pwc-archive/papers-with-abstracts huggingface dataset:
DASB -- Discrete Audio and Speech Benchmark
Authors: Pooneh Mousavi, Luca Della Libera, Jarod Duret, Artem Ploujnikov, Cem Subakan, Mirco Ravanelli
Date: 2024-06-20 00:00:00
Tasks: Benchmarking, Emotion Recognition, intent-classification, Intent Classification, Keyword Spotting, Speaker Identification, Speech Enhancement, speech-recognition, Speech Recognition, text-to-speech, Text to Speech

Discrete audio tokens have recently gained considerable attention for their potential to connect audio and language processing, enabling the creation of modern multimodal large language models. Ideal audio tokens must effectively preserve phonetic and semantic content along with paralinguistic information, speaker identity, and other details. While several types of audio tokens have been recently proposed, identifying the optimal tokenizer for various tasks is challenging due to the inconsistent evaluation settings in existing studies. To address this gap, we release the Discrete Audio and Speech Benchmark (DASB), a comprehensive leaderboard for benchmarking discrete audio tokens across a wide range of discriminative tasks, including speech recognition, speaker identification and verification, emotion recognition, keyword spotting, and intent classification, as well as generative tasks such as speech enhancement, separation, and text-to-speech. Our results show that, on average, semantic tokens outperform compression tokens across most discriminative and generative tasks. However, the performance gap between semantic tokens and standard continuous representations remains substantial, highlighting the need for further research in this field.
Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective
Authors: Yunfei Liu, Jintang Li, Yuehe Chen, Ruofan Wu, Ericbk Wang, Jing Zhou, Sheng Tian, Shuheng Shen, Xing Fu, Changhua Meng, Weiqiang Wang, Liang Chen
Date: 2024-06-20 00:00:00
Tasks: Clustering, Community Detection, Contrastive Learning, Graph Clustering, Graph Mining

Graph clustering, a fundamental and challenging task in graph mining, aims to classify nodes in a graph into several disjoint clusters. In recent years, graph contrastive learning (GCL) has emerged as a dominant line of research in graph clustering and advances the new state-of-the-art. However, GCL-based methods heavily rely on graph augmentations and contrastive schemes, which may potentially introduce challenges such as semantic drift and scalability issues. Another promising line of research involves the adoption of modularity maximization, a popular and effective measure for community detection, as the guiding principle for clustering tasks. Despite the recent progress, the underlying mechanism of modularity maximization is still not well understood. In this work, we dig into the hidden success of modularity maximization for graph clustering. Our analysis reveals the strong connections between modularity maximization and graph contrastive learning, where positive and negative examples are naturally defined by modularity. In light of our results, we propose a community-aware graph clustering framework, coined MAGI, which leverages modularity maximization as a contrastive pretext task to effectively uncover the underlying information of communities in graphs, while avoiding the problem of semantic drift. Extensive experiments on multiple graph datasets verify the effectiveness of MAGI in terms of scalability and clustering performance compared to state-of-the-art graph clustering methods. Notably, MAGI easily scales a sufficiently large graph with 100M nodes while outperforming strong baselines.
VAIYAKARANA : A Benchmark for Automatic Grammar Correction in Bangla
Authors: Pramit Bhattacharyya, Arnab Bhattacharya
Date: 2024-06-20 00:00:00
Tasks: Sentence

