pwc-archive/papers-with-abstracts huggingface dataset:
Automating Thematic Analysis: How LLMs Analyse Controversial Topics
Authors: Awais Hameed Khan, Hiruni Kegalle, Rhea D'Silva, Ned Watt, Daniel Whelan-Shamy, Lida Ghahremanlou, Liam Magee
Date: 2024-05-11 00:00:00

Large Language Models (LLMs) are promising analytical tools. They can augment human epistemic, cognitive and reasoning abilities, and support 'sensemaking', making sense of a complex environment or subject by analysing large volumes of data with a sensitivity to context and nuance absent in earlier text processing systems. This paper presents a pilot experiment that explores how LLMs can support thematic analysis of controversial topics. We compare how human researchers and two LLMs GPT-4 and Llama 2 categorise excerpts from media coverage of the controversial Australian Robodebt scandal. Our findings highlight intriguing overlaps and variances in thematic categorisation between human and machine agents, and suggest where LLMs can be effective in supporting forms of discourse and thematic analysis. We argue LLMs should be used to augment, and not replace human interpretation, and we add further methodological insights and reflections to existing research on the application of automation to qualitative research methods. We also introduce a novel card-based design toolkit, for both researchers and practitioners to further interrogate LLMs as analytical tools.
Finding structure in logographic writing with library learning
Authors: Guangyuan Jiang, Matthias Hofer, Jiayuan Mao, Lionel Wong, Joshua B. Tenenbaum, Roger P. Levy
Date: 2024-05-11 00:00:00
Tasks: Inductive Bias, Program Synthesis

One hallmark of human language is its combinatoriality -- reusing a relatively small inventory of building blocks to create a far larger inventory of increasingly complex structures. In this paper, we explore the idea that combinatoriality in language reflects a human inductive bias toward representational efficiency in symbol systems. We develop a computational framework for discovering structure in a writing system. Built on top of state-of-the-art library learning and program synthesis techniques, our computational framework discovers known linguistic structures in the Chinese writing system and reveals how the system evolves towards simplification under pressures for representational efficiency. We demonstrate how a library learning approach, utilizing learned abstractions and compression, may help reveal the fundamental computational principles that underlie the creation of combinatorial structures in human cognition, and offer broader insights into the evolution of efficient communication systems.
The Ghanaian NLP Landscape: A First Look
Authors: Sheriff Issaka, Zhaoyi Zhang, Mihir Heda, Keyi Wang, Yinka Ajibola, Ryan DeMar, Xuefeng Du
Date: 2024-05-10 00:00:00
Tasks: Diversity

Despite comprising one-third of global languages, African languages are critically underrepresented in Artificial Intelligence (AI), threatening linguistic diversity and cultural heritage. Ghanaian languages, in particular, face an alarming decline, with documented extinction and several at risk. This study pioneers a comprehensive survey of Natural Language Processing (NLP) research focused on Ghanaian languages, identifying methodologies, datasets, and techniques employed. Additionally, we create a detailed roadmap outlining challenges, best practices, and future directions, aiming to improve accessibility for researchers. This work serves as a foundational resource for Ghanaian NLP research and underscores the critical need for integrating global linguistic diversity into AI development.
Execution-Based Evaluation of Natural Language to Bash and PowerShell for Incident Remediation
Authors: Ngoc Phuoc An Vo, Brent Paulovicks, Vadim Sheinin
Date: 2024-05-10 00:00:00
Tasks: Code Generation, Few-Shot Learning

