Reviewerly/exHarmony huggingface dataset:
Stealthy Backdoor Attacks On Deep Point Cloud Recognization Networks

Abstract Deep neural networks are vulnerable to backdoor attacks. Previous backdoor attacks have mainly focused on images. Unlike images composed of regular pixels, 3D point clouds are composed of irregular three-dimensional XYZ coordinates, which are widely used in areas such as autonomous driving and 3D measurement. As many deep neural networks have been developed for processing 3D point clouds, these networks also face the risk of backdoor attacks. Nevertheless, backdoor attacks on 3D point clouds have rarely been investigated. This paper proposes a stealthy backdoor attack on point clouds in the physical world, aiming to generate trainable non-rigid deformations as backdoor patterns. Instead of directly adding backdoor patterns onto the point clouds, we deform the 3D space of the point clouds to a new space, ensuring that all point clouds have the same backdoor deformation. We use point cloud alignment to overcome the inconsistency of backdoor deformation caused by shifting and scaling in the physical world. We also propose a physical transformation layer to combat the physical transformations. Additionally, we propose mask contrast learning to eliminate pseudo backdoor patterns to make the network’s backdoor property stealthier. Extensive experiments indicate that the proposed method can achieve better attack success rates and stealthiness.
Privacy-Preserving Image Retrieval with Multi-Modal Query

Abstract The ever-growing multi-modal images pose great challenges to local image storage and retrieval systems. Cloud computing provides a solution to large-scale image data storage but suffers from privacy issues and lacks the support for multi-modal image retrieval. To address these, a searchable encryption-empowered privacy-preserving multi-modal image retrieval method is proposed. First, we design a hybrid image retrieval framework that fuses visual features and textual features at a decision level and further supports similar image retrieval and multi-keyword image retrieval. Second, we construct a new hybrid inverted index structure to distinguish high-frequency terms from low-frequency terms and index them through hierarchical index trees and data blocks, respectively, which greatly improves query efficiency. Third, we design a prime encoding-based multi-keyword query method that converts mapping operations in bloom filters into inner product calculations, and further implements secure multi-keyword image query. Experiments against the Baseline schemes are conducted to verify the performance of the scheme in terms of high efficiency.
Non-Interactive DSSE for Medical Data Sharing With Forward and Backward Privacy

In medical cloud computing, more medical data owners are preferred to outsource their sensitive data to the cloud after encryption. Meanwhile, dynamic searchable symmetric encryption (DSSE) provides the capability for data users to query over the dynamically-updated encrypted database. To reduce update leakage, a secure DSSE scheme usually requires forward and backward privacy. However, existing multi-client DSSE schemes with forward and backward privacy require the data owner to keep online to respond to per-query interaction from data users. To address this issue, we propose a multi-client non-interactive DSSE scheme with forward and backward privacy, namely MCNI. The core design of MCNI is leveraging time range queries to achieve non-interactive forward privacy since the past queries cannot be used to search the newly-added timestamps. To enable efficient time range queries, we convert the timestamp and time range into the boolean wildcard form and develop Boolean Wildcard Matching (BWM) algorithm that formulates the match as a dot product calculation problem. Finally, we combine the polynomial fitting technique, time range query, and random matrix multiplication technique to achieve efficient keyword searches without revealing sensitive information. Theoretical analysis and extensive experiments demonstrate the security and effectiveness of our proposed scheme, respectively.
Decision support for detecting sensitive text in government records

Abstract Freedom of information laws promote transparency by permitting individuals and organizations to obtain government documents. However, exemptions from disclosure are necessary to protect privacy and to permit government officials to deliberate freely. Deliberative language is often the most challenging and burdensome exemption to detect, leading to high processing costs and delays in responding to open-records requests. This paper describes a novel deliberative-language detection model trained on a new annotated training set. The deliberative-language detection model is a component of a decision-support system for open-records requests under the US Freedom of Information Act, the FOIA Assistant , that ingests documents responsive to an open-records requests, suggests passages likely to be subject to deliberative language, privacy, or other exemptions, and assists analysts in rapidly redacting suggested passages. The tool’s interface is based on extensive human-factors and usability studies with analysts and is currently in operational testing by multiple US federal agencies.
BanglaCHQ-Summ: An Abstractive Summarization Dataset for Medical Queries in Bangla Conversational Speech

