pwc-archive/papers-with-abstracts huggingface dataset:
Leveraging Multimodal CycleGAN for the Generation of Anatomically Accurate Synthetic CT Scans from MRIs
Authors: Leonardo Crespi, Samuele Camnasio, Damiano Dei, Nicola Lambri, Pietro Mancosu, Marta Scorsetti, Daniele Loiacono
Date: 2024-07-15 00:00:00
Tasks: Anatomy, Computed Tomography (CT)

In many clinical settings, the use of both Computed Tomography (CT) and Magnetic Resonance (MRI) is necessary to pursue a thorough understanding of the patient's anatomy and to plan a suitable therapeutical strategy; this is often the case in MRI-based radiotherapy, where CT is always necessary to prepare the dose delivery, as it provides the essential information about the radiation absorption properties of the tissues. Sometimes, MRI is preferred to contour the target volumes. However, this approach is often not the most efficient, as it is more expensive, time-consuming and, most importantly, stressful for the patients. To overcome this issue, in this work, we analyse the capabilities of different configurations of Deep Learning models to generate synthetic CT scans from MRI, leveraging the power of Generative Adversarial Networks (GANs) and, in particular, the CycleGAN architecture, capable of working in an unsupervised manner and without paired images, which were not available. Several CycleGAN models were trained unsupervised to generate CT scans from different MRI modalities with and without contrast agents. To overcome the problem of not having a ground truth, distribution-based metrics were used to assess the model's performance quantitatively, together with a qualitative evaluation where physicians were asked to differentiate between real and synthetic images to understand how realistic the generated images were. The results show how, depending on the input modalities, the models can have very different performances; however, models with the best quantitative results, according to the distribution-based metrics used, can generate very difficult images to distinguish from the real ones, even for physicians, demonstrating the approach's potential.
Hey, That's My Model! Introducing Chain & Hash, An LLM Fingerprinting Technique
Authors: Mark Russinovich, Ahmed Salem
Date: 2024-07-15 00:00:00

Growing concerns over the theft and misuse of Large Language Models (LLMs) have heightened the need for effective fingerprinting, which links a model to its original version to detect misuse. In this paper, we define five key properties for a successful fingerprint: Transparency, Efficiency, Persistence, Robustness, and Unforgeability. We introduce a novel fingerprinting framework that provides verifiable proof of ownership while maintaining fingerprint integrity. Our approach makes two main contributions. First, we propose a Chain and Hash technique that cryptographically binds fingerprint prompts with their responses, ensuring no adversary can generate colliding fingerprints and allowing model owners to irrefutably demonstrate their creation. Second, we address a realistic threat model in which instruction-tuned models' output distribution can be significantly altered through meta-prompts. By integrating random padding and varied meta-prompt configurations during training, our method preserves fingerprint robustness even when the model's output style is significantly modified. Experimental results demonstrate that our framework offers strong security for proving ownership and remains resilient against benign transformations like fine-tuning, as well as adversarial attempts to erase fingerprints. Finally, we also demonstrate its applicability to fingerprinting LoRA adapters.
Deep Causal Learning to Explain and Quantify The Geo-Tension's Impact on Natural Gas Market
Authors: Philipp Kai Peter, Yulin Li, Ziyue Li, Wolfgang Ketter
Date: 2024-07-15 00:00:00
Tasks: counterfactual

Natural gas demand is a crucial factor for predicting natural gas prices and thus has a direct influence on the power system. However, existing methods face challenges in assessing the impact of shocks, such as the outbreak of the Russian-Ukrainian war. In this context, we apply deep neural network-based Granger causality to identify important drivers of natural gas demand. Furthermore, the resulting dependencies are used to construct a counterfactual case without the outbreak of the war, providing a quantifiable estimate of the overall effect of the shock on various German energy sectors. The code and dataset are available at https://github.com/bonaldli/CausalEnergy.
Understanding the Importance of Evolutionary Search in Automated Heuristic Design with Large Language Models
Authors: Rui Zhang, Fei Liu, Xi Lin, Zhenkun Wang, Zhichao Lu, Qingfu Zhang
Date: 2024-07-15 00:00:00