Given recent advancements of Large Language Models (LLMs), code generation tasks attract immense attention for wide application in different domains. In an effort to evaluate and select a best model to automatically remediate system incidents discovered by Application Performance Monitoring (APM) platforms, it is crucial to verify if the generated code is syntactically and semantically correct, and whether it can be executed correctly as intended. However, current methods for evaluating the quality of code generated by LLMs heavily rely on surface form similarity metrics (e.g. BLEU, ROUGE, and exact/partial match) which have numerous limitations. In contrast, execution based evaluation focuses more on code functionality and does not constrain the code generation to any fixed solution. Nevertheless, designing and implementing such execution-based evaluation platform is not a trivial task. There are several works creating execution-based evaluation platforms for popular programming languages such as SQL, Python, Java, but limited or no attempts for scripting languages such as Bash and PowerShell. In this paper, we present the first execution-based evaluation platform in which we created three test suites (total 125 handcrafted test cases) to evaluate Bash (both single-line commands and multiple-line scripts) and PowerShell codes generated by LLMs. We benchmark seven closed and open-source LLMs using our platform with different techniques (zero-shot vs. few-shot learning).
LLM-Generated Black-box Explanations Can Be Adversarially Helpful
Authors: Rohan Ajwani, Shashidhar Reddy Javaji, Frank Rudzicz, Zining Zhu
Date: 2024-05-10 00:00:00
Tasks: Navigate

Large Language Models (LLMs) are becoming vital tools that help us solve and understand complex problems by acting as digital assistants. LLMs can generate convincing explanations, even when only given the inputs and outputs of these problems, i.e., in a ``black-box'' approach. However, our research uncovers a hidden risk tied to this approach, which we call *adversarial helpfulness*. This happens when an LLM's explanations make a wrong answer look right, potentially leading people to trust incorrect solutions. In this paper, we show that this issue affects not just humans, but also LLM evaluators. Digging deeper, we identify and examine key persuasive strategies employed by LLMs. Our findings reveal that these models employ strategies such as reframing the questions, expressing an elevated level of confidence, and cherry-picking evidence to paint misleading answers in a credible light. To examine if LLMs are able to navigate complex-structured knowledge when generating adversarially helpful explanations, we create a special task based on navigating through graphs. Most LLMs are not able to find alternative paths along simple graphs, indicating that their misleading explanations aren't produced by only logical deductions using complex knowledge. These findings shed light on the limitations of the black-box explanation setting and allow us to provide advice on the safe usage of LLMs.
LIVE: LaTex Interactive Visual Editing
Authors: Jinwei Lin
Date: 2024-05-10 00:00:00
Tasks: NeRF

LaTex coding is one of the main methods of writing an academic paper. When writing a paper, abundant proper visual or graphic components will represent more information volume than the textual data. However, most of the implementation of LaTex graphic items are designed as static items that have some weaknesses in representing more informative figures or tables with an interactive reading experience. To address this problem, we propose LIVE, a novel design methods idea to design interactive LaTex graphic items. To make a lucid representation of the main idea of LIVE, we designed several novels representing implementations that are interactive and enough explanation for the basic level principles. Using LIVE can design more graphic items, which we call the Gitems, and easily and automatically get the relationship of the mutual application of a specific range of papers, which will add more vitality and performance factors into writing of traditional papers especially the review papers. For vividly representing the functions of LIVE, we use the papers from NeRF as the example reference papers. The code of the implementation project is open source.
Enhanced Review Detection and Recognition: A Platform-Agnostic Approach with Application to Online Commerce
Authors: Priyabrata Karmakar, John Hawkins
Date: 2024-05-09 00:00:00

Online commerce relies heavily on user generated reviews to provide unbiased information about products that they have not physically seen. The importance of reviews has attracted multiple exploitative online behaviours and requires methods for monitoring and detecting reviews. We present a machine learning methodology for review detection and extraction, and demonstrate that it generalises for use across websites that were not contained in the training data. This method promises to drive applications for automatic detection and evaluation of reviews, regardless of their source. Furthermore, we showcase the versatility of our method by implementing and discussing three key applications for analysing reviews: Sentiment Inconsistency Analysis, which detects and filters out unreliable reviews based on inconsistencies between ratings and comments; Multi-language support, enabling the extraction and translation of reviews from various languages without relying on HTML scraping; and Fake review detection, achieved by integrating a trained NLP model to identify and distinguish between genuine and fake reviews.
Interpretable Cross-Examination Technique (ICE-T): Using highly informative features to boost LLM performance
Authors: Goran Muric, Ben Delay, Steven Minton
Date: 2024-05-08 00:00:00
Tasks: Decision Making