Online health consultation is steadily gaining popularity as a platform for patients to discuss their medical health inquiries, known as Consumer Health Questions (CHQs). The emergence of the COVID-19 pandemic has also led to a surge in the use of such platforms, creating a significant burden for the limited number of healthcare professionals attempting to respond to the influx of questions. Abstractive text summarization is a promising solution to this challenge, since shortening CHQs to only the information essential to answering them reduces the amount of time spent parsing unnecessary information. The summarization process can also serve as an intermediate step towards the eventual development of an automated medical question-answering system. This paper presents ‘BanglaCHQ-Summ’, the first CHQ summarization dataset for the Bangla language, consisting of 2,350 question-summary pairs. It is benchmarked on state-of-the-art Bangla and multilingual text generation models, with the best-performing model, BanglaT5, achieving a ROUGE-L score of 48.35%. In addition, we address the limitations of existing automatic metrics for summarization by conducting a human evaluation. The dataset and all relevant code used in this work have been made publicly available.
Composition of Various Percentages of Terracotta Clay as Colourant Glaze

Terracotta is valuable for its mineral content, which produces pigments for coloring ceramic bodies and glazes. Unlike commercial colors, terracotta's natural minerals make it unique. This study explores the potential use of local terracotta clay in transparent glaze formulations. Methods like clay powder percentages, X-ray diffraction, X-ray fluorescence tests, electron scanning, and microscope analysis (SEM) are used to examine the clay elements' morphology and structure. The addition of terracotta clay powder in proportions (20-50%) rendered the glaze translucent. Samples were applied on porcelain and fired at gloss temperatures (1200°C). The result colors were glossy, smooth, and creamy, with no defects. Keywords: Terracotta, colorant, glaze, percentage
EmptyMind at BLP-2023 Task 2: Sentiment Analysis of Bangla Social Media Posts using Transformer-Based Models

With the popularity of social media platforms, people are sharing their individual thoughts by posting, commenting, and messaging with their friends, which generates a significant amount of digital text data every day. Conducting sentiment analysis of social media content is a vibrant research domain within the realm of Natural Language Processing (NLP), and it has practical, real-world uses. Numerous prior studies have focused on sentiment analysis for languages that have abundant linguistic resources, such as English. However, limited prior research works have been done for automatic sentiment analysis in low-resource languages like Bangla. In this research work, we are going to finetune different transformer-based models for Bangla sentiment analysis. To train and evaluate the model, we have utilized a dataset provided in a shared task organized by the BLP Workshop co-located with EMNLP-2023. Moreover, we have conducted a comparative study among different machine learning models, deep learning models, and transformer-based models for Bangla sentiment analysis. Our findings show that the BanglaBERT (Large) model has achieved the best result with a micro F1-Score of 0.7109 and secured 7th position in the shared task 2 leaderboard of the BLP Workshop in EMNLP 2023.
Tunable Soft Prompts are Messengers in Federated Learning

Federated learning (FL) enables multiple participants to collaboratively train machine learning models using decentralized data sources, alleviating privacy concerns that arise from directly sharing local data. However, the lack of model privacy protection in FL becomes an unneglectable challenge, especially when people want to federally finetune models based on a proprietary large language model. In this study, we propose a novel FL training approach that accomplishes information exchange among participants via tunable soft prompts. These soft prompts, updated and transmitted between the server and clients, assume the role of the global model parameters and serve as messengers to deliver useful knowledge from the local data and global model. As the global model itself is not required to be shared and the local training is conducted based on an auxiliary model with fewer parameters than the global model, the proposed approach provides protection for the global model while reducing communication and computation costs in FL. Extensive experiments show the effectiveness of the proposed approach compared to several baselines. We have released the source code at https://github.com/alibaba/FederatedScope/tree/fedsp/federatedscope/nlp/fedsp.
Event Causality Extraction via Implicit Cause-Effect Interactions