Bangla (Bengali) is the fifth most spoken language globally and, yet, the problem of automatic grammar correction in Bangla is still in its nascent stage. This is mostly due to the need for a large corpus of grammatically incorrect sentences, with their corresponding correct counterparts. The present state-of-the-art techniques to curate a corpus for grammatically wrong sentences involve random swapping, insertion and deletion of words. However,these steps may not always generate grammatically wrong sentences in Bangla. In this work, we propose a pragmatic approach to generate grammatically wrong sentences in Bangla. We first categorize the different kinds of errors in Bangla into 5 broad classes and 12 finer classes. We then use these to generate grammatically wrong sentences systematically from a correct sentence. This approach can generate a large number of wrong sentences and can, thus, mitigate the challenge of lacking a large corpus for neural networks. We provide a dataset, Vaiyakarana, consisting of 92,830 grammatically incorrect sentences as well as 18,426 correct sentences. We also collected 619 human-generated sentences from essays written by Bangla native speakers. This helped us to understand errors that are more frequent. We evaluated our corpus against neural models and LLMs and also benchmark it against human evaluators who are native speakers of Bangla. Our analysis shows that native speakers are far more accurate than state-of-the-art models to detect whether the sentence is grammatically correct. Our methodology of generating erroneous sentences can be applied for most other Indian languages as well.
Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning
Authors: Chaojie Wang, Yanchen Deng, Zhiyi Lyu, Liang Zeng, Jujie He, Shuicheng Yan, Bo An
Date: 2024-06-20 00:00:00
Tasks: GSM8K, Heuristic Search, Math, mbpp

Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. However, the auto-regressive generation process makes LLMs prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. In this paper, by casting multi-step reasoning of LLMs as a heuristic search problem, we aim to alleviate the pathology by introducing Q*, a general, versatile and agile framework for guiding LLMs decoding process with deliberative planning. By learning a plug-and-play Q-value model as heuristic function for estimating expected future rewards, our Q* can effectively guide LLMs to select the most promising next reasoning step without fine-tuning LLMs for the current task, which avoids the significant computational overhead and potential risk of performance degeneration on other tasks. Extensive experiments on GSM8K, MATH and MBPP demonstrate the superiority of our method, contributing to improving the reasoning performance of existing open-source LLMs.
Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs
Authors: Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Z. Pan, Wen Zhang, Huajun Chen
Date: 2024-06-20 00:00:00
Tasks: Knowledge Distillation, Knowledge Graphs, Question Answering, Retrieval

Improving the performance of large language models (LLMs) in complex question-answering (QA) scenarios has always been a research focal point. Recent studies have attempted to enhance LLMs' performance by combining step-wise planning with external retrieval. While effective for advanced models like GPT-3.5, smaller LLMs face challenges in decomposing complex questions, necessitating supervised fine-tuning. Previous work has relied on manual annotation and knowledge distillation from teacher LLMs, which are time-consuming and not accurate enough. In this paper, we introduce a novel framework for enhancing LLMs' planning capabilities by using planning data derived from knowledge graphs (KGs). LLMs fine-tuned with this data have improved planning capabilities, better equipping them to handle complex QA tasks that involve retrieval. Evaluations on multiple datasets, including our newly proposed benchmark, highlight the effectiveness of our framework and the benefits of KG-derived planning data.
FairX: A comprehensive benchmarking tool for model analysis using fairness, utility, and explainability
Authors: Md Fahim Sikder, Resmi Ramachandranpillai, Daniel de Leng, Fredrik Heintz
Date: 2024-06-20 00:00:00
Tasks: Benchmarking, Fairness

We present FairX, an open-source Python-based benchmarking tool designed for the comprehensive analysis of models under the umbrella of fairness, utility, and eXplainability (XAI). FairX enables users to train benchmarking bias-mitigation models and evaluate their fairness using a wide array of fairness metrics, data utility metrics, and generate explanations for model predictions, all within a unified framework. Existing benchmarking tools do not have the way to evaluate synthetic data generated from fair generative models, also they do not have the support for training fair generative models either. In FairX, we add fair generative models in the collection of our fair-model library (pre-processing, in-processing, post-processing) and evaluation metrics for evaluating the quality of synthetic fair data. This version of FairX supports both tabular and image datasets. It also allows users to provide their own custom datasets. The open-source FairX benchmarking package is publicly available at \url{https://github.com/fahim-sikder/FairX}.
QPaug: Question and Passage Augmentation for Open-Domain Question Answering of LLMs
Authors: Minsang Kim, Cheoneum Park, Seungjun Baek
Date: 2024-06-20 00:00:00
Tasks: Open-Domain Question Answering, Question Answering, RAG, Retrieval, Retrieval-augmented Generation

