pwc-archive/papers-with-abstracts huggingface dataset:
Predicting environment effects on breast cancer by implementing machine learning
Authors: Muhammad Shoaib Farooq, Mehreen Ilyas
Date: 2023-09-25 00:00:00
Tasks: regression, Survival Analysis

The biggest Breast cancer is increasingly a major factor in female fatalities, overtaking heart disease. While genetic factors are important in the growth of breast cancer, new research indicates that environmental factors also play a substantial role in its occurrence and progression. The literature on the various environmental factors that may affect breast cancer risk, incidence, and outcomes is thoroughly reviewed in this study report. The study starts by looking at how lifestyle decisions, such as eating habits, exercise routines, and alcohol consumption, may affect hormonal imbalances and inflammation, two important factors driving the development of breast cancer. Additionally, it explores the part played by environmental contaminants such pesticides, endocrine-disrupting chemicals (EDCs), and industrial emissions, all of which have been linked to a higher risk of developing breast cancer due to their interference with hormone signaling and DNA damage. Algorithms for machine learning are used to express predictions. Logistic Regression, Random Forest, KNN Algorithm, SVC and extra tree classifier. Metrics including the confusion matrix correlation coefficient, F1-score, Precision, Recall, and ROC curve were used to evaluate the models. The best accuracy among all the classifiers is Random Forest with 0.91% accuracy and ROC curve 0.901% of Logistic Regression. The accuracy of the multiple algorithms for machine learning utilized in this research was good, which is important and indicates that these techniques could serve as replacement forecasting techniques in breast cancer survival analysis, notably in the Asia region.
Guess & Sketch: Language Model Guided Transpilation
Authors: Celine Lee, Abdulrahman Mahmoud, Michal Kurek, Simone Campanoni, David Brooks, Stephen Chong, Gu-Yeon Wei, Alexander M. Rush
Date: 2023-09-25 00:00:00
Tasks: Language Modeling, Language Modelling, model, Translation

Maintaining legacy software requires many software and systems engineering hours. Assembly code programs, which demand low-level control over the computer machine state and have no variable names, are particularly difficult for humans to analyze. Existing conventional program translators guarantee correctness, but are hand-engineered for the source and target programming languages in question. Learned transpilation, i.e. automatic translation of code, offers an alternative to manual re-writing and engineering efforts. Automated symbolic program translation approaches guarantee correctness but struggle to scale to longer programs due to the exponentially large search space. Their rigid rule-based systems also limit their expressivity, so they can only reason about a reduced space of programs. Probabilistic neural language models (LMs) produce plausible outputs for every input, but do so at the cost of guaranteed correctness. In this work, we leverage the strengths of LMs and symbolic solvers in a neurosymbolic approach to learned transpilation for assembly code. Assembly code is an appropriate setting for a neurosymbolic approach, since assembly code can be divided into shorter non-branching basic blocks amenable to the use of symbolic methods. Guess & Sketch extracts alignment and confidence information from features of the LM then passes it to a symbolic solver to resolve semantic equivalence of the transpilation input and output. We test Guess & Sketch on three different test sets of assembly transpilation tasks, varying in difficulty, and show that it successfully transpiles 57.6% more examples than GPT-4 and 39.6% more examples than an engineered transpiler. We also share a training and evaluation dataset for this task.
RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models
Authors: Ronak Pradeep, Sahel Sharifymoghaddam, Jimmy Lin
Date: 2023-09-26 00:00:00
Tasks: Information Retrieval, Reranking, Retrieval

