pwc-archive/papers-with-abstracts huggingface dataset:
Fine-tuning Smaller Language Models for Question Answering over Financial Documents
Authors: Karmvir Singh Phogat, Sai Akhil Puranam, Sridhar Dasaratha, Chetan Harsha, Shashishekar Ramakrishna
Date: 2024-08-22 00:00:00
Tasks: Question Answering

Recent research has shown that smaller language models can acquire substantial reasoning abilities when fine-tuned with reasoning exemplars crafted by a significantly larger teacher model. We explore this paradigm for the financial domain, focusing on the challenge of answering questions that require multi-hop numerical reasoning over financial texts. We assess the performance of several smaller models that have been fine-tuned to generate programs that encode the required financial reasoning and calculations. Our findings demonstrate that these fine-tuned smaller models approach the performance of the teacher model. To provide a granular analysis of model performance, we propose an approach to investigate the specific student model capabilities that are enhanced by fine-tuning. Our empirical analysis indicates that fine-tuning refines the student models ability to express and apply the required financial concepts along with adapting the entity extraction for the specific data format. In addition, we hypothesize and demonstrate that comparable financial reasoning capability can be induced using relatively smaller datasets.
Enhanced Expressivity in Graph Neural Networks with Lanczos-Based Linear Constraints
Authors: Niloofar Azizi, Nils Kriege, Horst Bischof
Date: 2024-08-22 00:00:00
Tasks: Link Prediction

Graph Neural Networks (GNNs) excel in handling graph-structured data but often underperform in link prediction tasks compared to classical methods, mainly due to the limitations of the commonly used Message Passing GNNs (MPNNs). Notably, their ability to distinguish non-isomorphic graphs is limited by the 1-dimensional Weisfeiler-Lehman test. Our study presents a novel method to enhance the expressivity of GNNs by embedding induced subgraphs into the graph Laplacian matrix's eigenbasis. We introduce a Learnable Lanczos algorithm with Linear Constraints (LLwLC), proposing two novel subgraph extraction strategies: encoding vertex-deleted subgraphs and applying Neumann eigenvalue constraints. For the former, we conjecture that LLwLC establishes a universal approximator, offering efficient time complexity. The latter focuses on link representations enabling differentiation between $k$-regular graphs and node automorphism, a vital aspect for link prediction tasks. Our approach results in an extremely lightweight architecture, reducing the need for extensive training datasets. Empirically, our method improves performance in challenging link prediction tasks across benchmark datasets, establishing its practical utility and supporting our theoretical findings. Notably, LLwLC achieves 20x and 10x speedup by only requiring 5% and 10% data from the PubMed and OGBL-Vessel datasets while comparing to the state-of-the-art.
GRATR: Zero-Shot Evidence Graph Retrieval-Augmented Trustworthiness Reasoning
Authors: Ying Zhu, Shengchang Li, Ziqian Kong, Qiang Yang, Peilan Xu
Date: 2024-08-22 00:00:00
Tasks: Decision Making, Hallucination, RAG, Retrieval

Trustworthiness reasoning aims to enable agents in multiplayer games with incomplete information to identify potential allies and adversaries, thereby enhancing decision-making. In this paper, we introduce the graph retrieval-augmented trustworthiness reasoning (GRATR) framework, which retrieves observable evidence from the game environment to inform decision-making by large language models (LLMs) without requiring additional training, making it a zero-shot approach. Within the GRATR framework, agents first observe the actions of other players and evaluate the resulting shifts in inter-player trust, constructing a corresponding trustworthiness graph. During decision-making, the agent performs multi-hop retrieval to evaluate trustworthiness toward a specific target, where evidence chains are retrieved from multiple trusted sources to form a comprehensive assessment. Experiments in the multiplayer game \emph{Werewolf} demonstrate that GRATR outperforms the alternatives, improving reasoning accuracy by 50.5\% and reducing hallucination by 30.6\% compared to the baseline method. Additionally, when tested on a dataset of Twitter tweets during the U.S. election period, GRATR surpasses the baseline method by 10.4\% in accuracy, highlighting its potential in real-world applications such as intent analysis.
Interactive DualChecker for Mitigating Hallucinations in Distilling Large Language Models
Authors: Meiyun Wang, Masahiro Suzuki, Hiroki Sakaji, Kiyoshi Izumi
Date: 2024-08-22 00:00:00
Tasks: In-Context Learning, Knowledge Distillation, token-classification, Token Classification, Transfer Learning

