pwc-archive/papers-with-abstracts huggingface dataset:
LookHere: Vision Transformers with Directed Attention Generalize and Extrapolate
Authors: Anthony Fuller, Daniel G. Kyrollos, Yousef Yassin, James R. Green
Date: 2024-05-22 00:00:00
Tasks: Adversarial Attack, Attribute, Avg, Diversity, Position

High-resolution images offer more information about scenes that can improve model accuracy. However, the dominant model architecture in computer vision, the vision transformer (ViT), cannot effectively leverage larger images without finetuning -- ViTs poorly extrapolate to more patches at test time, although transformers offer sequence length flexibility. We attribute this shortcoming to the current patch position encoding methods, which create a distribution shift when extrapolating. We propose a drop-in replacement for the position encoding of plain ViTs that restricts attention heads to fixed fields of view, pointed in different directions, using 2D attention masks. Our novel method, called LookHere, provides translation-equivariance, ensures attention head diversity, and limits the distribution shift that attention heads face when extrapolating. We demonstrate that LookHere improves performance on classification (avg. 1.6%), against adversarial attack (avg. 5.4%), and decreases calibration error (avg. 1.5%) -- on ImageNet without extrapolation. With extrapolation, LookHere outperforms the current SoTA position encoding method, 2D-RoPE, by 21.7% on ImageNet when trained at $224^2$ px and tested at $1024^2$ px. Additionally, we release a high-resolution test set to improve the evaluation of high-resolution image classifiers, called ImageNet-HR.
TS40K: a 3D Point Cloud Dataset of Rural Terrain and Electrical Transmission System
Authors: Diogo Lavado, Cláudia Soares, Alessandra Micheletti, Ricardo Santos, André Coelho, João Santos
Date: 2024-05-22 00:00:00
Tasks: 3D Object Detection, 3D Semantic Segmentation, Autonomous Driving, object-detection, Object Detection, Scene Segmentation, Scene Understanding, Semantic Segmentation

Research on supervised learning algorithms in 3D scene understanding has risen in prominence and witness great increases in performance across several datasets. The leading force of this research is the problem of autonomous driving followed by indoor scene segmentation. However, openly available 3D data on these tasks mainly focuses on urban scenarios. In this paper, we propose TS40K, a 3D point cloud dataset that encompasses more than 40,000 Km on electrical transmission systems situated in European rural terrain. This is not only a novel problem for the research community that can aid in the high-risk mission of power-grid inspection, but it also offers 3D point clouds with distinct characteristics from those in self-driving and indoor 3D data, such as high point-density and no occlusion. In our dataset, each 3D point is labeled with 1 out of 22 annotated classes. We evaluate the performance of state-of-the-art methods on our dataset concerning 3D semantic segmentation and 3D object detection. Finally, we provide a comprehensive analysis of the results along with key challenges such as using labels that were not originally intended for learning tasks.
Multilingual Audio-Visual Speech Recognition with Hybrid CTC/RNN-T Fast Conformer
Authors: Maxime Burchi, Krishna C. Puvvada, Jagadeesh Balam, Boris Ginsburg, Radu Timofte
Date: 2024-03-14 00:00:00
Tasks: Audio-Visual Speech Recognition, Robust Speech Recognition, speech-recognition, Speech Recognition, Visual Speech Recognition

Humans are adept at leveraging visual cues from lip movements for recognizing speech in adverse listening conditions. Audio-Visual Speech Recognition (AVSR) models follow similar approach to achieve robust speech recognition in noisy conditions. In this work, we present a multilingual AVSR model incorporating several enhancements to improve performance and audio noise robustness. Notably, we adapt the recently proposed Fast Conformer model to process both audio and visual modalities using a novel hybrid CTC/RNN-T architecture. We increase the amount of audio-visual training data for six distinct languages, generating automatic transcriptions of unlabelled multilingual datasets (VoxCeleb2 and AVSpeech). Our proposed model achieves new state-of-the-art performance on the LRS3 dataset, reaching WER of 0.8%. On the recently introduced MuAViC benchmark, our model yields an absolute average-WER reduction of 11.9% in comparison to the original baseline. Finally, we demonstrate the ability of the proposed model to perform audio-only, visual-only, and audio-visual speech recognition at test time.
Sketch2Prototype: Rapid Conceptual Design Exploration and Prototyping with Generative AI
Authors: Kristen M. Edwards, Brandon Man, Faez Ahmed
Date: 2024-03-26 00:00:00
Tasks: Image to 3D