Researchers have successfully applied large language models (LLMs) such as ChatGPT to reranking in an information retrieval context, but to date, such work has mostly been built on proprietary models hidden behind opaque API endpoints. This approach yields experimental results that are not reproducible and non-deterministic, threatening the veracity of outcomes that build on such shaky foundations. To address this significant shortcoming, we present RankVicuna, the first fully open-source LLM capable of performing high-quality listwise reranking in a zero-shot setting. Experimental results on the TREC 2019 and 2020 Deep Learning Tracks show that we can achieve effectiveness comparable to zero-shot reranking with GPT-3.5 with a much smaller 7B parameter model, although our effectiveness remains slightly behind reranking with GPT-4. We hope our work provides the foundation for future research on reranking with modern LLMs. All the code necessary to reproduce our results is available at https://github.com/castorini/rank_llm.
Question-Answering Approach to Evaluating Legal Summaries
Authors: Huihui Xu, Kevin Ashley
Date: 2023-09-26 00:00:00
Tasks: Question Answering

Traditional evaluation metrics like ROUGE compare lexical overlap between the reference and generated summaries without taking argumentative structure into account, which is important for legal summaries. In this paper, we propose a novel legal summarization evaluation framework that utilizes GPT-4 to generate a set of question-answer pairs that cover main points and information in the reference summary. GPT-4 is then used to generate answers based on the generated summary for the questions from the reference summary. Finally, GPT-4 grades the answers from the reference summary and the generated summary. We examined the correlation between GPT-4 grading with human grading. The results suggest that this question-answering approach with GPT-4 can be a useful tool for gauging the quality of the summary.
Updated Corpora and Benchmarks for Long-Form Speech Recognition
Authors: Jennifer Drexler Fox, Desh Raj, Natalie Delworth, Quinn McNamara, Corey Miller, Migüel Jetté
Date: 2023-09-26 00:00:00
Tasks: Form, speech-recognition, Speech Recognition

The vast majority of ASR research uses corpora in which both the training and test data have been pre-segmented into utterances. In most real-word ASR use-cases, however, test audio is not segmented, leading to a mismatch between inference-time conditions and models trained on segmented utterances. In this paper, we re-release three standard ASR corpora - TED-LIUM 3, Gigapeech, and VoxPopuli-en - with updated transcription and alignments to enable their use for long-form ASR research. We use these reconstituted corpora to study the train-test mismatch problem for transducers and attention-based encoder-decoders (AEDs), confirming that AEDs are more susceptible to this issue. Finally, we benchmark a simple long-form training for these models, showing its efficacy for model robustness under this domain shift.
Robustness of the Random Language Model
Authors: Fatemeh Lalegani, Eric De Giuli
Date: 2023-09-26 00:00:00
Tasks: Language Acquisition, Language Modeling, Language Modelling, model

The Random Language Model (De Giuli 2019) is an ensemble of stochastic context-free grammars, quantifying the syntax of human and computer languages. The model suggests a simple picture of first language learning as a type of annealing in the vast space of potential languages. In its simplest formulation, it implies a single continuous transition to grammatical syntax, at which the symmetry among potential words and categories is spontaneously broken. Here this picture is scrutinized by considering its robustness against extensions of the original model, and trajectories through parameter space different from those originally considered. It is shown here that (i) the scenario is robust to explicit symmetry breaking, an inevitable component of learning in the real world; and (ii) the transition to grammatical syntax can be encountered by fixing the deep (hidden) structure while varying the surface (observable) properties. It is also argued that the transition becomes a sharp thermodynamic transition in an idealized limit. Moreover, comparison with human data on the clustering coefficient of syntax networks suggests that the observed transition is equivalent to that normally experienced by children at age 24 months. The results are discussed in light of theory of first-language acquisition in linguistics, and recent successes in machine learning.
Segmentation-Free Streaming Machine Translation
Authors: Javier Iranzo-Sánchez, Jorge Iranzo-Sánchez, Adrià Giménez, Jorge Civera, Alfons Juan
Date: 2023-09-26 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, Segmentation, Sentence, speech-recognition, Speech Recognition, Translation