Automated heuristic design (AHD) has gained considerable attention for its potential to automate the development of effective heuristics. The recent advent of large language models (LLMs) has paved a new avenue for AHD, with initial efforts focusing on framing AHD as an evolutionary program search (EPS) problem. However, inconsistent benchmark settings, inadequate baselines, and a lack of detailed component analysis have left the necessity of integrating LLMs with search strategies and the true progress achieved by existing LLM-based EPS methods to be inadequately justified. This work seeks to fulfill these research queries by conducting a large-scale benchmark comprising four LLM-based EPS methods and four AHD problems across nine LLMs and five independent runs. Our extensive experiments yield meaningful insights, providing empirical grounding for the importance of evolutionary search in LLM-based AHD approaches, while also contributing to the advancement of future EPS algorithmic development. To foster accessibility and reproducibility, we have fully open-sourced our benchmark and corresponding results.
GPT Sonograpy: Hand Gesture Decoding from Forearm Ultrasound Images via VLM
Authors: Keshav Bimbraw, Ye Wang, Jing Liu, Toshiaki Koike-Akino
Date: 2024-07-15 00:00:00
Tasks: In-Context Learning

Large vision-language models (LVLMs), such as the Generative Pre-trained Transformer 4-omni (GPT-4o), are emerging multi-modal foundation models which have great potential as powerful artificial-intelligence (AI) assistance tools for a myriad of applications, including healthcare, industrial, and academic sectors. Although such foundation models perform well in a wide range of general tasks, their capability without fine-tuning is often limited in specialized tasks. However, full fine-tuning of large foundation models is challenging due to enormous computation/memory/dataset requirements. We show that GPT-4o can decode hand gestures from forearm ultrasound data even with no fine-tuning, and improves with few-shot, in-context learning.
Weighted Grouped Query Attention in Transformers
Authors: Sai Sena Chinnakonduru, Astarag Mohapatra
Date: 2024-07-15 00:00:00
Tasks: Decoder

The attention mechanism forms the foundational blocks for transformer language models. Recent approaches show that scaling the model achieves human-level performance. However, with increasing demands for scaling and constraints on hardware memory, the inference costs of these models remain high. To reduce the inference time, Multi-Query Attention (MQA) and Grouped-Query Attention (GQA) were proposed in (Shazeer, 2019) and (Ainslieet al., 2023) respectively. In this paper, we propose a variation of Grouped-Query Attention, termed Weighted Grouped-Query Attention (WGQA). We introduced new learnable parameters for each key and value head in the T5 decoder attention blocks, enabling the model to take a weighted average during finetuning. Our model achieves an average of 0.53% improvement over GQA, and the performance converges to traditional Multi-head attention (MHA) with no additional overhead during inference. We evaluated the introduction of these parameters and subsequent finetuning informs the model about the grouping mechanism during training, thereby enhancing performance. Additionally, we demonstrate the scaling laws in our analysis by comparing the results between T5-small and T5-base architecture.
An Actionable Framework for Assessing Bias and Fairness in Large Language Model Use Cases
Authors: Dylan Bouchard
Date: 2024-07-15 00:00:00
Tasks: Attribute, counterfactual, Fairness, Language Modeling, Language Modelling, Large Language Model

Large language models (LLMs) can exhibit bias in a variety of ways. Such biases can create or exacerbate unfair outcomes for certain groups within a protected attribute, including, but not limited to sex, race, sexual orientation, or age. This paper aims to provide a technical guide for practitioners to assess bias and fairness risks in LLM use cases. The main contribution of this work is a decision framework that allows practitioners to determine which metrics to use for a specific LLM use case. To achieve this, this study categorizes LLM bias and fairness risks, maps those risks to a taxonomy of LLM use cases, and then formally defines various metrics to assess each type of risk. As part of this work, several new bias and fairness metrics are introduced, including innovative counterfactual metrics as well as metrics based on stereotype classifiers. Instead of focusing solely on the model itself, the sensitivity of both prompt-risk and model-risk are taken into account by defining evaluations at the level of an LLM use case, characterized by a model and a population of prompts. Furthermore, because all of the evaluation metrics are calculated solely using the LLM output, the proposed framework is highly practical and easily actionable for practitioners.
Offline Reinforcement Learning with Imputed Rewards
Authors: Carlo Romeo, Andrew D. Bagdanov
Date: 2024-07-15 00:00:00
Tasks: D4RL, reinforcement-learning, Reinforcement Learning