Retrieval-augmented generation (RAG) has received much attention for Open-domain question-answering (ODQA) tasks as a means to compensate for the parametric knowledge of large language models (LLMs). While previous approaches focused on processing retrieved passages to remove irrelevant context, they still rely heavily on the quality of retrieved passages which can degrade if the question is ambiguous or complex. In this paper, we propose a simple yet efficient method called question and passage augmentation (QPaug) via LLMs for open-domain QA. QPaug first decomposes the original questions into multiple-step sub-questions. By augmenting the original question with detailed sub-questions and planning, we are able to make the query more specific on what needs to be retrieved, improving the retrieval performance. In addition, to compensate for the case where the retrieved passages contain distracting information or divided opinions, we augment the retrieved passages with self-generated passages by LLMs to guide the answer extraction. Experimental results show that QPaug outperforms the previous state-of-the-art and achieves significant performance gain over existing RAG methods. The source code is available at \url{https://github.com/kmswin1/QPaug}.
Step-Back Profiling: Distilling User History for Personalized Scientific Writing
Authors: Xiangru Tang, Xingyao Zhang, Yanjun Shao, Jie Wu, Yilun Zhao, Arman Cohan, Ming Gong, Dongmei Zhang, Mark Gerstein
Date: 2024-06-20 00:00:00

Large language models (LLM) excel at a variety of natural language processing tasks, yet they struggle to generate personalized content for individuals, particularly in real-world scenarios like scientific writing. Addressing this challenge, we introduce STEP-BACK PROFILING to personalize LLMs by distilling user history into concise profiles, including essential traits and preferences of users. To conduct the experiments, we construct a Personalized Scientific Writing (PSW) dataset to study multi-user personalization. PSW requires the models to write scientific papers given specialized author groups with diverse academic backgrounds. As for the results, we demonstrate the effectiveness of capturing user characteristics via STEP-BACK PROFILING for collaborative writing. Moreover, our approach outperforms the baselines by up to 3.6 points on the general personalization benchmark (LaMP), including 7 personalization LLM tasks. Our ablation studies validate the contributions of different components in our method and provide insights into our task definition. Our dataset and code are available at \url{https://github.com/gersteinlab/step-back-profiling}.
The Impact of AI on Perceived Job Decency and Meaningfulness: A Case Study
Authors: Kuntal Ghosh, Shadan Sadeghian
Date: 2024-06-20 00:00:00

The proliferation of Artificial Intelligence (AI) in workplaces stands to change the way humans work, with job satisfaction intrinsically linked to work life. Existing research on human-AI collaboration tends to prioritize performance over the experiential aspects of work. In contrast, this paper explores the impact of AI on job decency and meaningfulness in workplaces. Through interviews in the Information Technology (IT) domain, we not only examined the current work environment, but also explored the perceived evolution of the workplace ecosystem with the introduction of an AI. Findings from the preliminary exploratory study reveal that respondents tend to visualize a workplace where humans continue to play a dominant role, even with the introduction of advanced AIs. In this prospective scenario, AI is seen as serving as a complement rather than replacing the human workforce. Furthermore, respondents believe that the introduction of AI will maintain or potentially increase overall job satisfaction.
On the Evaluation Practices in Multilingual NLP: Can Machine Translation Offer an Alternative to Human Translations?
Authors: Rochelle Choenni, Sara Rajaee, Christof Monz, Ekaterina Shutova
Date: 2024-06-20 00:00:00
Tasks: Machine Translation, Multilingual NLP, Translation