Event Causality Extraction (ECE) aims to extract the cause-effect event pairs from the given text, which requires the model to possess a strong reasoning ability to capture event causalities. However, existing works have not adequately exploited the interactions between the cause and effect event that could provide crucial clues for causality reasoning. To this end, we propose an Implicit Cause-Effect interaction (ICE) framework, which formulates ECE as a template-based conditional generation problem. The proposed method captures the implicit intra- and inter-event interactions by incorporating the privileged information (ground truth event types and arguments) for reasoning, and a knowledge distillation mechanism is introduced to alleviate the unavailability of privileged information in the test stage. Furthermore, to facilitate knowledge transfer from teacher to student, we design an event-level alignment strategy named Cause-Effect Optimal Transport (CEOT) to strengthen the semantic interactions of cause-effect event types and arguments. Experimental results indicate that ICE achieves state-of-the-art performance on the ECE-CCKS dataset.
PaRaDe: Passage Ranking using Demonstrations with LLMs

Andrew Drozdov, Honglei Zhuang, Zhuyun Dai, Zhen Qin, Razieh Rahimi, Xuanhui Wang, Dana Alon, Mohit Iyyer, Andrew McCallum, Donald Metzler, Kai Hui. Findings of the Association for Computational Linguistics: EMNLP 2023. 2023.
The Art of SOCRATIC QUESTIONING: Recursive Thinking with Large Language Models

Chain-of-Thought (CoT) prompting enables large language models to solve complex reasoning problems by generating intermediate steps. However, confined by its inherent single-pass and sequential generation process, CoT heavily relies on the initial decisions, causing errors in early steps to accumulate and impact the final answers. In contrast, humans adopt recursive thinking when tackling complex reasoning problems, i.e. iteratively breaking the original problem into approachable sub-problems and aggregating their answers to resolve the original one. Inspired by the human cognitive process, we propose SOCRATIC QUESTIONING, a divide-and-conquer style algorithm that mimics the recursive thinking process. Specifically, SOCRATIC QUESTIONING leverages large language models to raise and answer sub-questions until collecting enough information to tackle the original question. Unlike CoT, SOCRATIC QUESTIONING explicitly navigates the thinking space, stimulates effective recursive thinking, and is more robust towards errors in the thinking process. Extensive experiments on several complex reasoning tasks, including MMLU, MATH, LogiQA, and visual question-answering demonstrate significant performance improvements over the state-of-the-art prompting methods, such as CoT, and Tree-of-Thought. The qualitative analysis clearly shows that the intermediate reasoning steps elicited by SOCRATIC QUESTIONING are similar to humans’ recursively thinking process of complex reasoning problems.
Toward Joint Language Modeling for Speech Units and Text

Speech and text are two major forms of human language. The research community has been focusing on mapping speech to text or vice versa for many years. However, in the field of language modeling, very little effort has been made to model them jointly. In light of this, we explore joint language modeling for speech units and text. Specifically, we compare different speech tokenizers to transform continuous speech signals into discrete units and use different methods to construct mixed speech-text data. We introduce automatic metrics to evaluate how well the joint LM mixes speech and text. We also fine-tune the LM on downstream spoken language understanding (SLU) tasks with different modalities (speech or text) and test its performance to assess the model’s learning of shared representations. Our results show that by mixing speech units and text with our proposed mixing techniques, the joint LM improves over a speech-only baseline on SLU tasks and shows zero-shot cross-modal transferability.
Domain Private Transformers for Multi-Domain Dialog Systems

Large, general purpose language models have demonstrated impressive performance across many different conversational domains. While multi-domain language models achieve low overall perplexity, their outputs are not guaranteed to stay within the domain of a given input prompt. This paper proposes domain privacy as a novel way to quantify how likely a conditional language model will leak across domains. We also develop policy functions based on token-level domain classification, and propose an efficient fine-tuning method to improve the trained model’s domain privacy. Experiments on membership inference attacks show that our proposed method has comparable resiliency to methods adapted from recent literature on differentially private language models.
Conic10K: A Challenging Math Problem Understanding and Reasoning Dataset