Offline Reinforcement Learning (ORL) offers a robust solution to training agents in applications where interactions with the environment must be strictly limited due to cost, safety, or lack of accurate simulation environments. Despite its potential to facilitate deployment of artificial agents in the real world, Offline Reinforcement Learning typically requires very many demonstrations annotated with ground-truth rewards. Consequently, state-of-the-art ORL algorithms can be difficult or impossible to apply in data-scarce scenarios. In this paper we propose a simple but effective Reward Model that can estimate the reward signal from a very limited sample of environment transitions annotated with rewards. Once the reward signal is modeled, we use the Reward Model to impute rewards for a large sample of reward-free transitions, thus enabling the application of ORL techniques. We demonstrate the potential of our approach on several D4RL continuous locomotion tasks. Our results show that, using only 1\% of reward-labeled transitions from the original datasets, our learned reward model is able to impute rewards for the remaining 99\% of the transitions, from which performant agents can be learned using Offline Reinforcement Learning.
MetaLLM: A High-performant and Cost-efficient Dynamic Framework for Wrapping LLMs
Authors: Quang H. Nguyen, Duy C. Hoang, Juliette Decugis, Saurav Manchanda, Nitesh V. Chawla, Khoa D. Doan
Date: 2024-07-15 00:00:00

The rapid progress in machine learning (ML) has brought forth many large language models (LLMs) that excel in various tasks and areas. These LLMs come with different abilities and costs in terms of computation or pricing. Since the demand for each query can vary, e.g., because of the queried domain or its complexity, defaulting to one LLM in an application is not usually the best choice, whether it is the biggest, priciest, or even the one with the best average test performance. Consequently, picking the right LLM that is both accurate and cost-effective for an application remains a challenge. In this paper, we introduce MetaLLM, a framework that dynamically and intelligently routes each query to the optimal LLM (among several available LLMs) for classification tasks, achieving significantly improved accuracy and cost-effectiveness. By framing the selection problem as a multi-armed bandit, MetaLLM balances prediction accuracy and cost efficiency under uncertainty. Our experiments, conducted on popular LLM platforms such as OpenAI's GPT models, Amazon's Titan, Anthropic's Claude, and Meta's LLaMa, showcase MetaLLM's efficacy in real-world scenarios, laying the groundwork for future extensions beyond classification tasks.
BiasScanner: Automatic Detection and Classification of News Bias to Strengthen Democracy
Authors: Tim Menzner, Jochen L. Leidner
Date: 2024-07-15 00:00:00
Tasks: Articles, Bias Detection, Language Modeling, Language Modelling, Large Language Model, Sentence

The increasing consumption of news online in the 21st century coincided with increased publication of disinformation, biased reporting, hate speech and other unwanted Web content. We describe BiasScanner, an application that aims to strengthen democracy by supporting news consumers with scrutinizing news articles they are reading online. BiasScanner contains a server-side pre-trained large language model to identify biased sentences of news articles and a front-end Web browser plug-in. At the time of writing, BiasScanner can identify and classify more than two dozen types of media bias at the sentence level, making it the most fine-grained model and only deployed application (automatic system in use) of its kind. It was implemented in a light-weight and privacy-respecting manner, and in addition to highlighting likely biased sentence it also provides explanations for each classification decision as well as a summary analysis for each news article. While prior research has addressed news bias detection, we are not aware of any work that resulted in a deployed browser plug-in (c.f. also biasscanner.org for a Web demo).
Towards Enhanced Classification of Abnormal Lung sound in Multi-breath: A Light Weight Multi-label and Multi-head Attention Classification Method
Authors: Yi-Wei Chua, Yun-Chien Cheng
Date: 2024-07-15 00:00:00
Tasks: Diagnostic, Diversity, Multi-Label Learning, Sound Classification

