pwc-archive/papers-with-abstracts huggingface dataset:
Contextual Breach: Assessing the Robustness of Transformer-based QA Models
Authors: Asir Saadat, Nahian Ibn Asad, Md Farhan Ishmam
Date: 2024-09-17 00:00:00
Tasks: Question Answering

Contextual question-answering models are susceptible to adversarial perturbations to input context, commonly observed in real-world scenarios. These adversarial noises are designed to degrade the performance of the model by distorting the textual input. We introduce a unique dataset that incorporates seven distinct types of adversarial noise into the context, each applied at five different intensity levels on the SQuAD dataset. To quantify the robustness, we utilize robustness metrics providing a standardized measure for assessing model performance across varying noise types and levels. Experiments on transformer-based question-answering models reveal robustness vulnerabilities and important insights into the model's performance in realistic textual input.
Improving Speech Emotion Recognition in Under-Resourced Languages via Speech-to-Speech Translation with Bootstrapping Data Selection
Authors: Hsi-Che Lin, Yi-Cheng Lin, Huang-Cheng Chou, Hung-Yi Lee
Date: 2024-09-17 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition, Speech-to-Speech Translation

Speech Emotion Recognition (SER) is a crucial component in developing general-purpose AI agents capable of natural human-computer interaction. However, building robust multilingual SER systems remains challenging due to the scarcity of labeled data in languages other than English and Chinese. In this paper, we propose an approach to enhance SER performance in low SER resource languages by leveraging data from high-resource languages. Specifically, we employ expressive Speech-to-Speech translation (S2ST) combined with a novel bootstrapping data selection pipeline to generate labeled data in the target language. Extensive experiments demonstrate that our method is both effective and generalizable across different upstream models and languages. Our results suggest that this approach can facilitate the development of more scalable and robust multilingual SER systems.
Enhancing Multilingual Speech Generation and Recognition Abilities in LLMs with Constructed Code-switched Data
Authors: Jing Xu, Daxin Tan, Jiaqi Wang, Xiao Chen
Date: 2024-09-17 00:00:00
Tasks: Speech Synthesis

While large language models (LLMs) have been explored in the speech domain for both generation and recognition tasks, their applications are predominantly confined to the monolingual scenario, with limited exploration in multilingual and code-switched (CS) contexts. Additionally, speech generation and recognition tasks are often handled separately, such as VALL-E and Qwen-Audio. In this paper, we propose a MutltiLingual MultiTask (MLMT) model, integrating multilingual speech generation and recognition tasks within the single LLM. Furthermore, we develop an effective data construction approach that splits and concatenates words from different languages to equip LLMs with CS synthesis ability without relying on CS data. The experimental results demonstrate that our model outperforms other baselines with a comparable data scale. Furthermore, our data construction approach not only equips LLMs with CS speech synthesis capability with comparable speaker consistency and similarity to any given speaker, but also improves the performance of LLMs in multilingual speech generation and recognition tasks.
Propulsion: Steering LLM with Tiny Fine-Tuning
Authors: Md Kowsher, Nusrat Jahan Prottasha, Prakash Bhat
Date: 2024-09-17 00:00:00
Tasks: parameter-efficient fine-tuning

The rapid advancements in Large Language Models (LLMs) have revolutionized natural language processing (NLP) and related fields. However, fine-tuning these models for specific tasks remains computationally expensive and risks degrading pre-learned features. To address these challenges, we propose Propulsion, a novel parameter efficient fine-tuning (PEFT) method designed to optimize task-specific performance while drastically reducing computational overhead. Inspired by the concept of controlled adjustments in physical motion, Propulsion selectively re-scales specific dimensions of a pre-trained model, guiding output predictions toward task objectives without modifying the model's parameters. By introducing lightweight, trainable Propulsion parameters at the pre-trained layer, we minimize the number of parameters updated during fine-tuning, preventing overfitting or overwriting of existing knowledge. Our theoretical analysis, supported by Neural Tangent Kernel (NTK) theory, shows that Propulsion approximates the performance of full fine-tuning with far fewer trainable parameters. Empirically, Propulsion reduces the parameter count from 355.3 million to just 0.086 million, achieving over a 10x reduction compared to standard approaches like LoRA while maintaining competitive performance across benchmarks.
Attention-Seeker: Dynamic Self-Attention Scoring for Unsupervised Keyphrase Extraction
Authors: Erwin D. López Z., Cheng Tang, Atsushi Shimada
Date: 2024-09-17 00:00:00
Tasks: Keyphrase Extraction, Language Modeling, Language Modelling, Large Language Model

