art algorithms across 4 benchmark datasets consisting of up to 1.3M labels. Gandalf can be applied in a plug-and-play manner to various methods and thus forwards the state-of-the-art in the domain, without incurring any additional computational overheads.
Conditional Information Bottleneck Approach for Time Series Imputation
Authors: MinGyu Choi, Changhee Lee
Date: 2024-01-16 00:00:00
Proceeding: ICLR 2024 1
Tasks: Imputation, Time Series

Time series imputation presents a significant challenge because it requires capturing the underlying temporal dynamics from partially observed time series data. Among the recent successes of imputation methods based on generative models, the information bottleneck (IB) framework offers a well-suited theoretical foundation for multiple imputations, allowing us to account for the uncertainty associated with the imputed values. However, directly applying the IB framework to time series data without considering their temporal context can lead to a substantial loss of temporal dependencies, which, in turn, can degrade the overall imputation performance. To address such a challenge, we propose a novel conditional information bottleneck (CIB) approach for time series imputation, which aims to mitigate the potentially negative consequences of the regularization constraint by focusing on reducing the redundant information conditioned on the temporal context. We provide a theoretical analysis of its effect by adapting variational decomposition. We use the resulting insight and propose a novel deep learning method that can approximately achieve the proposed CIB objective for time series imputation as a combination of evidence lower bound and novel temporal kernel-enhanced contrastive optimization. Our experiments, conducted on multiple real-world datasets, consistently demonstrate that our method significantly improves imputation performance (including both interpolation and extrapolation), and also enhances classification performance based on the imputed values.
MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting
Authors: Huiqiang Wang, Jian Peng, Feihu Huang, Jince Wang, Junhui Chen, Yifei Xiao
Date: 2023-02-02 00:00:00
Proceeding: ICLR 2023 2
Tasks: Time Series

Recently, Transformer-based methods have achieved surprising performance in the field of long-term series forecasting, but the attention mechanism for computing global correlations entails high complexity. And they do not allow for targeted modeling of local features as CNN structures do. To solve the above problems, we propose to combine local features and global correlations to capture the overall view of time series (e.g., fluctuations, trends). To fully exploit the underlying information in the time series, a multi-scale branch structure is adopted to model different potential patterns separately. Each pattern is extracted with down-sampled convolution and isometric convolution for local features and global correlations, respectively. In addition to being more effective, our proposed method, termed as Multi-scale Isometric Convolution Network (MICN), is more efficient with linear complexity about the sequence length with suitable convolution kernels. Our experiments on six benchmark datasets show that compared with state-of-the-art methods, MICN yields 17.2% and 21.6% relative improvements for multivariate and univariate time series, respectively.
ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis
Authors: Donghao Luo, Xue Wang
Date: 2024-01-16 00:00:00
Proceeding: ICLR 2024 1
Tasks: Time Series, Time Series Analysis

Recently, Transformer-based and MLP-based models have emerged rapidly and won dominance in time series analysis. In contrast, convolution is losing steam in time series tasks nowadays for inferior performance. This paper studies the open question of how to better use convolution in time series analysis and makes efforts to bring convolution back to the arena of time series analysis. To this end, we modernize the traditional TCN and conduct time series related modifications to make it more suitable for time series tasks. As the outcome, we propose ModernTCN and successfully solve this open question through a seldom-explored way in time series community. As a pure convolution structure, ModernTCN still achieves the consistent state-of-the-art performance on five mainstream time series analysis tasks while maintaining the efficiency advantage of convolution-based models, therefore providing a better balance of efficiency and performance than state-of-the-art Transformer-based and MLP-based models. Our study further reveals that, compared with previous convolution-based models, our ModernTCN has much larger effective receptive fields (ERFs), therefore can better unleash the potential of convolution in time series analysis. Code is available at this repository: https://github.com/luodhhh/ModernTCN.
Audio-Visual Speech Recognition based on Regulated Transformer and Spatio-Temporal Fusion Strategy for Driver Assistive Systems
Authors: Dmitry Ryumin, Alexandr Axyonov, Elena Ryumina, Denis Ivanko, Alexey Kashevnik, Alexey Karpov
Date: 2024-05-09 00:00:00
Proceeding: Expert Systems with Applications 2024 5
Tasks: Audio-Visual Speech Recognition, Lipreading, Lip Reading, speech-recognition, Speech Recognition, Visual Speech Recognition

This article presents a research methodology for audio-visual speech recognition (AVSR) in driver assistive systems. These systems necessitate ongoing interaction with drivers while driving through voice control for safety reasons. The article introduces a novel audio-visual speech command recognition transformer (AVCRFormer) specifically designed for robust AVSR. We propose (i) a multimodal fusion strategy based on spatio-temporal fusion of audio and video feature matrices, (ii) a regulated transformer based on iterative model refinement module with multiple encoders, (iii) a classifier ensemble strategy based on multiple decoders. The spatio-temporal fusion strategy preserves contextual information of both modalities and achieves their synchronization. An iterative model refinement module can bridge the gap between acoustic and visual data by leveraging their impact on speech recognition accuracy. The proposed multi-prediction strategy demonstrates superior performance compared to traditional single-prediction strategy, showcasing the model’s adaptability across diverse audio-visual contexts. The transformer proposed has achieved the highest values of speech command recognition accuracy, reaching 98.87% and 98.81% on the RUSAVIC and LRW corpora, respectively. This research has significant implications for advancing human–computer interaction. The capabilities of AVCRFormer extend beyond AVSR, making it a valuable contribution to the intersection of audio-visual processing and artificial intelligence.
Part-based Lipreading for Audio-Visual Speech Recognition
Authors: Ziling Miao, Hong Liu, Bing Yang
Date: 2020-12-14 00:00:00
Proceeding: IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2020 12
Tasks: Audio-Visual Speech Recognition, Lipreading, speech-recognition, Speech Recognition, Visual Speech Recognition

Lipreading is an important component of audio-visual speech recognition. However, lips are usually modeled as a whole in lipreading, which ignores that each part of lip focuses on different characteristics of mouth and the overall model can not fit each part perfectly. Besides, features based on the whole lip usually vary a lot according to different speakers, which leads that the training databases usually need to contain as much speakers as possible. In this paper, A part-based lipreading (PBL) method is proposed to deal with the mismatch between an overall lip model and the separate parts of lips, also the excessive dependence of models on the speakers in training set. PBL models lips partly and predicts jointly. It employs a uniform partition strategy on convolutional features and generates several part-level sub-results for final prediction. Experiments are performed on a large publicly available dataset (LRW) and part of it (p-LRW, 65 words), in order to simulate the progressive instructions in the working scene of robots. Word accuracy of PBL reaches 82.8% on LRW and 88.9% on p-LRW. Finally, an end-to-end audio-visual speech recognition system using PBL is established and achieves 98.3% word accuracy on LRW.