Streaming Machine Translation (MT) is the task of translating an unbounded input text stream in real-time. The traditional cascade approach, which combines an Automatic Speech Recognition (ASR) and an MT system, relies on an intermediate segmentation step which splits the transcription stream into sentence-like units. However, the incorporation of a hard segmentation constrains the MT system and is a source of errors. This paper proposes a Segmentation-Free framework that enables the model to translate an unsegmented source stream by delaying the segmentation decision until the translation has been generated. Extensive experiments show how the proposed Segmentation-Free framework has better quality-latency trade-off than competing approaches that use an independent segmentation model. Software, data and models will be released upon paper acceptance.
KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse Transformation
Authors: Haotian Li, Bin Yu, Yuliang Wei, Kai Wang, Richard Yi Da Xu, Bailing Wang
Date: 2023-09-26 00:00:00
Tasks: Diversity, Knowledge Graph Completion, Knowledge Graphs, Link Prediction, Relation

Knowledge graph completion (KGC) revolves around populating missing triples in a knowledge graph using available information. Text-based methods, which depend on textual descriptions of triples, often encounter difficulties when these descriptions lack sufficient information for accurate prediction-an issue inherent to the datasets and not easily resolved through modeling alone. To address this and ensure data consistency, we first use large language models (LLMs) to generate coherent descriptions, bridging the semantic gap between queries and answers. Secondly, we utilize inverse relations to create a symmetric graph, thereby providing augmented training samples for KGC. Additionally, we employ the label information inherent in knowledge graphs (KGs) to enhance the existing contrastive framework, making it fully supervised. These efforts have led to significant performance improvements on the WN18RR and FB15k-237 datasets. According to standard evaluation metrics, our approach achieves a 4.2% improvement in Hit@1 on WN18RR and a 3.4% improvement in Hit@3 on FB15k-237, demonstrating superior performance.
Introducing DictaLM -- A Large Generative Language Model for Modern Hebrew
Authors: Shaltiel Shmidman, Avi Shmidman, Amir David Nissan Cohen, Moshe Koppel
Date: 2023-09-25 00:00:00
Tasks: Language Modeling, Language Modelling, Sentiment Analysis

We present DictaLM, a large-scale language model tailored for Modern Hebrew. Boasting 7B parameters, this model is predominantly trained on Hebrew-centric data. As a commitment to promoting research and development in the Hebrew language, we release both the foundation model and the instruct-tuned model under a Creative Commons license. Concurrently, we introduce DictaLM-Rab, another foundation model geared towards Rabbinic/Historical Hebrew. These foundation models serve as ideal starting points for fine-tuning various Hebrew-specific tasks, such as instruction, Q&A, sentiment analysis, and more. This release represents a preliminary step, offering an initial Hebrew LLM model for the Hebrew NLP community to experiment with.
ChatGPT Performance on Standardized Testing Exam -- A Proposed Strategy for Learners
Authors: Umer Farooq, Saira Anwar
Date: 2023-09-25 00:00:00

This study explores the problem solving capabilities of ChatGPT and its prospective applications in standardized test preparation, focusing on the GRE quantitative exam. Prior research has shown great potential for the utilization of ChatGPT for academic purposes in revolutionizing the approach to studying across various disciplines. We investigate how ChatGPT performs across various question types in the GRE quantitative domain, and how modifying question prompts impacts its accuracy. More specifically this study addressed two research questions: 1. How does ChatGPT perform in answering GRE-based quantitative questions across various content areas? 2. How does the accuracy of ChatGPT vary with modifying the question prompts? The dataset consisting of 100 randomly selected GRE quantitative questions was collected from the ETS official guide to GRE test preparation. We used quantitative evaluation to answer our first research question, and t-test to examine the statistical association between prompt modification and ChatGPT's accuracy. Results show a statistical improvement in the ChatGPT's accuracy after applying instruction priming and contextual prompts to the original questions. ChatGPT showed 84% accuracy with the modified prompts compared to 69% with the original data. The study discusses the areas where ChatGPT struggled with certain questions and how modifications can be helpful for preparing for standardized tests like GRE and provides future directions for prompt modifications.
Physics of Language Models: Part 3.2, Knowledge Manipulation
Authors: Zeyuan Allen-Zhu, Yuanzhi Li
Date: 2023-09-25 00:00:00
Tasks: Attribute, Language Modelling, Logical Reasoning, Retrieval