This paper proposes Attention-Seeker, an unsupervised keyphrase extraction method that leverages self-attention maps from a Large Language Model to estimate the importance of candidate phrases. Our approach identifies specific components - such as layers, heads, and attention vectors - where the model pays significant attention to the key topics of the text. The attention weights provided by these components are then used to score the candidate phrases. Unlike previous models that require manual tuning of parameters (e.g., selection of heads, prompts, hyperparameters), Attention-Seeker dynamically adapts to the input text without any manual adjustments, enhancing its practical applicability. We evaluate Attention-Seeker on four publicly available datasets: Inspec, SemEval2010, SemEval2017, and Krapivin. Our results demonstrate that, even without parameter tuning, Attention-Seeker outperforms most baseline models, achieving state-of-the-art performance on three out of four datasets, particularly excelling in extracting keyphrases from long documents.
CREAM: Comparison-Based Reference-Free ELO-Ranked Automatic Evaluation for Meeting Summarization
Authors: Ziwei Gong, Lin Ai, Harshsaiprasad Deshpande, Alexander Johnson, Emmy Phung, Zehui Wu, Ahmad Emami, Julia Hirschberg
Date: 2024-09-17 00:00:00
Tasks: Meeting Summarization

Large Language Models (LLMs) have spurred interest in automatic evaluation methods for summarization, offering a faster, more cost-effective alternative to human evaluation. However, existing methods often fall short when applied to complex tasks like long-context summarizations and dialogue-based meeting summarizations. In this paper, we introduce CREAM (Comparison-Based Reference-Free Elo-Ranked Automatic Evaluation for Meeting Summarization), a novel framework that addresses the unique challenges of evaluating meeting summaries. CREAM leverages a combination of chain-of-thought reasoning and key facts alignment to assess conciseness and completeness of model-generated summaries without requiring reference. By employing an ELO ranking system, our approach provides a robust mechanism for comparing the quality of different models or prompt configurations.
American Sign Language to Text Translation using Transformer and Seq2Seq with LSTM
Authors: Gregorius Guntur Sunardi Putra, Adifa Widyadhani Chanda D'Layla, Dimas Wahono, Riyanarto Sarno, Agus Tri Haryono
Date: 2024-09-17 00:00:00
Tasks: Machine Translation, Sign Language Translation, Translation

Sign language translation is one of the important issues in communication between deaf and hearing people, as it expresses words through hand, body, and mouth movements. American Sign Language is one of the sign languages used, one of which is the alphabetic sign. The development of neural machine translation technology is moving towards sign language translation. Transformer became the state-of-the-art in natural language processing. This study compares the Transformer with the Sequence-to-Sequence (Seq2Seq) model in translating sign language to text. In addition, an experiment was conducted by adding Residual Long Short-Term Memory (ResidualLSTM) in the Transformer. The addition of ResidualLSTM to the Transformer reduces the performance of the Transformer model by 23.37% based on the BLEU Score value. In comparison, the Transformer itself increases the BLEU Score value by 28.14 compared to the Seq2Seq model.
ReXErr: Synthesizing Clinically Meaningful Errors in Diagnostic Radiology Reports
Authors: Vishwanatha M. Rao, Serena Zhang, Julian N. Acosta, Subathra Adithan, Pranav Rajpurkar
Date: 2024-09-17 00:00:00
Tasks: Diagnostic

