pwc-archive/papers-with-abstracts huggingface dataset:
Novel Architecture for Distributed Travel Data Integration and Service Provision Using Microservices
Authors: Biman Barua, M. Shamim Kaiser
Date: 2024-10-31 00:00:00
Tasks: Data Integration

This paper introduces a microservices architecture for the purpose of enhancing the flexibility and performance of an airline reservation system. The architectural design incorporates Redis cache technologies, two different messaging systems (Kafka and RabbitMQ), two types of storages (MongoDB, and PostgreSQL). It also introduces authorization techniques, including secure communication through OAuth2 and JWT which is essential with the management of high-demand travel services. According to selected indicators, the architecture provides an impressive level of data consistency at 99.5% and a latency of data propagation of less than 75 ms allowing rapid and reliable intercommunication between microservices. A system throughput of 1050 events per second was achieved so that the acceptability level was maintained even during peak time. Redis caching reduced a 92% cache hit ratio on the database thereby lowering the burden on the database and increasing the speed of response. Further improvement of the systems scalability was done through the use of Docker and Kubernetes which enabled services to be expanded horizontally to cope with the changes in demand. The error rates were very low, at 0.2% further enhancing the efficiency of the system in handling real-time data integration. This approach is suggested to meet the specific needs of the airline reservation system. It is secure, fast, scalable, all serving to improve the user experience as well as the efficiency of operations. The low latency and high data integration levels and prevaiing efficient usage of the resources demonstrates the architecture ability to offer continued support in the ever growing high demand situations.
GPT or BERT: why not both?
Authors: Lucas Georges Gabriel Charpentier, David Samuel
Date: 2024-10-31 00:00:00
Tasks: Causal Language Modeling, Language Modeling, Language Modelling, Masked Language Modeling

We present a simple way to merge masked language modeling with causal language modeling. This hybrid training objective results in a model that combines the strengths of both modeling paradigms within a single transformer stack: GPT-BERT can be transparently used like any standard causal or masked language model. We test the pretraining process that enables this flexible behavior on the BabyLM Challenge 2024. The results show that the hybrid pretraining outperforms masked-only or causal-only models. We openly release the models, training corpora and code.
Thought Space Explorer: Navigating and Expanding Thought Space for Large Language Model Reasoning
Authors: Jinghan Zhang, Fengran Mo, Xiting Wang, Kunpeng Liu
Date: 2024-10-31 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Recent advances in large language models (LLMs) have demonstrated their potential in handling complex reasoning tasks, which are usually achieved by constructing a thought chain to guide the model to solve the problem with multi-step thinking. However, existing methods often remain confined to previously explored solution spaces and thus overlook the critical blind spot within LLMs' cognitive range. To address these issues, we design the Thought Space Explorer (TSE), a novel framework to expand and optimize thought structures to guide LLMs to explore their blind spots of thinking. By generating new reasoning steps and branches based on the original thought structure with various designed strategies, TSE broadens the thought space and alleviates the impact of blind spots for LLM reasoning. Experimental results on multiple levels of reasoning tasks demonstrate the efficacy of TSE. We also conduct extensive analysis to understand how structured and expansive thought can contribute to unleashing the potential of LLM reasoning capabilities.
Don't Touch My Diacritics
Authors: Kyle Gorman, Yuval Pinter
Date: 2024-10-31 00:00:00
Tasks: Multilingual NLP

The common practice of preprocessing text before feeding it into NLP models introduces many decision points which have unintended consequences on model performance. In this opinion piece, we focus on the handling of diacritics in texts originating in many languages and scripts. We demonstrate, through several case studies, the adverse effects of inconsistent encoding of diacritized characters and of removing diacritics altogether. We call on the community to adopt simple but necessary steps across all models and toolkits in order to improve handling of diacritized text and, by extension, increase equity in multilingual NLP.
Multi-environment Topic Models
Authors: Dominic Sobhani, Amir Feder, David Blei
Date: 2024-10-31 00:00:00
Tasks: Topic Models