In this paper, we introduce the Interpretable Cross-Examination Technique (ICE-T), a novel approach that leverages structured multi-prompt techniques with Large Language Models (LLMs) to improve classification performance over zero-shot and few-shot methods. In domains where interpretability is crucial, such as medicine and law, standard models often fall short due to their "black-box" nature. ICE-T addresses these limitations by using a series of generated prompts that allow an LLM to approach the problem from multiple directions. The responses from the LLM are then converted into numerical feature vectors and processed by a traditional classifier. This method not only maintains high interpretability but also allows for smaller, less capable models to achieve or exceed the performance of larger, more advanced models under zero-shot conditions. We demonstrate the effectiveness of ICE-T across a diverse set of data sources, including medical records and legal documents, consistently surpassing the zero-shot baseline in terms of classification metrics such as F1 scores. Our results indicate that ICE-T can be used for improving both the performance and transparency of AI applications in complex decision-making environments.
Lightweight Spatial Modeling for Combinatorial Information Extraction From Documents
Authors: Yanfei Dong, Lambert Deng, Jiazheng Zhang, Xiaodong Yu, Ting Lin, Francesco Gelli, Soujanya Poria, Wee Sun Lee
Date: 2024-05-08 00:00:00

Documents that consist of diverse templates and exhibit complex spatial structures pose a challenge for document entity classification. We propose KNN-former, which incorporates a new kind of spatial bias in attention calculation based on the K-nearest-neighbor (KNN) graph of document entities. We limit entities' attention only to their local radius defined by the KNN graph. We also use combinatorial matching to address the one-to-one mapping property that exists in many documents, where one field has only one corresponding entity. Moreover, our method is highly parameter-efficient compared to existing approaches in terms of the number of trainable parameters. Despite this, experiments across various datasets show our method outperforms baselines in most entity types. Many real-world documents exhibit combinatorial properties which can be leveraged as inductive biases to improve extraction accuracy, but existing datasets do not cover these documents. To facilitate future research into these types of documents, we release a new ID document dataset that covers diverse templates and languages. We also release enhanced annotations for an existing dataset.
ChatSOS: Vector Database Augmented Generative Question Answering Assistant in Safety Engineering
Authors: Haiyang Tang, Dongping Chen, Qingzhao Chu
Date: 2024-05-08 00:00:00
Tasks: Generative Question Answering, Information Retrieval, Question Answering, Retrieval

With the rapid advancement of natural language processing technologies, generative artificial intelligence techniques, represented by large language models (LLMs), are gaining increasing prominence and demonstrating significant potential for applications in safety engineering. However, fundamental LLMs face constraints such as limited training data coverage and unreliable responses. This study develops a vector database from 117 explosion accident reports in China spanning 2013 to 2023, employing techniques such as corpus segmenting and vector embedding. By utilizing the vector database, which outperforms the relational database in information retrieval quality, we provide LLMs with richer, more relevant knowledge. Comparative analysis of LLMs demonstrates that ChatSOS significantly enhances reliability, accuracy, and comprehensiveness, improves adaptability and clarification of responses. These results illustrate the effectiveness of supplementing LLMs with an external database, highlighting their potential to handle professional queries in safety engineering and laying a foundation for broader applications.
Automated Conversion of Static to Dynamic Scheduler via Natural Language
Authors: Paul Mingzheng Tang, Kenji Kah Hoe Leong, Nowshad Shaik, Hoong Chuin Lau
Date: 2024-05-08 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation, Scheduling

In this paper, we explore the potential application of Large Language Models (LLMs) that will automatically model constraints and generate code for dynamic scheduling problems given an existing static model. Static scheduling problems are modelled and coded by optimization experts. These models may be easily obsoleted as the underlying constraints may need to be fine-tuned in order to reflect changes in the scheduling rules. Furthermore, it may be necessary to turn a static model into a dynamic one in order to cope with disturbances in the environment. In this paper, we propose a Retrieval-Augmented Generation (RAG) based LLM model to automate the process of implementing constraints for Dynamic Scheduling (RAGDyS), without seeking help from an optimization modeling expert. Our framework aims to minimize technical complexities related to mathematical modelling and computational workload for end-users, thereby allowing end-users to quickly obtain a new schedule close to the original schedule with changes reflected by natural language constraint descriptions.
Multi-level Shared Knowledge Guided Learning for Knowledge Graph Completion
Authors: Yongxue Shan, Jie zhou, Jie Peng, Xin Zhou, Jiaqian Yin, Xiaodong Wang
Date: 2024-05-08 00:00:00
Tasks: Knowledge Graph Completion, Multi-Task Learning, Prediction, Relation Prediction, Text Summarization