Accurately interpreting medical images and writing radiology reports is a critical but challenging task in healthcare. Both human-written and AI-generated reports can contain errors, ranging from clinical inaccuracies to linguistic mistakes. To address this, we introduce ReXErr, a methodology that leverages Large Language Models to generate representative errors within chest X-ray reports. Working with board-certified radiologists, we developed error categories that capture common mistakes in both human and AI-generated reports. Our approach uses a novel sampling scheme to inject diverse errors while maintaining clinical plausibility. ReXErr demonstrates consistency across error categories and produces errors that closely mimic those found in real-world scenarios. This method has the potential to aid in the development and evaluation of report correction algorithms, potentially enhancing the quality and reliability of radiology reporting.
Predicting Punctuation in Ancient Chinese Texts: A Multi-Layered LSTM and Attention-Based Approach
Authors: Tracy Cai, Kimmy Chang, Fahad Nabi
Date: 2024-09-16 00:00:00

It was only until the 20th century when the Chinese language began using punctuation. In fact, many ancient Chinese texts contain thousands of lines with no distinct punctuation marks or delimiters in sight. The lack of punctuation in such texts makes it difficult for humans to identify when there pauses or breaks between particular phrases and understand the semantic meaning of the written text (Mogahed, 2012). As a result, unless one was educated in the ancient time period, many readers of ancient Chinese would have significantly different interpretations of the texts. We propose an approach to predict the location (and type) of punctuation in ancient Chinese texts that extends the work of Oh et al (2017) by leveraging a bidirectional multi-layered LSTM with a multi-head attention mechanism as inspired by Luong et al.'s (2015) discussion of attention-based architectures. We find that the use of multi-layered LSTMs and multi-head attention significantly outperforms RNNs that don't incorporate such components when evaluating ancient Chinese texts.
Semantics Preserving Emoji Recommendation with Large Language Models
Authors: Zhongyi Qiu, Kangyi Qiu, Hanjia Lyu, Wei Xiong, Jiebo Luo
Date: 2024-09-16 00:00:00

Emojis have become an integral part of digital communication, enriching text by conveying emotions, tone, and intent. Existing emoji recommendation methods are primarily evaluated based on their ability to match the exact emoji a user chooses in the original text. However, they ignore the essence of users' behavior on social media in that each text can correspond to multiple reasonable emojis. To better assess a model's ability to align with such real-world emoji usage, we propose a new semantics preserving evaluation framework for emoji recommendation, which measures a model's ability to recommend emojis that maintain the semantic consistency with the user's text. To evaluate how well a model preserves semantics, we assess whether the predicted affective state, demographic profile, and attitudinal stance of the user remain unchanged. If these attributes are preserved, we consider the recommended emojis to have maintained the original semantics. The advanced abilities of Large Language Models (LLMs) in understanding and generating nuanced, contextually relevant output make them well-suited for handling the complexities of semantics preserving emoji recommendation. To this end, we construct a comprehensive benchmark to systematically assess the performance of six proprietary and open-source LLMs using different prompting techniques on our task. Our experiments demonstrate that GPT-4o outperforms other LLMs, achieving a semantics preservation score of 79.23%. Additionally, we conduct case studies to analyze model biases in downstream classification tasks and evaluate the diversity of the recommended emojis.
NaviQAte: Functionality-Guided Web Application Navigation
Authors: Mobina Shahbandeh, Parsa Alian, Noor Nashid, Ali Mesbah
Date: 2024-09-16 00:00:00
Tasks: Decision Making