Probabilistic topic models are a powerful tool for extracting latent themes from large text datasets. In many text datasets, we also observe per-document covariates (e.g., source, style, political affiliation) that act as environments that modulate a "global" (environment-agnostic) topic representation. Accurately learning these representations is important for prediction on new documents in unseen environments and for estimating the causal effect of topics on real-world outcomes. To this end, we introduce the Multi-environment Topic Model (MTM), an unsupervised probabilistic model that separates global and environment-specific terms. Through experimentation on various political content, from ads to tweets and speeches, we show that the MTM produces interpretable global topics with distinct environment-specific words. On multi-environment data, the MTM outperforms strong baselines in and out-of-distribution. It also enables the discovery of accurate causal effects.
Desert Camels and Oil Sheikhs: Arab-Centric Red Teaming of Frontier LLMs
Authors: Muhammed Saeed, Elgizouli Mohamed, Mukhtar Mohamed, Shaina Raza, Muhammad Abdul-Mageed, Shady Shehata
Date: 2024-10-31 00:00:00
Tasks: Red Teaming

Large language models (LLMs) are widely used but raise ethical concerns due to embedded social biases. This study examines LLM biases against Arabs versus Westerners across eight domains, including women's rights, terrorism, and anti-Semitism and assesses model resistance to perpetuating these biases. To this end, we create two datasets: one to evaluate LLM bias toward Arabs versus Westerners and another to test model safety against prompts that exaggerate negative traits ("jailbreaks"). We evaluate six LLMs -- GPT-4, GPT-4o, LlaMA 3.1 (8B & 405B), Mistral 7B, and Claude 3.5 Sonnet. We find 79% of cases displaying negative biases toward Arabs, with LlaMA 3.1-405B being the most biased. Our jailbreak tests reveal GPT-4o as the most vulnerable, despite being an optimized version, followed by LlaMA 3.1-8B and Mistral 7B. All LLMs except Claude exhibit attack success rates above 87% in three categories. We also find Claude 3.5 Sonnet the safest, but it still displays biases in seven of eight categories. Despite being an optimized version of GPT4, We find GPT-4o to be more prone to biases and jailbreaks, suggesting optimization flaws. Our findings underscore the pressing need for more robust bias mitigation strategies and strengthened security measures in LLMs.
Detecting text level intellectual influence with knowledge graph embeddings
Authors: Lucian Li, Eryclis Silva
Date: 2024-10-31 00:00:00
Tasks: Articles, Graph Embedding, Graph Neural Network, Knowledge Graph Embedding, Knowledge Graph Embeddings, Knowledge Graphs

Introduction: Tracing the spread of ideas and the presence of influence is a question of special importance across a wide range of disciplines, ranging from intellectual history to cultural analytics, computational social science, and the science of science. Method: We collect a corpus of open source journal articles, generate Knowledge Graph representations using the Gemini LLM, and attempt to predict the existence of citations between sampled pairs of articles using previously published methods and a novel Graph Neural Network based embedding model. Results: We demonstrate that our knowledge graph embedding method is superior at distinguishing pairs of articles with and without citation. Once trained, it runs efficiently and can be fine-tuned on specific corpora to suit individual researcher needs. Conclusion(s): This experiment demonstrates that the relationships encoded in a knowledge graph, especially the types of concepts brought together by specific relations can encode information capable of revealing intellectual influence. This suggests that further work in analyzing document level knowledge graphs to understand latent structures could provide valuable insights.
Speech is More Than Words: Do Speech-to-Text Translation Systems Leverage Prosody?
Authors: Ioannis Tsiamas, Matthias Sperber, Andrew Finch, Sarthak Garg
Date: 2024-10-31 00:00:00
Tasks: Rhythm, speech-recognition, Speech Recognition, Speech-to-Text, Speech-to-Text Translation, text-to-speech, Text to Speech, Translation