Sketch2Prototype is an AI-based framework that transforms a hand-drawn sketch into a diverse set of 2D images and 3D prototypes through sketch-to-text, text-to-image, and image-to-3D stages. This framework, shown across various sketches, rapidly generates text, image, and 3D modalities for enhanced early-stage design exploration. We show that using text as an intermediate modality outperforms direct sketch-to-3D baselines for generating diverse and manufacturable 3D models. We find limitations in current image-to-3D techniques, while noting the value of the text modality for user-feedback and iterative design augmentation.
A Novel Feature Map Enhancement Technique Integrating Residual CNN and Transformer for Alzheimer Diseases Diagnosis
Authors: Saddam Hussain Khan
Date: 2024-03-30 00:00:00
Tasks: Computational Efficiency

Alzheimer diseases (ADs) involves cognitive decline and abnormal brain protein accumulation, necessitating timely diagnosis for effective treatment. Therefore, CAD systems leveraging deep learning advancements have demonstrated success in AD detection but pose computational intricacies and the dataset minor contrast, structural, and texture variations. In this regard, a novel hybrid FME-Residual-HSCMT technique is introduced, comprised of residual CNN and Transformer concepts to capture global and local fine-grained AD analysis in MRI. This approach integrates three distinct elements: a novel CNN Meet Transformer (HSCMT), customized residual learning CNN, and a new Feature Map Enhancement (FME) strategy to learn diverse morphological, contrast, and texture variations of ADs. The proposed HSCMT at the initial stage utilizes stem convolution blocks that are integrated with CMT blocks followed by systematic homogenous and structural (HS) operations. The customized CMT block encapsulates each element with global contextual interactions through multi-head attention and facilitates computational efficiency through lightweight. Moreover, inverse residual and stem CNN in customized CMT enables effective extraction of local texture information and handling vanishing gradients. Furthermore, in the FME strategy, residual CNN blocks utilize TL-based generated auxiliary and are combined with the proposed HSCMT channels at the target level to achieve diverse enriched feature space. Finally, diverse enhanced channels are fed into a novel spatial attention mechanism for optimal pixel selection to reduce redundancy and discriminate minor contrast and texture inter-class variation. The proposed achieves an F1-score (98.55%), an accuracy of 98.42% and a sensitivity of 98.50%, a precision of 98.60% on the standard Kaggle dataset, and demonstrates outperformance existing ViTs and CNNs methods.
Faithful Attention Explainer: Verbalizing Decisions Based on Discriminative Features
Authors: Yao Rong, David Scheerer, Enkelejda Kasneci
Date: 2024-05-16 00:00:00
Tasks: Decision Making, Sentence

In recent years, model explanation methods have been designed to interpret model decisions faithfully and intuitively so that users can easily understand them. In this paper, we propose a framework, Faithful Attention Explainer (FAE), capable of generating faithful textual explanations regarding the attended-to features. Towards this goal, we deploy an attention module that takes the visual feature maps from the classifier for sentence generation. Furthermore, our method successfully learns the association between features and words, which allows a novel attention enforcement module for attention explanation. Our model achieves promising performance in caption quality metrics and a faithful decision-relevance metric on two datasets (CUB and ACT-X). In addition, we show that FAE can interpret gaze-based human attention, as human gaze indicates the discriminative features that humans use for decision-making, demonstrating the potential of deploying human gaze for advanced human-AI interaction.
An Assessment of Model-On-Model Deception
Authors: Julius Heitkoetter, Michael Gerovitch, Laker Newhouse
Date: 2024-05-10 00:00:00
Tasks: MMLU, model