While multilingual language models (MLMs) have been trained on 100+ languages, they are typically only evaluated across a handful of them due to a lack of available test data in most languages. This is particularly problematic when assessing MLM's potential for low-resource and unseen languages. In this paper, we present an analysis of existing evaluation frameworks in multilingual NLP, discuss their limitations, and propose several directions for more robust and reliable evaluation practices. Furthermore, we empirically study to what extent machine translation offers a {reliable alternative to human translation} for large-scale evaluation of MLMs across a wide set of languages. We use a SOTA translation model to translate test data from 4 tasks to 198 languages and use them to evaluate three MLMs. We show that while the selected subsets of high-resource test languages are generally sufficiently representative of a wider range of high-resource languages, we tend to overestimate MLMs' ability on low-resource languages. Finally, we show that simpler baselines can achieve relatively strong performance without having benefited from large-scale multilingual pretraining.
Intelligent Interface: Enhancing Lecture Engagement with Didactic Activity Summaries
Authors: Anna Wróblewska, Marcel Witas, Kinga Frańczak, Arkadiusz Kniaź, Siew Ann Cheong, Tan Seng Chee, Janusz Hołyst, Marcin Paprzycki
Date: 2024-06-20 00:00:00
Tasks: Automatic Speech Recognition, speech-recognition, Speech Recognition

Recently, multiple applications of machine learning have been introduced. They include various possibilities arising when image analysis methods are applied to, broadly understood, video streams. In this context, a novel tool, developed for academic educators to enhance the teaching process by automating, summarizing, and offering prompt feedback on conducting lectures, has been developed. The implemented prototype utilizes machine learning-based techniques to recognise selected didactic and behavioural teachers' features within lecture video recordings. Specifically, users (teachers) can upload their lecture videos, which are preprocessed and analysed using machine learning models. Next, users can view summaries of recognized didactic features through interactive charts and tables. Additionally, stored ML-based prediction results support comparisons between lectures based on their didactic content. In the developed application text-based models trained on lecture transcriptions, with enhancements to the transcription quality, by adopting an automatic speech recognition solution are applied. Furthermore, the system offers flexibility for (future) integration of new/additional machine-learning models and software modules for image and video analysis.
VeriFlow: Modeling Distributions for Neural Network Verification
Authors: Faried Abu Zaid, Daniel Neider, Mustafa Yalçıner
Date: 2024-06-20 00:00:00

Formal verification has emerged as a promising method to ensure the safety and reliability of neural networks. Naively verifying a safety property amounts to ensuring the safety of a neural network for the whole input space irrespective of any training or test set. However, this also implies that the safety of the neural network is checked even for inputs that do not occur in the real-world and have no meaning at all, often resulting in spurious errors. To tackle this shortcoming, we propose the VeriFlow architecture as a flow based density model tailored to allow any verification approach to restrict its search to the some data distribution of interest. We argue that our architecture is particularly well suited for this purpose because of two major properties. First, we show that the transformation and log-density function that are defined by our model are piece-wise affine. Therefore, the model allows the usage of verifiers based on SMT with linear arithmetic. Second, upper density level sets (UDL) of the data distribution take the shape of an $L^p$-ball in the latent space. As a consequence, representations of UDLs specified by a given probability are effectively computable in latent space. This allows for SMT and abstract interpretation approaches with fine-grained, probabilistically interpretable, control regarding on how (a)typical the inputs subject to verification are.
CityNav: Language-Goal Aerial Navigation Dataset with Geographic Information
Authors: Jungdae Lee, Taiki Miyanishi, Shuhei Kurita, Koya Sakamoto, Daichi Azuma, Yutaka Matsuo, Nakamasa Inoue
Date: 2024-06-20 00:00:00
Tasks: Vision and Language Navigation

Vision-and-language navigation (VLN) aims to guide autonomous agents through real-world environments by integrating visual and linguistic cues. Despite notable advancements in ground-level navigation, the exploration of aerial navigation using these modalities remains limited. This gap primarily arises from a lack of suitable resources for real-world, city-scale aerial navigation studies. To remedy this gap, we introduce CityNav, a novel dataset explicitly designed for language-guided aerial navigation in photorealistic 3D environments of real cities. CityNav comprises 32k natural language descriptions paired with human demonstration trajectories, collected via a newly developed web-based 3D simulator. Each description identifies a navigation goal, utilizing the names and locations of landmarks within actual cities. As an initial step toward addressing this challenge, we provide baseline models of navigation agents that incorporate an internal 2D spatial map representing landmarks referenced in the descriptions. We have benchmarked the latest aerial navigation methods alongside our proposed baseline model on the CityNav dataset. The findings are revealing: (i) our aerial agent model trained on human demonstration trajectories, outperform those trained on shortest path trajectories by a large margin; (ii) incorporating 2D spatial map information markedly and robustly enhances navigation performance at a city scale; (iii) despite the use of map information, our challenging CityNav dataset reveals a persistent performance gap between our baseline models and human performance. To foster further research in aerial VLN, we have made the dataset and code available at https://water-cookie.github.io/city-nav-proj/
Enhancing robustness of data-driven SHM models: adversarial training with circle loss
Authors: Xiangli Yang, Xijie Deng, Hanwei Zhang, Yang Zou, Jianxi Yang
Date: 2024-06-20 00:00:00
Tasks: Structural Health Monitoring