The prosody of a spoken utterance, including features like stress, intonation and rhythm, can significantly affect the underlying semantics, and as a consequence can also affect its textual translation. Nevertheless, prosody is rarely studied within the context of speech-to-text translation (S2TT) systems. In particular, end-to-end (E2E) systems have been proposed as well-suited for prosody-aware translation because they have direct access to the speech signal when making translation decisions, but the understanding of whether this is successful in practice is still limited. A main challenge is the difficulty of evaluating prosody awareness in translation. To address this challenge, we introduce an evaluation methodology and a focused benchmark (named ContraProST) aimed at capturing a wide range of prosodic phenomena. Our methodology uses large language models and controllable text-to-speech (TTS) to generate contrastive examples. Through experiments in translating English speech into German, Spanish, and Japanese, we find that (a) S2TT models possess some internal representation of prosody, but the prosody signal is often not strong enough to affect the translations, (b) E2E systems outperform cascades of speech recognition and text translation systems, confirming their theoretical advantage in this regard, and (c) certain cascaded systems also capture prosodic information in the translation, but only to a lesser extent that depends on the particulars of the transcript's surface form.
Multilingual Pretraining Using a Large Corpus Machine-Translated from a Single Source Language
Authors: Jiayi Wang, Yao Lu, Maurice Weber, Max Ryabinin, Yihong Chen, Raphael Tang, Pontus Stenetorp
Date: 2024-10-31 00:00:00

English, as a very high-resource language, enables the pretraining of high-quality large language models (LLMs). The same cannot be said for most other languages, as leading LLMs still underperform for non-English languages, likely due to a gap in the quality and diversity of the available multilingual pretraining corpora. In this work, we find that machine-translated text from a single high-quality source language can contribute significantly to the pretraining of multilingual LLMs. We translate FineWeb-Edu, a high-quality English web dataset, into French, German, and Spanish, resulting in a final 300B-token dataset, which we call TransWeb-Edu, and pretrain a 1.3B-parameter model, CuatroLLM, from scratch on this dataset. Across five non-English reasoning tasks, we show that CuatroLLM matches or outperforms state-of-the-art multilingual models trained using closed data, such as Llama3.2 and Gemma2, despite using an order of magnitude less data, such as about 6% of the tokens used for Llama3.2's training. We further demonstrate that with additional domain-specific pretraining, amounting to less than 1% of TransWeb-Edu, CuatroLLM surpasses the state of the art in multilingual reasoning. To promote reproducibility, we release our corpus, models, and training pipeline under open licenses at hf.co/britllm/CuatroLLM.
Language Models can Self-Lengthen to Generate Long Texts
Authors: Shanghaoran Quan, Tianyi Tang, Bowen Yu, An Yang, Dayiheng Liu, Bofei Gao, Jianhong Tu, Yichang Zhang, Jingren Zhou, Junyang Lin
Date: 2024-10-31 00:00:00
Tasks: Text Generation

Recent advancements in Large Language Models (LLMs) have significantly enhanced their ability to process long contexts, yet a notable gap remains in generating long, aligned outputs. This limitation stems from a training gap where pre-training lacks effective instructions for long-text generation, and post-training data primarily consists of short query-response pairs. Current approaches, such as instruction backtranslation and behavior imitation, face challenges including data quality, copyright issues, and constraints on proprietary model usage. In this paper, we introduce an innovative iterative training framework called Self-Lengthen that leverages only the intrinsic knowledge and skills of LLMs without the need for auxiliary data or proprietary models. The framework consists of two roles: the Generator and the Extender. The Generator produces the initial response, which is then split and expanded by the Extender. This process results in a new, longer response, which is used to train both the Generator and the Extender iteratively. Through this process, the models are progressively trained to handle increasingly longer responses. Experiments on benchmarks and human evaluations show that Self-Lengthen outperforms existing methods in long-text generation, when applied to top open-source LLMs such as Qwen2 and LLaMA3. Our code is publicly available at https://github.com/QwenLM/Self-Lengthen.
Responsible Retrieval Augmented Generation for Climate Decision Making from Documents
Authors: Matyas Juhasz, Kalyan Dutia, Henry Franks, Conor Delahunty, Patrick Fawbert Mills, Harrison Pim
Date: 2024-10-31 00:00:00
Tasks: Decision Making, RAG, Retrieval, Retrieval-augmented Generation