The trustworthiness of highly capable language models is put at risk when they are able to produce deceptive outputs. Moreover, when models are vulnerable to deception it undermines reliability. In this paper, we introduce a method to investigate complex, model-on-model deceptive scenarios. We create a dataset of over 10,000 misleading explanations by asking Llama-2 7B, 13B, 70B, and GPT-3.5 to justify the wrong answer for questions in the MMLU. We find that, when models read these explanations, they are all significantly deceived. Worryingly, models of all capabilities are successful at misleading others, while more capable models are only slightly better at resisting deception. We recommend the development of techniques to detect and defend against deception.
Large Language Models for Education: A Survey
Authors: Hanyi Xu, Wensheng Gan, Zhenlian Qi, Jiayang Wu, Philip S. Yu
Date: 2024-05-12 00:00:00
Tasks: Autonomous Driving, speech-recognition, Speech Recognition, Survey

Artificial intelligence (AI) has a profound impact on traditional education. In recent years, large language models (LLMs) have been increasingly used in various applications such as natural language processing, computer vision, speech recognition, and autonomous driving. LLMs have also been applied in many fields, including recommendation, finance, government, education, legal affairs, and finance. As powerful auxiliary tools, LLMs incorporate various technologies such as deep learning, pre-training, fine-tuning, and reinforcement learning. The use of LLMs for smart education (LLMEdu) has been a significant strategic direction for countries worldwide. While LLMs have shown great promise in improving teaching quality, changing education models, and modifying teacher roles, the technologies are still facing several challenges. In this paper, we conduct a systematic review of LLMEdu, focusing on current technologies, challenges, and future developments. We first summarize the current state of LLMEdu and then introduce the characteristics of LLMs and education, as well as the benefits of integrating LLMs into education. We also review the process of integrating LLMs into the education industry, as well as the introduction of related technologies. Finally, we discuss the challenges and problems faced by LLMEdu, as well as prospects for future optimization of LLMEdu.
DuetRAG: Collaborative Retrieval-Augmented Generation
Authors: Dian Jiao, Li Cai, Jingsheng Huang, Wenqiao Zhang, Siliang Tang, Yueting Zhuang
Date: 2024-05-12 00:00:00
Tasks: Philosophy, RAG, Retrieval, Retrieval-augmented Generation

Retrieval-Augmented Generation (RAG) methods augment the input of Large Language Models (LLMs) with relevant retrieved passages, reducing factual errors in knowledge-intensive tasks. However, contemporary RAG approaches suffer from irrelevant knowledge retrieval issues in complex domain questions (e.g., HotPot QA) due to the lack of corresponding domain knowledge, leading to low-quality generations. To address this issue, we propose a novel Collaborative Retrieval-Augmented Generation framework, DuetRAG. Our bootstrapping philosophy is to simultaneously integrate the domain fintuning and RAG models to improve the knowledge retrieval quality, thereby enhancing generation quality. Finally, we demonstrate DuetRAG' s matches with expert human researchers on HotPot QA.
MathDivide: Improved mathematical reasoning by large language models
Authors: Saksham Sahai Srivastava, Ashutosh Gandhi
Date: 2024-05-12 00:00:00
Tasks: GSM8K, Logical Reasoning, Math, Mathematical Reasoning

Large language models have been proven to be capable of handling complex linguistic and cognitive tasks. Therefore their usage has been extended to tasks requiring logical reasoning ability such as Mathematics. In this paper, we propose a prompting technique called MathDivide that breaks down the mathematical problem into simpler subproblems. Each of the subproblems is formulated as an algebraic expression whose value is evaluated by the Python code generated by the LLM for the corresponding algebraic expression. The values fed to the Python code are the numerical values provided in the problem statement. The solutions for the subproblems are composed together to obtain the final answer for the problem statement. Finally, the final answer is compared to the correct answer. If the final answer matches the correct answer, it is produced as output else a refinement prompt is fed to the LLM. We experiment with this prompting technique on both closed-source LLM models and open-source LLM models using GSM8K dataset. The results obtained demonstrate that MathDivide was able to significantly outperform the leading prompting technique called Math-prompter.
Auto FAQ Generation
Authors: Anjaneya Teja Kalvakolanu, NagaSai Chandra, Michael Fekadu
Date: 2024-05-13 00:00:00
Tasks: Philosophy, Question Generation, Question-Generation, Reading Comprehension, Sentence, Text Summarization