Large Language Models (LLMs) have demonstrated exceptional capabilities across various machine learning (ML) tasks. Given the high costs of creating annotated datasets for supervised learning, LLMs offer a valuable alternative by enabling effective few-shot in-context learning. However, these models can produce hallucinations, particularly in domains with incomplete knowledge. Additionally, current methods for knowledge distillation using LLMs often struggle to enhance the effectiveness of both teacher and student models. To address these challenges, we introduce DualChecker, an innovative framework designed to mitigate hallucinations and improve the performance of both teacher and student models during knowledge distillation. DualChecker employs ContextAligner to ensure that the context provided by teacher models aligns with human labeling standards. It also features a dynamic checker system that enhances model interaction: one component re-prompts teacher models with more detailed content when they show low confidence, and another identifies borderline cases from student models to refine the teaching templates. This interactive process promotes continuous improvement and effective knowledge transfer between the models. We evaluate DualChecker using a green innovation textual dataset that includes binary, multiclass, and token classification tasks. The experimental results show that DualChecker significantly outperforms existing state-of-the-art methods, achieving up to a 17% improvement in F1 score for teacher models and 10% for student models. Notably, student models fine-tuned with LLM predictions perform comparably to those fine-tuned with actual data, even in a challenging domain. We make all datasets, models, and code from this research publicly available.
TensorOpera Router: A Multi-Model Router for Efficient LLM Inference
Authors: Dimitris Stripelis, Zijian Hu, Jipeng Zhang, Zhaozhuo Xu, Alay Dilipbhai Shah, Han Jin, Yuhang Yao, Salman Avestimehr, Chaoyang He
Date: 2024-08-22 00:00:00

With the rapid growth of Large Language Models (LLMs) across various domains, numerous new LLMs have emerged, each possessing domain-specific expertise. This proliferation has highlighted the need for quick, high-quality, and cost-effective LLM query response methods. Yet, no single LLM exists to efficiently balance this trilemma. Some models are powerful but extremely costly, while others are fast and inexpensive but qualitatively inferior. To address this challenge, we present TO-Router, a non-monolithic LLM querying system that seamlessly integrates various LLM experts into a single query interface and dynamically routes incoming queries to the most high-performant expert based on query's requirements. Through extensive experiments, we demonstrate that when compared to standalone expert models, TO-Router improves query efficiency by up to 40\%, and leads to significant cost reductions of up to 30%, while maintaining or enhancing model performance by up to 10%.
Large Language Models Are Self-Taught Reasoners: Enhancing LLM Applications via Tailored Problem-Solving Demonstrations
Authors: Kai Tzu-iunn Ong, Taeyoon Kwon, Jinyoung Yeo
Date: 2024-08-22 00:00:00
Tasks: Multiple-choice

Guiding large language models with a selected set of human-authored demonstrations is a common practice for improving LLM applications. However, human effort can be costly, especially in specialized domains (e.g., clinical diagnosis), and does not guarantee optimal performance due to the potential discrepancy of target skills between selected demonstrations and real test instances. Motivated by these, this paper explores the automatic creation of customized demonstrations, whose target skills align with the given target instance. We present SELF-TAUGHT, a problem-solving framework, which facilitates demonstrations that are "tailored" to the target problem and "filtered" for better quality (i.e., correctness) in a zero-shot manner. In 15 tasks of multiple-choice questions of diverse domains and the diagnosis of Alzheimer's disease (AD) with real-world patients, SELF-TAUGHT achieves superior performance to strong baselines (e.g., Few-shot CoT, Plan-and-Solve, Auto-CoT). We conduct comprehensive analyses on SELF-TAUGHT, including its generalizability to existing prompting methods and different LLMs, the quality of its intermediate generation, and more.
Deep Learning with CNNs: A Compact Holistic Tutorial with Focus on Supervised Regression (Preprint)
Authors: Yansel Gonzalez Tejeda, Helmut A. Mayer
Date: 2024-08-22 00:00:00
Tasks: Articles, Deep Learning, Learning Theory