In the task of Knowledge Graph Completion (KGC), the existing datasets and their inherent subtasks carry a wealth of shared knowledge that can be utilized to enhance the representation of knowledge triplets and overall performance. However, no current studies specifically address the shared knowledge within KGC. To bridge this gap, we introduce a multi-level Shared Knowledge Guided learning method (SKG) that operates at both the dataset and task levels. On the dataset level, SKG-KGC broadens the original dataset by identifying shared features within entity sets via text summarization. On the task level, for the three typical KGC subtasks - head entity prediction, relation prediction, and tail entity prediction - we present an innovative multi-task learning architecture with dynamically adjusted loss weights. This approach allows the model to focus on more challenging and underperforming tasks, effectively mitigating the imbalance of knowledge sharing among subtasks. Experimental results demonstrate that SKG-KGC outperforms existing text-based methods significantly on three well-known datasets, with the most notable improvement on WN18RR.
Utilizing Large Language Models to Generate Synthetic Data to Increase the Performance of BERT-Based Neural Networks
Authors: Chancellor R. Woolsey, Prakash Bisht, Joshua Rothman, Gondy Leroy
Date: 2024-05-08 00:00:00

An important issue impacting healthcare is a lack of available experts. Machine learning (ML) models could resolve this by aiding in diagnosing patients. However, creating datasets large enough to train these models is expensive. We evaluated large language models (LLMs) for data creation. Using Autism Spectrum Disorders (ASD), we prompted ChatGPT and GPT-Premium to generate 4,200 synthetic observations to augment existing medical data. Our goal is to label behaviors corresponding to autism criteria and improve model accuracy with synthetic training data. We used a BERT classifier pre-trained on biomedical literature to assess differences in performance between models. A random sample (N=140) from the LLM-generated data was evaluated by a clinician and found to contain 83% correct example-label pairs. Augmenting data increased recall by 13% but decreased precision by 16%, correlating with higher quality and lower accuracy across pairs. Future work will analyze how different synthetic data traits affect ML outcomes.
SUTRA: Scalable Multilingual Language Model Architecture
Authors: Abhijit Bendale, Michael Sapienza, Steven Ripplinger, Simon Gibbs, Jaewon Lee, Pranav Mistry
Date: 2024-05-07 00:00:00
Tasks: Computational Efficiency, Hallucination, Language Modeling, Language Modelling, Large Language Model, Mixture-of-Experts, MMLU, model

In this paper, we introduce SUTRA, multilingual Large Language Model architecture capable of understanding, reasoning, and generating text in over 50 languages. SUTRA's design uniquely decouples core conceptual understanding from language-specific processing, which facilitates scalable and efficient multilingual alignment and learning. Employing a Mixture of Experts framework both in language and concept processing, SUTRA demonstrates both computational efficiency and responsiveness. Through extensive evaluations, SUTRA is demonstrated to surpass existing models like GPT-3.5, Llama2 by 20-30% on leading Massive Multitask Language Understanding (MMLU) benchmarks for multilingual tasks. SUTRA models are also online LLMs that can use knowledge from the internet to provide hallucination-free, factual and up-to-date responses while retaining their multilingual capabilities. Furthermore, we explore the broader implications of its architecture for the future of multilingual AI, highlighting its potential to democratize access to AI technology globally and to improve the equity and utility of AI in regions with predominantly non-English languages. Our findings suggest that SUTRA not only fills pivotal gaps in multilingual model capabilities but also establishes a new benchmark for operational efficiency and scalability in AI applications.
Analyzing Language Bias Between French and English in Conventional Multilingual Sentiment Analysis Models
Authors: Ethan Parker Wong, Faten M'hiri
Date: 2024-05-07 00:00:00
Tasks: Multilingual NLP, Sentiment Analysis, Sentiment Classification