FAQ documents are commonly used with text documents and websites to provide important information in the form of question answer pairs to either aid in reading comprehension or provide a shortcut to the key ideas. We suppose that salient sentences from a given document serve as a good proxy fro the answers to an aggregated set of FAQs from readers. We propose a system for generating FAQ documents that extract the salient questions and their corresponding answers from sizeable text documents scraped from the Stanford Encyclopedia of Philosophy. We use existing text summarization, sentence ranking via the Text rank algorithm, and question-generation tools to create an initial set of questions and answers. Finally, we apply some heuristics to filter out invalid questions. We use human evaluation to rate the generated questions on grammar, whether the question is meaningful, and whether the question's answerability is present within a summarized context. On average, participants thought 71 percent of the questions were meaningful.
Control Token with Dense Passage Retrieval
Authors: Juhwan Lee, Jisu Kim
Date: 2024-05-13 00:00:00
Tasks: Hallucination, Passage Retrieval, RAG, Retrieval, Retrieval-augmented Generation

This study addresses the hallucination problem in large language models (LLMs). We adopted Retrieval-Augmented Generation(RAG) (Lewis et al., 2020), a technique that involves embedding relevant information in the prompt to obtain accurate answers. However, RAG also faced inherent issues in retrieving correct information. To address this, we employed the Dense Passage Retrieval(DPR) (Karpukhin et al., 2020) model for fetching domain-specific documents related to user queries. Despite this, the DPR model still lacked accuracy in document retrieval. We enhanced the DPR model by incorporating control tokens, achieving significantly superior performance over the standard DPR model, with a 13% improvement in Top-1 accuracy and a 4% improvement in Top-20 accuracy.
MetaReflection: Learning Instructions for Language Agents using Past Reflections
Authors: Priyanshu Gupta, Shashank Kirtania, Ananya Singha, Sumit Gulwani, Arjun Radhakrishna, Sherry Shi, Gustavo Soares
Date: 2024-05-13 00:00:00
Tasks: Logical Reasoning, Question Answering, Semantic Similarity, Semantic Textual Similarity, Vulnerability Detection

The popularity of Large Language Models (LLMs) have unleashed a new age ofLanguage Agents for solving a diverse range of tasks. While contemporary frontier LLMs are capable enough to power reasonably good Language agents, the closed-API model makes it hard to improve in cases they perform sub-optimally. To address this, recent works have explored ways to improve their performance using techniques like self-reflection and prompt optimization. Unfortunately, techniques like self-reflection can be used only in an online setup, while contemporary prompt optimization techniques are designed and tested to work on simple tasks. To this end, we introduce MetaReflection, a novel offline reinforcement learning technique that enhances the performance of Language Agents by augmenting a semantic memory based on experiential learnings from past trials. We demonstrate the efficacy of MetaReflection by evaluating across multiple domains, including complex logical reasoning, biomedical semantic similarity, open world question answering, and vulnerability threat detection, in Infrastructure-as-Code, spanning different agent designs. MetaReflection boosts Language agents' performance by 4% to 16.82% over the raw GPT-4 baseline and performs on par with existing state-of-the-art prompt optimization techniques while requiring fewer LLM calls.
UCCIX: Irish-eXcellence Large Language Model
Authors: Khanh-Tung Tran, Barry O'Sullivan, Hoang D. Nguyen
Date: 2024-05-13 00:00:00
Tasks: Benchmarking, Language Modeling, Language Modelling, Large Language Model, model, Question Answering

The development of Large Language Models (LLMs) has predominantly focused on high-resource languages, leaving extremely low-resource languages like Irish with limited representation. This work presents UCCIX, a pioneering effort on the development of an open-source Irish-based LLM. We propose a novel framework for continued pre-training of LLMs specifically adapted for extremely low-resource languages, requiring only a fraction of the textual data typically needed for training LLMs according to scaling laws. Our model, based on Llama 2-13B, outperforms much larger models on Irish language tasks with up to 12% performance improvement, showcasing the effectiveness and efficiency of our approach. We also contribute comprehensive Irish benchmarking datasets, including IrishQA, a question-answering dataset, and Irish version of MT-bench. These datasets enable rigorous evaluation and facilitate future research in Irish LLM systems. Our work aims to preserve and promote the Irish language, knowledge, and culture of Ireland in the digital era while providing a framework for adapting LLMs to other indigenous languages.
Unveiling Social Media Comments with a Novel Named Entity Recognition System for Identity Groups
Authors: Andrés Carvallo, Tamara Quiroga, Carlos Aspillaga, Marcelo Mendoza
Date: 2024-05-13 00:00:00
Tasks: Articles, Hate Speech Detection, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Sentence