In this tutorial, we present a compact and holistic discussion of Deep Learning with a focus on Convolutional Neural Networks (CNNs) and supervised regression. While there are numerous books and articles on the individual topics we cover, comprehensive and detailed tutorials that address Deep Learning from a foundational yet rigorous and accessible perspective are rare. Most resources on CNNs are either too advanced, focusing on cutting-edge architectures, or too narrow, addressing only specific applications like image classification.This tutorial not only summarizes the most relevant concepts but also provides an in-depth exploration of each, offering a complete yet agile set of ideas. Moreover, we highlight the powerful synergy between learning theory, statistic, and machine learning, which together underpin the Deep Learning and CNN frameworks. We aim for this tutorial to serve as an optimal resource for students, professors, and anyone interested in understanding the foundations of Deep Learning. Upon acceptance we will provide an accompanying repository under \href{https://github.com/neoglez/deep-learning-tutorial}{https://github.com/neoglez/deep-learning-tutorial} Keywords: Tutorial, Deep Learning, Convolutional Neural Networks, Machine Learning.
Tipta uzmanlik sinavinda (tus) buyuk dil modelleri insanlardan daha mi basarili?
Authors: Yesim Aygul, Muge Olucoglu, Adil Alpkocak
Date: 2024-08-22 00:00:00

The potential of artificial intelligence in medical education and assessment has been made evident by recent developments in natural language processing and artificial intelligence. Medical questions can now be successfully answered by artificial intelligence algorithms. It can help medical practitioners. This study evaluates the performance of three different artificial intelligence models in answering Turkish medical questions in the 2021 1st Term Medical Specialization Examination (MSE). MSE consists of a total of 240 questions across clinical (CMST) and basic (BMST) medical sciences. According to the results in CMST, it was concluded that Gemini correctly answered 82 questions, ChatGPT-4 answered 105 questions and ChatGPT-4o answered 117 questions. In BMST, Gemini and ChatGPT-4 answered 93 questions and ChatGPT-4o answered 107 questions correctly according to the answer key. ChatGPT-4o outperformed the candidate with the highest scores of 113 and 106 according to CMST and BMST respectively. This study highlights the importance of the potential of artificial intelligence in medical education and assessment. It demonstrates that advanced models can achieve high accuracy and contextual understanding, demonstrating their potential role in medical education and evaluation.
OPTDTALS: Approximate Logic Synthesis via Optimal Decision Trees Approach
Authors: Hao Hu, Shaowei Cai
Date: 2024-08-22 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Interpretable Machine Learning

The growing interest in Explainable Artificial Intelligence (XAI) motivates promising studies of computing optimal Interpretable Machine Learning models, especially decision trees. Such models generally provide optimality in compact size or empirical accuracy. Recent works focus on improving efficiency due to the natural scalability issue. The application of such models to practical problems is quite limited. As an emerging problem in circuit design, Approximate Logic Synthesis (ALS) aims to reduce circuit complexity by sacrificing correctness. Recently, multiple heuristic machine learning methods have been applied in ALS, which learns approximated circuits from samples of input-output pairs. In this paper, we propose a new ALS methodology realizing the approximation via learning optimal decision trees in empirical accuracy. Compared to previous heuristic ALS methods, the guarantee of optimality achieves a more controllable trade-off between circuit complexity and accuracy. Experimental results show clear improvements in our methodology in the quality of approximated designs (circuit complexity and accuracy) compared to the state-of-the-art approaches.
AT-SNN: Adaptive Tokens for Vision Transformer on Spiking Neural Network
Authors: Donghwa Kang, Youngmoon Lee, Eun-Kyu Lee, Brent Kang, Jinkyu Lee, Hyeongboo Baek
Date: 2024-08-22 00:00:00
Tasks: image-classification, Image Classification