Inspired by the 'Bias Considerations in Bilingual Natural Language Processing' report by Statistics Canada, this study delves into potential biases in multilingual sentiment analysis between English and French. Given a 50-50 dataset of French and English, we aim to determine if there exists a language bias and explore how the incorporation of more diverse datasets in the future might affect the equity of multilingual Natural Language Processing (NLP) systems. By employing Support Vector Machine (SVM) and Naive Bayes models on three balanced datasets, we reveal potential biases in multilingual sentiment classification. Utilizing Fairlearn, a tool for assessing bias in machine learning models, our findings indicate nuanced outcomes. With French data outperforming English across accuracy, recall, and F1 score metrics in both models, hinting at a language bias favoring French. However, Fairlearn's metrics suggest that the SVM approaches equitable levels with a demographic parity ratio of 0.963, 0.989, and 0.985 for the three separate datasets, indicating near-equitable treatment across languages. In contrast, Naive Bayes demonstrates greater disparities, evidenced by a demographic parity ratio of 0.813, 0.908, and 0.961. These findings reveal the importance of developing equitable multilingual NLP systems, particularly as we anticipate the inclusion of more datasets in various languages in the future.
Fleet of Agents: Coordinated Problem Solving with Large Language Models
Authors: Lars Klein, Nearchos Potamitis, Roland Aydin, Robert West, Caglar Gulcehre, Akhil Arora
Date: 2024-05-07 00:00:00
Tasks: Navigate

While numerous frameworks have been developed to enhance the reasoning abilities of large language models (LLMs), there is a scarcity of methods that effectively balance the trade-off between cost and quality. In this paper, we introduce Fleet of Agents (FoA), a novel and intuitive yet principled framework utilizing LLMs as agents to navigate through dynamic tree searches, employing a genetic-type particle filtering approach. FoA spawns a multitude of agents, each exploring the search space autonomously, followed by a selection phase where resampling based on a heuristic value function optimizes the balance between exploration and exploitation. This mechanism enables dynamic branching, adapting the exploration strategy based on discovered solutions. We conduct extensive experiments on three benchmark tasks, ``Game of 24'', ``Mini-Crosswords'', and ``WebShop'', utilizing four different LLMs, ``GPT-3.5'', ``GPT-4'', ``LLaMA3.2-11B'', and ``LLaMA3.2-90B''. On average across all tasks and LLMs, FoA obtains a quality improvement of ~5% while requiring only ~40% of the cost of previous SOTA methods. Notably, our analyses reveal that (1) FoA achieves the best cost-quality trade-off among all benchmarked methods and (2) FoA + LLaMA3.2-11B surpasses the Llama3.2-90B model. FoA is publicly available at https://github.com/au-clan/FoA.
DrugLLM: Open Large Language Model for Few-shot Molecule Generation
Authors: Xianggen Liu, Yan Guo, Haoran Li, Jin Liu, Shudong Huang, Bowen Ke, Jiancheng Lv
Date: 2024-05-07 00:00:00
Tasks: Drug Design, Drug Discovery, Few-Shot Learning, Language Modeling, Language Modelling, Large Language Model, molecular representation

Large Language Models (LLMs) have made great strides in areas such as language processing and computer vision. Despite the emergence of diverse techniques to improve few-shot learning capacity, current LLMs fall short in handling the languages in biology and chemistry. For example, they are struggling to capture the relationship between molecule structure and pharmacochemical properties. Consequently, the few-shot learning capacity of small-molecule drug modification remains impeded. In this work, we introduced DrugLLM, a LLM tailored for drug design. During the training process, we employed Group-based Molecular Representation (GMR) to represent molecules, arranging them in sequences that reflect modifications aimed at enhancing specific molecular properties. DrugLLM learns how to modify molecules in drug discovery by predicting the next molecule based on past modifications. Extensive computational experiments demonstrate that DrugLLM can generate new molecules with expected properties based on limited examples, presenting a powerful few-shot molecule generation capacity.