While civilized users employ social media to stay informed and discuss daily occurrences, haters perceive these platforms as fertile ground for attacking groups and individuals. The prevailing approach to counter this phenomenon involves detecting such attacks by identifying toxic language. Effective platform measures aim to report haters and block their network access. In this context, employing hate speech detection methods aids in identifying these attacks amidst vast volumes of text, which are impossible for humans to analyze manually. In our study, we expand upon the usual hate speech detection methods, typically based on text classifiers, to develop a Named Entity Recognition (NER) System for Identity Groups. To achieve this, we created a dataset that allows extending a conventional NER to recognize identity groups. Consequently, our tool not only detects whether a sentence contains an attack but also tags the sentence tokens corresponding to the mentioned group. Results indicate that the model performs competitively in identifying groups with an average f1-score of 0.75, outperforming in identifying ethnicity attack spans with an f1-score of 0.80 compared to other identity groups. Moreover, the tool shows an outstanding generalization capability to minority classes concerning sexual orientation and gender, achieving an f1-score of 0.77 and 0.72, respectively. We tested the utility of our tool in a case study on social media, annotating and comparing comments from Facebook related to news mentioning identity groups. The case study reveals differences in the types of attacks recorded, effectively detecting named entities related to the categories of the analyzed news articles. Entities are accurately tagged within their categories, with a negligible error rate for inter-category tagging.
Divergent Creativity in Humans and Large Language Models
Authors: Antoine Bellemare-Pepin, François Lespinasse, Philipp Thölke, Yann Harel, Kory Mathewson, Jay A. Olson, Yoshua Bengio, Karim Jerbi
Date: 2024-05-13 00:00:00
Tasks: Benchmarking

The recent surge in the capabilities of Large Language Models (LLMs) has led to claims that they are approaching a level of creativity akin to human capabilities. This idea has sparked a blend of excitement and apprehension. However, a critical piece that has been missing in this discourse is a systematic evaluation of LLM creativity, particularly in comparison to human divergent thinking. To bridge this gap, we leverage recent advances in creativity science to build a framework for in-depth analysis of divergent creativity in both state-of-the-art LLMs and a substantial dataset of 100,000 humans. We found evidence suggesting that LLMs can indeed surpass human capabilities in specific creative tasks such as divergent association and creative writing. Our quantitative benchmarking framework opens up new paths for the development of more creative LLMs, but it also encourages more granular inquiries into the distinctive elements that constitute human inventive thought processes, compared to those that can be artificially generated.
QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models
Authors: Wei Wang, Zhaowei Li, Qi Xu, Yiqing Cai, Hang Song, Qi Qi, Ran Zhou, Zhida Huang, Tao Wang, Li Xiao
Date: 2024-05-14 00:00:00
Tasks: Contrastive Learning, Denoising, Diversity, Knowledge Distillation, Language Modelling

The deployment of large language models (LLMs) faces considerable challenges concerning resource constraints and inference efficiency. Recent research has increasingly focused on smaller, task-specific models enhanced by distilling knowledge from LLMs. However, prior studies have often overlooked the diversity and quality of knowledge, especially the untapped potential of negative knowledge. Constructing effective negative knowledge remains severely understudied. In this paper, we introduce a novel framework called quality-guided contrastive rationale distillation aimed at enhancing reasoning capabilities through contrastive knowledge learning. For positive knowledge, we enrich its diversity through temperature sampling and employ self-consistency for further denoising and refinement. For negative knowledge, we propose an innovative self-adversarial approach that generates low-quality rationales by sampling previous iterations of smaller language models, embracing the idea that one can learn from one's own weaknesses. A contrastive loss is developed to distill both positive and negative knowledge into smaller language models, where an online-updating discriminator is integrated to assess qualities of rationales and assign them appropriate weights, optimizing the training process. Through extensive experiments across multiple reasoning tasks, we demonstrate that our method consistently outperforms existing distillation techniques, yielding higher-quality rationales.