In the training and inference of spiking neural networks (SNNs), direct training and lightweight computation methods have been orthogonally developed, aimed at reducing power consumption. However, only a limited number of approaches have applied these two mechanisms simultaneously and failed to fully leverage the advantages of SNN-based vision transformers (ViTs) since they were originally designed for convolutional neural networks (CNNs). In this paper, we propose AT-SNN designed to dynamically adjust the number of tokens processed during inference in SNN-based ViTs with direct training, wherein power consumption is proportional to the number of tokens. We first demonstrate the applicability of adaptive computation time (ACT), previously limited to RNNs and ViTs, to SNN-based ViTs, enhancing it to discard less informative spatial tokens selectively. Also, we propose a new token-merge mechanism that relies on the similarity of tokens, which further reduces the number of tokens while enhancing accuracy. We implement AT-SNN to Spikformer and show the effectiveness of AT-SNN in achieving high energy efficiency and accuracy compared to state-of-the-art approaches on the image classification tasks, CIFAR10, CIFAR-100, and TinyImageNet. For example, our approach uses up to 42.4% fewer tokens than the existing best-performing method on CIFAR-100, while conserving higher accuracy.
Towards Deconfounded Image-Text Matching with Causal Inference
Authors: Wenhui Li, Xinqi Su, Dan Song, Lanjun Wang, Kun Zhang, An-An Liu
Date: 2024-08-22 00:00:00
Tasks: Causal Inference, Image-text matching, Text Matching

Prior image-text matching methods have shown remarkable performance on many benchmark datasets, but most of them overlook the bias in the dataset, which exists in intra-modal and inter-modal, and tend to learn the spurious correlations that extremely degrade the generalization ability of the model. Furthermore, these methods often incorporate biased external knowledge from large-scale datasets as prior knowledge into image-text matching model, which is inevitable to force model further learn biased associations. To address above limitations, this paper firstly utilizes Structural Causal Models (SCMs) to illustrate how intra- and inter-modal confounders damage the image-text matching. Then, we employ backdoor adjustment to propose an innovative Deconfounded Causal Inference Network (DCIN) for image-text matching task. DCIN (1) decomposes the intra- and inter-modal confounders and incorporates them into the encoding stage of visual and textual features, effectively eliminating the spurious correlations during image-text matching, and (2) uses causal inference to mitigate biases of external knowledge. Consequently, the model can learn causality instead of spurious correlations caused by dataset bias. Extensive experiments on two well-known benchmark datasets, i.e., Flickr30K and MSCOCO, demonstrate the superiority of our proposed method.
Developing vocal system impaired patient-aimed voice quality assessment approach using ASR representation-included multiple features
Authors: Shaoxiang Dang, Tetsuya Matsumoto, Yoshinori Takeuchi, Takashi Tsuboi, Yasuhiro Tanaka, Daisuke Nakatsubo, Satoshi Maesawa, Ryuta Saito, Masahisa Katsuno, Hiroaki Kudo
Date: 2024-08-22 00:00:00
Tasks: Automatic Speech Recognition, Self-Supervised Learning, speech-recognition, Speech Recognition

The potential of deep learning in clinical speech processing is immense, yet the hurdles of limited and imbalanced clinical data samples loom large. This article addresses these challenges by showcasing the utilization of automatic speech recognition and self-supervised learning representations, pre-trained on extensive datasets of normal speech. This innovative approach aims to estimate voice quality of patients with impaired vocal systems. Experiments involve checks on PVQD dataset, covering various causes of vocal system damage in English, and a Japanese dataset focusing on patients with Parkinson's disease before and after undergoing subthalamic nucleus deep brain stimulation (STN-DBS) surgery. The results on PVQD reveal a notable correlation (>0.8 on PCC) and an extraordinary accuracy (<0.5 on MSE) in predicting Grade, Breathy, and Asthenic indicators. Meanwhile, progress has been achieved in predicting the voice quality of patients in the context of STN-DBS.
Variance reduction of diffusion model's gradients with Taylor approximation-based control variate
Authors: Paul Jeha, Will Grathwohl, Michael Riis Andersen, Carl Henrik Ek, Jes Frellsen
Date: 2024-08-22 00:00:00
Tasks: Denoising

Score-based models, trained with denoising score matching, are remarkably effective in generating high dimensional data. However, the high variance of their training objective hinders optimisation. We attempt to reduce it with a control variate, derived via a $k$-th order Taylor expansion on the training objective and its gradient. We prove an equivalence between the two and demonstrate empirically the effectiveness of our approach on a low dimensional problem setting; and study its effect on larger problems.
Toward the Evaluation of Large Language Models Considering Score Variance across Instruction Templates
Authors: Yusuke Sakai, Adam Nohejl, Jiangnan Hang, Hidetaka Kamigaito, Taro Watanabe
Date: 2024-08-22 00:00:00
Tasks: Natural Language Understanding