Climate decision making is constrained by the complexity and inaccessibility of key information within lengthy, technical, and multi-lingual documents. Generative AI technologies offer a promising route for improving the accessibility of information contained within these documents, but suffer from limitations. These include (1) a tendency to hallucinate or mis-represent information, (2) difficulty in steering or guaranteeing properties of generated output, and (3) reduced performance in specific technical domains. To address these challenges, we introduce a novel evaluation framework with domain-specific dimensions tailored for climate-related documents. We then apply this framework to evaluate Retrieval-Augmented Generation (RAG) approaches and assess retrieval- and generation-quality within a prototype tool that answers questions about individual climate law and policy documents. In addition, we publish a human-annotated dataset and scalable automated evaluation tools, with the aim of facilitating broader adoption and robust assessment of these systems in the climate domain. Our findings highlight the key components of responsible deployment of RAG to enhance decision-making, while also providing insights into user experience (UX) considerations for safely deploying such systems to build trust with users in high-risk domains.
Audio Is the Achilles' Heel: Red Teaming Audio Large Multimodal Models
Authors: Hao Yang, Lizhen Qu, Ehsan Shareghi, Gholamreza Haffari
Date: 2024-10-31 00:00:00
Tasks: Red Teaming, Safety Alignment

Large Multimodal Models (LMMs) have demonstrated the ability to interact with humans under real-world conditions by combining Large Language Models (LLMs) and modality encoders to align multimodal information (visual and auditory) with text. However, such models raise new safety challenges of whether models that are safety-aligned on text also exhibit consistent safeguards for multimodal inputs. Despite recent safety-alignment research on vision LMMs, the safety of audio LMMs remains under-explored. In this work, we comprehensively red team the safety of five advanced audio LMMs under three settings: (i) harmful questions in both audio and text formats, (ii) harmful questions in text format accompanied by distracting non-speech audio, and (iii) speech-specific jailbreaks. Our results under these settings demonstrate that open-source audio LMMs suffer an average attack success rate of 69.14% on harmful audio questions, and exhibit safety vulnerabilities when distracted with non-speech audio noise. Our speech-specific jailbreaks on Gemini-1.5-Pro achieve an attack success rate of 70.67% on the harmful query benchmark. We provide insights on what could cause these reported safety-misalignments. Warning: this paper contains offensive examples.
The Automated Verification of Textual Claims (AVeriTeC) Shared Task
Authors: Michael Schlichtkrull, Yulong Chen, Chenxi Whitehouse, Zhenyun Deng, Mubashara Akhtar, Rami Aly, Zhijiang Guo, Christos Christodoulopoulos, Oana Cocarascu, Arpit Mittal, James Thorne, Andreas Vlachos
Date: 2024-10-31 00:00:00

The Automated Verification of Textual Claims (AVeriTeC) shared task asks participants to retrieve evidence and predict veracity for real-world claims checked by fact-checkers. Evidence can be found either via a search engine, or via a knowledge store provided by the organisers. Submissions are evaluated using AVeriTeC score, which considers a claim to be accurately verified if and only if both the verdict is correct and retrieved evidence is considered to meet a certain quality threshold. The shared task received 21 submissions, 18 of which surpassed our baseline. The winning team was TUDA_MAI with an AVeriTeC score of 63%. In this paper we describe the shared task, present the full results, and highlight key takeaways from the shared task.
GigaCheck: Detecting LLM-generated Content
Authors: Irina Tolstykh, Aleksandra Tsybina, Sergey Yakubson, Aleksandr Gordeev, Vladimir Dokholyan, Maksim Kuprashevich
Date: 2024-10-31 00:00:00
Tasks: Binary text classification, Boundary Detection, LLM-generated Text Detection, Text Classification, Text Detection