End-to-end web testing is challenging due to the need to explore diverse web application functionalities. Current state-of-the-art methods, such as WebCanvas, are not designed for broad functionality exploration; they rely on specific, detailed task descriptions, limiting their adaptability in dynamic web environments. We introduce NaviQAte, which frames web application exploration as a question-and-answer task, generating action sequences for functionalities without requiring detailed parameters. Our three-phase approach utilizes advanced large language models like GPT-4o for complex decision-making and cost-effective models, such as GPT-4o mini, for simpler tasks. NaviQAte focuses on functionality-guided web application navigation, integrating multi-modal inputs such as text and images to enhance contextual understanding. Evaluations on the Mind2Web-Live and Mind2Web-Live-Abstracted datasets show that NaviQAte achieves a 44.23% success rate in user task navigation and a 38.46% success rate in functionality navigation, representing a 15% and 33% improvement over WebCanvas. These results underscore the effectiveness of our approach in advancing automated web application testing.
Visualizing Temporal Topic Embeddings with a Compass
Authors: Daniel Palamarchuk, Lemara Williams, Brian Mayer, Thomas Danielson, Rebecca Faust, Larry Deschaine, Chris North
Date: 2024-09-16 00:00:00
Tasks: Diversity, Dynamic Topic Modeling, Word Embeddings

Dynamic topic modeling is useful at discovering the development and change in latent topics over time. However, present methodology relies on algorithms that separate document and word representations. This prevents the creation of a meaningful embedding space where changes in word usage and documents can be directly analyzed in a temporal context. This paper proposes an expansion of the compass-aligned temporal Word2Vec methodology into dynamic topic modeling. Such a method allows for the direct comparison of word and document embeddings across time in dynamic topics. This enables the creation of visualizations that incorporate temporal word embeddings within the context of documents into topic visualizations. In experiments against the current state-of-the-art, our proposed method demonstrates overall competitive performance in topic relevancy and diversity across temporal datasets of varying size. Simultaneously, it provides insightful visualizations focused on temporal word embeddings while maintaining the insights provided by global topic evolution, advancing our understanding of how topics evolve over time.
Language Models and Retrieval Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports
Authors: Mohamed Sobhi Jabal, Pranav Warman, Jikai Zhang, Kartikeye Gupta, Ayush Jain, Maciej Mazurowski, Walter Wiggins, Kirti Magudia, Evan Calabrese
Date: 2024-09-15 00:00:00
Tasks: Diagnostic, Model Selection, Privacy Preserving, Prompt Engineering, Quantization, RAG, Retrieval, Retrieval-augmented Generation

Purpose: To develop and evaluate an automated system for extracting structured clinical information from unstructured radiology and pathology reports using open-weights large language models (LMs) and retrieval augmented generation (RAG), and to assess the effects of model configuration variables on extraction performance. Methods and Materials: The study utilized two datasets: 7,294 radiology reports annotated for Brain Tumor Reporting and Data System (BT-RADS) scores and 2,154 pathology reports annotated for isocitrate dehydrogenase (IDH) mutation status. An automated pipeline was developed to benchmark the performance of various LMs and RAG configurations. The impact of model size, quantization, prompting strategies, output formatting, and inference parameters was systematically evaluated. Results: The best performing models achieved over 98% accuracy in extracting BT-RADS scores from radiology reports and over 90% for IDH mutation status extraction from pathology reports. The top model being medical fine-tuned llama3. Larger, newer, and domain fine-tuned models consistently outperformed older and smaller models. Model quantization had minimal impact on performance. Few-shot prompting significantly improved accuracy. RAG improved performance for complex pathology reports but not for shorter radiology reports. Conclusions: Open LMs demonstrate significant potential for automated extraction of structured clinical data from unstructured clinical reports with local privacy-preserving application. Careful model selection, prompt engineering, and semi-automated optimization using annotated data are critical for optimal performance. These approaches could be reliable enough for practical use in research workflows, highlighting the potential for human-machine collaboration in healthcare data extraction.
Contrastive Learning in Memristor-based Neuromorphic Systems
Authors: Cory Merkel, Alexander Ororbia
Date: 2024-09-17 00:00:00
Tasks: Contrastive Learning