The natural language understanding (NLU) performance of large language models (LLMs) has been evaluated across various tasks and datasets. The existing evaluation methods, however, do not take into account the variance in scores due to differences in prompts, which leads to unfair evaluation and comparison of NLU performance. Moreover, evaluation designed for specific prompts is inappropriate for instruction tuning, which aims to perform well with any prompt. It is therefore necessary to find a way to measure NLU performance in a fair manner, considering score variance between different instruction templates. In this study, we provide English and Japanese cross-lingual datasets for evaluating the NLU performance of LLMs, which include multiple instruction templates for fair evaluation of each task, along with regular expressions to constrain the output format. Furthermore, we propose the Sharpe score as an evaluation metric that takes into account the variance in scores between templates. Comprehensive analysis of English and Japanese LLMs reveals that the high variance among templates has a significant impact on the fair evaluation of LLMs.
Can You Trust Your Metric? Automatic Concatenation-Based Tests for Metric Validity
Authors: Ora Nova Fandina, Leshem Choshen, Eitan Farchi, George Kour, Yotam Perlitz, Orna Raz
Date: 2024-08-22 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, valid

Consider a scenario where a harmfulness detection metric is employed by a system to filter unsafe responses generated by a Large Language Model. When analyzing individual harmful and unethical prompt-response pairs, the metric correctly classifies each pair as highly unsafe, assigning the highest score. However, when these same prompts and responses are concatenated, the metric's decision flips, assigning the lowest possible score, thereby misclassifying the content as safe and allowing it to bypass the filter. In this study, we discovered that several harmfulness LLM-based metrics, including GPT-based, exhibit this decision-flipping phenomenon. Additionally, we found that even an advanced metric like GPT-4o is highly sensitive to input order. Specifically, it tends to classify responses as safe if the safe content appears first, regardless of any harmful content that follows, and vice versa. This work introduces automatic concatenation-based tests to assess the fundamental properties a valid metric should satisfy. We applied these tests in a model safety scenario to assess the reliability of harmfulness detection metrics, uncovering a number of inconsistencies.
A Language-agnostic Model of Child Language Acquisition
Authors: Louis Mahon, Omri Abend, Uri Berger, Katherine Demuth, Mark Johnson, Mark Steedman
Date: 2024-08-22 00:00:00
Tasks: Language Acquisition, model

This work reimplements a recent semantic bootstrapping child-language acquisition model, which was originally designed for English, and trains it to learn a new language: Hebrew. The model learns from pairs of utterances and logical forms as meaning representations, and acquires both syntax and word meanings simultaneously. The results show that the model mostly transfers to Hebrew, but that a number of factors, including the richer morphology in Hebrew, makes the learning slower and less robust. This suggests that a clear direction for future work is to enable the model to leverage the similarities between different word forms.
Can Artificial Intelligence Embody Moral Values?
Authors: Torben Swoboda, Lode Lauwaert
Date: 2024-08-22 00:00:00
Tasks: Fairness

The neutrality thesis holds that technology cannot be laden with values. This long-standing view has faced critiques, but much of the argumentation against neutrality has focused on traditional, non-smart technologies like bridges and razors. In contrast, AI is a smart technology increasingly used in high-stakes domains like healthcare, finance, and policing, where its decisions can cause moral harm. In this paper, we argue that artificial intelligence, particularly artificial agents that autonomously make decisions to pursue their goals, challenge the neutrality thesis. Our central claim is that the computational models underlying artificial agents can integrate representations of moral values such as fairness, honesty and avoiding harm. We provide a conceptual framework discussing the neutrality thesis, values, and AI. Moreover, we examine two approaches to designing computational models of morality, artificial conscience and ethical prompting, and present empirical evidence from text-based game environments that artificial agents with such models exhibit more ethical behavior compared to agents without these models. The findings support that AI can embody moral values, which contradicts the claim that all technologies are necessarily value-neutral.