Language models can store vast factual knowledge, yet their ability to flexibly use this knowledge for downstream tasks (e.g., via instruction finetuning) remains questionable. This paper investigates four fundamental knowledge manipulation tasks: retrieval (e.g., "What is person A's attribute X?"), classification (e.g., "Is A's attribute X even or odd?"), comparison (e.g., "Is A greater than B in attribute X?"), and inverse search (e.g., "Which person's attribute X equals T?"). We show that language models excel in knowledge retrieval but struggle even in the simplest classification or comparison tasks unless Chain of Thoughts (CoTs) are employed during both training and inference. Moreover, their performance in inverse knowledge search is virtually 0%, regardless of the prompts. Our primary contribution is a controlled, synthetic experiment that confirms these weaknesses are inherent to language models: they cannot efficiently manipulate knowledge from pre-training data, even when such knowledge is perfectly stored in the models, despite adequate training and sufficient model size. Our findings also apply to modern pretrained language models such as GPT-4, thus giving rise to many Turing tests to distinguish Humans from contemporary AIs.
PopBERT. Detecting populism and its host ideologies in the German Bundestag
Authors: L. Erhard, S. Hanke, U. Remer, A. Falenska, R. Heiberger
Date: 2023-09-22 00:00:00
Tasks: valid

The rise of populism concerns many political scientists and practitioners, yet the detection of its underlying language remains fragmentary. This paper aims to provide a reliable, valid, and scalable approach to measure populist stances. For that purpose, we created an annotated dataset based on parliamentary speeches of the German Bundestag (2013 to 2021). Following the ideational definition of populism, we label moralizing references to the virtuous people or the corrupt elite as core dimensions of populist language. To identify, in addition, how the thin ideology of populism is thickened, we annotate how populist statements are attached to left-wing or right-wing host ideologies. We then train a transformer-based model (PopBERT) as a multilabel classifier to detect and quantify each dimension. A battery of validation checks reveals that the model has a strong predictive accuracy, provides high qualitative face validity, matches party rankings of expert surveys, and detects out-of-sample text snippets correctly. PopBERT enables dynamic analyses of how German-speaking politicians and parties use populist language as a strategic device. Furthermore, the annotator-level data may also be applied in cross-domain applications or to develop related classifiers.
Convergence guarantees for forward gradient descent in the linear regression model
Authors: Thijs Bos, Johannes Schmidt-Hieber
Date: 2023-09-26 00:00:00
Tasks: regression

Renewed interest in the relationship between artificial and biological neural networks motivates the study of gradient-free methods. Considering the linear regression model with random design, we theoretically analyze in this work the biologically motivated (weight-perturbed) forward gradient scheme that is based on random linear combination of the gradient. If d denotes the number of parameters and k the number of samples, we prove that the mean squared error of this method converges for $k\gtrsim d^2\log(d)$ with rate $d^2\log(d)/k.$ Compared to the dimension dependence d for stochastic gradient descent, an additional factor $d\log(d)$ occurs.
Smooth Exact Gradient Descent Learning in Spiking Neural Networks
Authors: Christian Klos, Raoul-Martin Memmesheimer
Date: 2023-09-25 00:00:00

Gradient descent prevails in artificial neural network training, but seems inept for spiking neural networks as small parameter changes can cause sudden, disruptive (dis-)appearances of spikes. Here, we demonstrate exact gradient descent based on continuously changing spiking dynamics. These are generated by neuron models whose spikes vanish and appear at the end of a trial, where it cannot influence subsequent dynamics. This also enables gradient-based spike addition and removal. We illustrate our scheme with various tasks and setups, including recurrent and deep, initially silent networks.
Automata Quest: NCAs as a Video Game Life Mechanic
Authors: Hiroki Sato, Tanner Lund, Takahide Yoshida, Atsushi Masumori
Date: 2023-09-23 00:00:00