With the increasing quality and spread of LLM-based assistants, the amount of LLM-generated content is growing rapidly. In many cases and tasks, such texts are already indistinguishable from those written by humans, and the quality of generation tends to only increase. At the same time, detection methods are developing more slowly, making it challenging to prevent misuse of generative AI technologies. In this work, we investigate the task of generated text detection by proposing the GigaCheck. Our research explores two approaches: (i) distinguishing human-written texts from LLM-generated ones, and (ii) detecting LLM-generated intervals in Human-Machine collaborative texts. For the first task, our approach utilizes a general-purpose LLM, leveraging its extensive language abilities to fine-tune efficiently for the downstream task of LLM-generated text detection, achieving high performance even with limited data. For the second task, we propose a novel approach that combines computer vision and natural language processing techniques. Specifically, we use a fine-tuned general-purpose LLM in conjunction with a DETR-like detection model, adapted from computer vision, to localize AI-generated intervals within text. We evaluate the GigaCheck on five classification datasets with English texts and three datasets designed for Human-Machine collaborative text analysis. Our results demonstrate that GigaCheck outperforms previous methods, even in out-of-distribution settings, establishing a strong baseline across all datasets.
Instruction-Tuning Llama-3-8B Excels in City-Scale Mobility Prediction
Authors: Peizhi Tang, Chuang Yang, Tong Xing, Xiaohang Xu, Renhe Jiang, Kaoru Sezaki
Date: 2024-10-31 00:00:00
Tasks: Disaster Response, Language Modeling, Language Modelling, Large Language Model, Prediction, Zero-shot Generalization

Human mobility prediction plays a critical role in applications such as disaster response, urban planning, and epidemic forecasting. Traditional methods often rely on designing crafted, domain-specific models, and typically focus on short-term predictions, which struggle to generalize across diverse urban environments. In this study, we introduce Llama-3-8B-Mob, a large language model fine-tuned with instruction tuning, for long-term citywide mobility prediction -- in a Q&A manner. We validate our approach using large-scale human mobility data from four metropolitan areas in Japan, focusing on predicting individual trajectories over the next 15 days. The results demonstrate that Llama-3-8B-Mob excels in modeling long-term human mobility -- surpassing the state-of-the-art on multiple prediction metrics. It also displays strong zero-shot generalization capabilities -- effectively generalizing to other cities even when fine-tuned only on limited samples from a single city. Source codes are available at https://github.com/TANGHULU6/Llama3-8B-Mob.
Improbable Bigrams Expose Vulnerabilities of Incomplete Tokens in Byte-Level Tokenizers
Authors: Eugene Jang, Kimin Lee, Jin-Woo Chung, Keuntae Park, Seungwon Shin
Date: 2024-10-31 00:00:00
Tasks: Hallucination

Tokenization is a crucial step that bridges human-readable text with model-readable discrete tokens. However, recent studies have revealed that tokenizers can be exploited to elicit unwanted model behaviors. In this work, we investigate incomplete tokens, i.e., undecodable tokens with stray bytes resulting from byte-level byte-pair encoding (BPE) tokenization. We hypothesize that such tokens are heavily reliant on their adjacent tokens and are fragile when paired with unfamiliar tokens. To demonstrate this vulnerability, we introduce improbable bigrams: out-of-distribution combinations of incomplete tokens designed to exploit their dependency. Our experiments show that improbable bigrams are significantly prone to hallucinatory behaviors. Surprisingly, alternative tokenizations of the same phrases result in drastically lower rates of hallucination (93% reduction in Llama3.1). We caution against the potential vulnerabilities introduced by byte-level BPE tokenizers, which may impede the development of trustworthy language models.
Pseudo-Conversation Injection for LLM Goal Hijacking
Authors: Zheng Chen, Buhui Yao
Date: 2024-10-31 00:00:00
Tasks: Adversarial Attack

Goal hijacking is a type of adversarial attack on Large Language Models (LLMs) where the objective is to manipulate the model into producing a specific, predetermined output, regardless of the user's original input. In goal hijacking, an attacker typically appends a carefully crafted malicious suffix to the user's prompt, which coerces the model into ignoring the user's original input and generating the target response. In this paper, we introduce a novel goal hijacking attack method called Pseudo-Conversation Injection, which leverages the weaknesses of LLMs in role identification within conversation contexts. Specifically, we construct the suffix by fabricating responses from the LLM to the user's initial prompt, followed by a prompt for a malicious new task. This leads the model to perceive the initial prompt and fabricated response as a completed conversation, thereby executing the new, falsified prompt. Following this approach, we propose three Pseudo-Conversation construction strategies: Targeted Pseudo-Conversation, Universal Pseudo-Conversation, and Robust Pseudo-Conversation. These strategies are designed to achieve effective goal hijacking across various scenarios. Our experiments, conducted on two mainstream LLM platforms including ChatGPT and Qwen, demonstrate that our proposed method significantly outperforms existing approaches in terms of attack effectiveness.