This study aims to develop an auxiliary diagnostic system for classifying abnormal lung respiratory sounds, enhancing the accuracy of automatic abnormal breath sound classification through an innovative multi-label learning approach and multi-head attention mechanism. Addressing the issue of class imbalance and lack of diversity in existing respiratory sound datasets, our study employs a lightweight and highly accurate model, using a two-dimensional label set to represent multiple respiratory sound characteristics. Our method achieved a 59.2% ICBHI score in the four-category task on the ICBHI2017 dataset, demonstrating its advantages in terms of lightweight and high accuracy. This study not only improves the accuracy of automatic diagnosis of lung respiratory sound abnormalities but also opens new possibilities for clinical applications.
Enabling MCTS Explainability for Sequential Planning Through Computation Tree Logic
Authors: Ziyan An, Hendrik Baier, Abhishek Dubey, Ayan Mukhopadhyay, Meiyi Ma
Date: 2024-07-15 00:00:00
Tasks: Descriptive

Monte Carlo tree search (MCTS) is one of the most capable online search algorithms for sequential planning tasks, with significant applications in areas such as resource allocation and transit planning. Despite its strong performance in real-world deployment, the inherent complexity of MCTS makes it challenging to understand for users without technical background. This paper considers the use of MCTS in transportation routing services, where the algorithm is integrated to develop optimized route plans. These plans are required to meet a range of constraints and requirements simultaneously, further complicating the task of explaining the algorithm's operation in real-world contexts. To address this critical research gap, we introduce a novel computation tree logic-based explainer for MCTS. Our framework begins by taking user-defined requirements and translating them into rigorous logic specifications through the use of language templates. Then, our explainer incorporates a logic verification and quantitative evaluation module that validates the states and actions traversed by the MCTS algorithm. The outcomes of this analysis are then rendered into human-readable descriptive text using a second set of language templates. The user satisfaction of our approach was assessed through a survey with 82 participants. The results indicated that our explanatory approach significantly outperforms other baselines in user preference.
Foundational Autoraters: Taming Large Language Models for Better Automatic Evaluation
Authors: Tu Vu, Kalpesh Krishna, Salaheddin Alzubi, Chris Tar, Manaal Faruqui, Yun-Hsuan Sung
Date: 2024-07-15 00:00:00
Tasks: Code Generation

As large language models (LLMs) advance, it becomes more challenging to reliably evaluate their output due to the high costs of human evaluation. To make progress towards better LLM autoraters, we introduce FLAMe, a family of Foundational Large Autorater Models. FLAMe is trained on our large and diverse collection of 100+ quality assessment tasks comprising 5M+ human judgments, curated and standardized using publicly released human evaluations from previous research. FLAMe significantly improves generalization to a wide variety of held-out tasks, outperforming LLMs trained on proprietary data like GPT-4 and Claude-3 on many tasks. We show that FLAMe can also serve as a powerful starting point for further downstream fine-tuning, using reward modeling evaluation as a case study (FLAMe-RM). Notably, on RewardBench, our FLAMe-RM-24B model (with an accuracy of 87.8%) is the top-performing generative model trained exclusively on permissively licensed data, outperforming both GPT-4-0125 (85.9%) and GPT-4o (84.7%). Additionally, we explore a more computationally efficient approach using a novel tail-patch fine-tuning strategy to optimize our FLAMe multitask mixture for reward modeling evaluation (FLAMe-Opt-RM), offering competitive RewardBench performance while requiring approximately 25x less training datapoints. Overall, our FLAMe variants outperform all popular proprietary LLM-as-a-Judge models we consider across 8 out of 12 autorater evaluation benchmarks, encompassing 53 quality assessment tasks, including RewardBench and LLM-AggreFact. Finally, our analysis reveals that FLAMe is significantly less biased than these LLM-as-a-Judge models on the CoBBLEr autorater bias benchmark, while effectively identifying high-quality responses for code generation.
GuideLight: "Industrial Solution" Guidance for More Practical Traffic Signal Control Agents
Authors: Haoyuan Jiang, Xuantang Xiong, Ziyue Li, Hangyu Mao, Guanghu Sui, Jingqing Ruan, Yuheng Cheng, Hua Wei, Wolfgang Ketter, Rui Zhao
Date: 2024-07-15 00:00:00
Tasks: Reinforcement Learning (RL), Traffic Signal Control