We study life over the course of video game history as represented by their mechanics. While there have been some variations depending on genre or "character type", we find that most games converge to a similar representation. We also examine the development of Conway's Game of Life (one of the first zero player games) and related automata that have developed over the years. With this history in mind, we investigate the viability of one popular form of automata, namely Neural Cellular Automata, as a way to more fully express life within video game settings and innovate new game mechanics or gameplay loops.
SOFARI: High-Dimensional Manifold-Based Inference
Authors: Zemin Zheng, Xin Zhou, Yingying Fan, Jinchi Lv
Date: 2023-09-26 00:00:00
Tasks: Multi-Task Learning

Multi-task learning is a widely used technique for harnessing information from various tasks. Recently, the sparse orthogonal factor regression (SOFAR) framework, based on the sparse singular value decomposition (SVD) within the coefficient matrix, was introduced for interpretable multi-task learning, enabling the discovery of meaningful latent feature-response association networks across different layers. However, conducting precise inference on the latent factor matrices has remained challenging due to orthogonality constraints inherited from the sparse SVD constraint. In this paper, we suggest a novel approach called high-dimensional manifold-based SOFAR inference (SOFARI), drawing on the Neyman near-orthogonality inference while incorporating the Stiefel manifold structure imposed by the SVD constraints. By leveraging the underlying Stiefel manifold structure, SOFARI provides bias-corrected estimators for both latent left factor vectors and singular values, for which we show to enjoy the asymptotic mean-zero normal distributions with estimable variances. We introduce two SOFARI variants to handle strongly and weakly orthogonal latent factors, where the latter covers a broader range of applications. We illustrate the effectiveness of SOFARI and justify our theoretical results through simulation examples and a real data application in economic forecasting.
Decoding trust: A reinforcement learning perspective
Authors: Guozhong Zheng, Jiqiang Zhang, Jing Zhang, Weiran Cai, Li Chen
Date: 2023-09-26 00:00:00
Tasks: Decision Making, Q-Learning, reinforcement-learning, Reinforcement Learning

Behavioral experiments on the trust game have shown that trust and trustworthiness are universal among human beings, contradicting the prediction by assuming \emph{Homo economicus} in orthodox Economics. This means some mechanism must be at work that favors their emergence. Most previous explanations however need to resort to some factors based upon imitative learning, a simple version of social learning. Here, we turn to the paradigm of reinforcement learning, where individuals update their strategies by evaluating the long-term return through accumulated experience. Specifically, we investigate the trust game with the Q-learning algorithm, where each participant is associated with two evolving Q-tables that guide one's decision making as trustor and trustee respectively. In the pairwise scenario, we reveal that high levels of trust and trustworthiness emerge when individuals appreciate both their historical experience and returns in the future. Mechanistically, the evolution of the Q-tables shows a crossover that resembles human's psychological changes. We also provide the phase diagram for the game parameters, where the boundary analysis is conducted. These findings are robust when the scenario is extended to a latticed population. Our results thus provide a natural explanation for the emergence of trust and trustworthiness without external factors involved. More importantly, the proposed paradigm shows the potential in deciphering many puzzles in human behaviors.
Byzantine-Resilient Federated PCA and Low Rank Column-wise Sensing
Authors: Ankit Pratap Singh, Namrata Vaswani
Date: 2023-09-25 00:00:00

This work considers two related learning problems in a federated attack prone setting: federated principal components analysis (PCA) and federated low rank column-wise sensing (LRCS). The node attacks are assumed to be Byzantine which means that the attackers are omniscient and can collude. We introduce a novel provably Byzantine-resilient communication-efficient and sampleefficient algorithm, called Subspace-Median, that solves the PCA problem and is a key part of the solution for the LRCS problem. We also study the most natural Byzantine-resilient solution for federated PCA, a geometric median based modification of the federated power method, and explain why it is not useful. Our second main contribution is a complete alternating gradient descent (GD) and minimization (altGDmin) algorithm for Byzantine-resilient horizontally federated LRCS and sample and communication complexity guarantees for it. Extensive simulation experiments are used to corroborate our theoretical guarantees. The ideas that we develop for LRCS are easily extendable to other LR recovery problems as well.