Structural health monitoring (SHM) is critical to safeguarding the safety and reliability of aerospace, civil, and mechanical infrastructure. Machine learning-based data-driven approaches have gained popularity in SHM due to advancements in sensors and computational power. However, machine learning models used in SHM are vulnerable to adversarial examples -- even small changes in input can lead to different model outputs. This paper aims to address this problem by discussing adversarial defenses in SHM. In this paper, we propose an adversarial training method for defense, which uses circle loss to optimize the distance between features in training to keep examples away from the decision boundary. Through this simple yet effective constraint, our method demonstrates substantial improvements in model robustness, surpassing existing defense mechanisms.
Raising the Bar: Investigating the Values of Large Language Models via Generative Evolving Testing
Authors: Han Jiang, Xiaoyuan Yi, Zhihua Wei, Ziang Xiao, Shu Wang, Xing Xie
Date: 2024-06-20 00:00:00
Tasks: Ethics

Warning: Contains harmful model outputs. Despite significant advancements, the propensity of Large Language Models (LLMs) to generate harmful and unethical content poses critical challenges. Measuring value alignment of LLMs becomes crucial for their regulation and responsible deployment. Although numerous benchmarks have been constructed to assess social bias, toxicity, and ethical issues in LLMs, those static benchmarks suffer from evaluation chronoeffect, in which, as models rapidly evolve, existing benchmarks may leak into training data or become saturated, overestimating ever-developing LLMs. To tackle this problem, we propose GETA, a novel generative evolving testing approach based on adaptive testing methods in measurement theory. Unlike traditional adaptive testing methods that rely on a static test item pool, GETA probes the underlying moral boundaries of LLMs by dynamically generating test items tailored to model capability. GETA co-evolves with LLMs by learning a joint distribution of item difficulty and model value conformity, thus effectively addressing evaluation chronoeffect. We evaluated various popular LLMs with GETA and demonstrated that 1) GETA can dynamically create difficulty-tailored test items and 2) GETA's evaluation results are more consistent with models' performance on unseen OOD and i.i.d. items, laying the groundwork for future evaluation paradigms.
EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms
Authors: Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Dongsheng Li, Deqing Yang
Date: 2024-06-20 00:00:00
Tasks: Evolutionary Algorithms

The rise of powerful large language models (LLMs) has spurred a new trend in building LLM-based autonomous agents for solving complex tasks, especially multi-agent systems. Despite the remarkable progress, we notice that existing works are heavily dependent on human-designed frameworks, which greatly limits the functional scope and scalability of agent systems. How to automatically extend the specialized agent to multi-agent systems to improve task-solving capability still remains a significant challenge. In this paper, we introduce EvoAgent, a generic method to automatically extend expert agents to multi-agent systems via the evolutionary algorithm, thereby improving the effectiveness of LLM-based agents in solving tasks. Specifically, we consider the existing agent frameworks as the initial individual and then apply a series of evolutionary operators (e.g., mutation, crossover, selection, etc.) to generate multiple agents with diverse agent settings. EvoAgent can be generalized to any LLM-based agent framework, and can automatically extend the existing agent framework to multi-agent systems without any extra human designs. Experimental results across various tasks have shown that EvoAgent can automatically generate multiple expert agents and significantly enhance the task-solving capabilities of LLM-based agents.