Spiking neural networks, the third generation of artificial neural networks, have become an important family of neuron-based models that sidestep many of the key limitations facing modern-day backpropagation-trained deep networks, including their high energy inefficiency and long-criticized biological implausibility. In this work, we design and investigate a proof-of-concept instantiation of contrastive-signal-dependent plasticity (CSDP), a neuromorphic form of forward-forward-based, backpropagation-free learning. Our experimental simulations demonstrate that a hardware implementation of CSDP is capable of learning simple logic functions without the need to resort to complex gradient calculations.
Evolving a Multi-Population Evolutionary-QAOA on Distributed QPUs
Authors: Francesca Schiavello, Edoardo Altamura, Ivano Tavernelli, Stefano Mensa, Benjamin Symons
Date: 2024-09-16 00:00:00

Our research combines an Evolutionary Algorithm (EA) with a Quantum Approximate Optimization Algorithm (QAOA) to update the ansatz parameters, in place of traditional gradient-based methods, and benchmark on the Max-Cut problem. We demonstrate that our Evolutionary-QAOA (E-QAOA) pairing performs on par or better than a COBYLA-based QAOA in terms of solution accuracy and variance, for $d$-3 regular graphs between 4 and 26 nodes, using both $max\_count$ and Conditional Value at Risk (CVaR) for fitness function evaluations. Furthermore, we take our algorithm one step further and present a novel approach by presenting a multi-population EA distributed on two QPUs, which evolves independent and isolated populations in parallel, classically communicating elite individuals. Experiments were conducted on both simulators and IBM quantum hardware, and we investigated the relative performance accuracy and variance.
Performance of Cross-Validated Targeted Maximum Likelihood Estimation
Authors: Matthew J. Smith, Rachael V. Phillips, Camille Maringe, Miguel Angel Luque-Fernandez
Date: 2024-09-17 00:00:00
Tasks: Causal Inference, regression

Background: Advanced methods for causal inference, such as targeted maximum likelihood estimation (TMLE), require certain conditions for statistical inference. However, in situations where there is not differentiability due to data sparsity or near-positivity violations, the Donsker class condition is violated. In such situations, TMLE variance can suffer from inflation of the type I error and poor coverage, leading to conservative confidence intervals. Cross-validation of the TMLE algorithm (CVTMLE) has been suggested to improve on performance compared to TMLE in settings of positivity or Donsker class violations. We aim to investigate the performance of CVTMLE compared to TMLE in various settings. Methods: We utilised the data-generating mechanism as described in Leger et al. (2022) to run a Monte Carlo experiment under different Donsker class violations. Then, we evaluated the respective statistical performances of TMLE and CVTMLE with different super learner libraries, with and without regression tree methods. Results: We found that CVTMLE vastly improves confidence interval coverage without adversely affecting bias, particularly in settings with small sample sizes and near-positivity violations. Furthermore, incorporating regression trees using standard TMLE with ensemble super learner-based initial estimates increases bias and variance leading to invalid statistical inference. Conclusions: It has been shown that when using CVTMLE the Donsker class condition is no longer necessary to obtain valid statistical inference when using regression trees and under either data sparsity or near-positivity violations. We show through simulations that CVTMLE is much less sensitive to the choice of the super learner library and thereby provides better estimation and inference in cases where the super learner library uses more flexible candidates and is prone to overfitting.
Calibrated Multivariate Regression with Localized PIT Mappings
Authors: Lucas Kock, G. S. Rodrigues, Scott A. Sisson, Nadja Klein, David J. Nott
Date: 2024-09-17 00:00:00
Tasks: regression

Calibration ensures that predicted uncertainties align with observed uncertainties. While there is an extensive literature on recalibration methods for univariate probabilistic forecasts, work on calibration for multivariate forecasts is much more limited. This paper introduces a novel post-hoc recalibration approach that addresses multivariate calibration for potentially misspecified models. Our method involves constructing local mappings between vectors of marginal probability integral transform values and the space of observations, providing a flexible and model free solution applicable to continuous, discrete, and mixed responses. We present two versions of our approach: one uses K-nearest neighbors, and the other uses normalizing flows. Each method has its own strengths in different situations. We demonstrate the effectiveness of our approach on two real data applications: recalibrating a deep neural network's currency exchange rate forecast and improving a regression model for childhood malnutrition in India for which the multivariate response has both discrete and continuous components.