Currently, traffic signal control (TSC) methods based on reinforcement learning (RL) have proven superior to traditional methods. However, most RL methods face difficulties when applied in the real world due to three factors: input, output, and the cycle-flow relation. The industry's observable input is much more limited than simulation-based RL methods. For real-world solutions, only flow can be reliably collected, whereas common RL methods need more. For the output action, most RL methods focus on acyclic control, which real-world signal controllers do not support. Most importantly, industry standards require a consistent cycle-flow relationship: non-decreasing and different response strategies for low, medium, and high-level flows, which is ignored by the RL methods. To narrow the gap between RL methods and industry standards, we innovatively propose to use industry solutions to guide the RL agent. Specifically, we design behavior cloning and curriculum learning to guide the agent to mimic and meet industry requirements and, at the same time, leverage the power of exploration and exploitation in RL for better performance. We theoretically prove that such guidance can largely decrease the sample complexity to polynomials in the horizon when searching for an optimal policy. Our rigid experiments show that our method has good cycle-flow relation and superior performance.
FabGPT: An Efficient Large Multimodal Model for Complex Wafer Defect Knowledge Queries
Authors: Yuqi Jiang, Xudong Lu, Qian Jin, Qi Sun, Hanming Wu, Cheng Zhuo
Date: 2024-07-15 00:00:00
Tasks: Defect Detection, Question Answering

Intelligence is key to advancing integrated circuit (IC) fabrication. Recent breakthroughs in Large Multimodal Models (LMMs) have unlocked extraditionary abilities in understanding images and text, fostering intelligent fabrication. Leveraging the power of LMMs, we introduce FabGPT, a customized IC fabrication large multimodal model for wafer defect knowledge query. FabGPT manifests expertise in conducting defect detection in Scanning Electron Microscope (SEM) images, performing root cause analysis, and providing expert Q&A on fabrication processes. FabGPT matches enhanced multimodal features to automatically detect minute defects under complex wafer backgrounds and reduce the subjectivity of manual threshold settings. Besides, the proposed modulation module and interactive corpus training strategy embed wafer defect knowledge into the pre-trained model, effectively balancing Q&A queries related to defect knowledge and original knowledge and mitigating the modality bias issues. Experiments on in-house fab data show that FabGPT achieves significant performance improvement in wafer defect detection and knowledge querying.
Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation
Authors: Shengjie Ma, Chengjin Xu, Xuhui Jiang, Muzhi Li, Huaren Qu, Cehao Yang, Jiaxin Mao, Jian Guo
Date: 2024-07-15 00:00:00
Tasks: Information Retrieval, Knowledge Graphs, Language Modeling, Language Modelling, Large Language Model, RAG, Retrieval, Retrieval-augmented Generation, Semantic Similarity, Semantic Textual Similarity

Retrieval-augmented generation (RAG) has improved large language models (LLMs) by using knowledge retrieval to overcome knowledge deficiencies. However, current RAG methods often fall short of ensuring the depth and completeness of retrieved information, which is necessary for complex reasoning tasks. In this work, we introduce Think-on-Graph 2.0 (ToG-2), a hybrid RAG framework that iteratively retrieves information from both unstructured and structured knowledge sources in a tight-coupling manner. Specifically, ToG-2 leverages knowledge graphs (KGs) to link documents via entities, facilitating deep and knowledge-guided context retrieval. Simultaneously, it utilizes documents as entity contexts to achieve precise and efficient graph retrieval. ToG-2 alternates between graph retrieval and context retrieval to search for in-depth clues relevant to the question, enabling LLMs to generate answers. We conduct a series of well-designed experiments to highlight the following advantages of ToG-2: 1) ToG-2 tightly couples the processes of context retrieval and graph retrieval, deepening context retrieval via the KG while enabling reliable graph retrieval based on contexts; 2) it achieves deep and faithful reasoning in LLMs through an iterative knowledge retrieval process of collaboration between contexts and the KG; and 3) ToG-2 is training-free and plug-and-play compatible with various LLMs. Extensive experiments demonstrate that ToG-2 achieves overall state-of-the-art (SOTA) performance on 6 out of 7 knowledge-intensive datasets with GPT-3.5, and can elevate the performance of smaller models (e.g., LLAMA-2-13B) to the level of GPT-3.5's direct reasoning. The source code is available on https://github.com/IDEA-FinAI/ToG-2.