Mathematical understanding and reasoning are crucial tasks for assessing the capabilities of artificial intelligence (AI). However, existing benchmarks either require just a few steps of reasoning, or only contain a small amount of data in one specific topic, making it hard to analyse AI’s behaviour with reference to different problems within a specific topic in detail. In this work, we propose Conic10K, a challenging math problem dataset on conic sections in Chinese senior high school education. Our dataset contains various problems with different reasoning depths, while only the knowledge from conic sections is required. Since the dataset only involves a narrow range of knowledge, it is easy to separately analyse the knowledge a model possesses and the reasoning ability it has. For each problem, we provide a high-quality formal representation, the reasoning steps, and the final solution. Experiments show that existing large language models, including GPT-4, exhibit weak performance on complex reasoning. We hope that our findings could inspire more advanced techniques for precise natural language understanding and reasoning. Our dataset and codes are available at https://github.com/whyNLP/Conic10K.
Evaluating Dependencies in Fact Editing for Language Models: Specificity and Implication Awareness

The potential of using a large language model (LLM) as a knowledge base (KB) has sparked significant interest. To maintain the knowledge acquired by LLMs, we need to ensure that the editing of learned facts respects internal logical constraints, which are known as dependency of knowledge. Existing work on editing LLMs has partially addressed the issue of dependency, when the editing of a fact should apply to its lexical variations without disrupting irrelevant ones. However, they neglect the dependency between a fact and its logical implications. We propose an evaluation protocol with an accompanying question-answering dataset, StandUp, that provides a comprehensive assessment of the editing process considering the above notions of dependency. Our protocol involves setting up a controlled environment in which we edit facts and monitor their impact on LLMs, along with their implications based on If-Then rules. Extensive experiments on StandUp show that existing knowledge editing methods are sensitive to the surface form of knowledge, and that they have limited performance in inferring the implications of edited facts.
CRaSh: Clustering, Removing, and Sharing Enhance Fine-tuning without Full Large Language Model

Instruction tuning has recently been recognized as an effective way of aligning Large Language Models (LLMs) to enhance their generalization ability across various tasks. However, when tuning publicly accessible, centralized LLMs with private instruction data, privacy concerns are inevitable. While direct transfer of parameterized modules between models is a plausible approach to address this, its implications and effectiveness need further exploration. This paper focuses on Offsite-Tuning (OFT), a representative technique that transfers transformer blocks between centralized LLMs and downstream emulators. Given the limited understanding of the underlying mechanism of OFT, we perform an empirical analysis on LLMs from the perspectives of representation and functional similarity. Interestingly, our findings reveal a unique modular structure within the layers of LLMs that appears to emerge as the model size expands. Simultaneously, we note subtle but potentially significant changes in representation and intermediate predictions across the layers. Inspired by these observations, we propose CRaSh, involving Clustering, Removing, and Sharing, a training-free strategy to derive improved emulators from LLMs. CRaSh significantly boosts performance of OFT with billions of parameters. Furthermore, we investigate the optimal solutions yielded by fine-tuning with and without full model through the lens of loss landscape. Our findings demonstrate a linear connectivity among these optima falling over the same basin, thereby highlighting the effectiveness of CRaSh and OFT.
Revisiting Source Context in Nearest Neighbor Machine Translation

Nearest neighbor machine translation (kNN-MT), which interpolates target token probabilities with estimates derived from additional examples, has achieved significant improvements and attracted extensive interest in recent years. However, existing research does not explicitly consider the source context when retrieving similar examples, potentially leading to suboptimal performance. To address this, we comprehensively revisit the role of source context and propose a simple and effective method for improving neural machine translation via source context enhancement, demonstrating its crucial role in both retrieving superior examples and determining more suitable interpolation coefficients. Furthermore, we reveal that the probability estimation can be further optimized by incorporating a source-aware distance calibration module. Comprehensive experiments show that our proposed approach can be seamlessly integrated with representative kNN-MT baselines, resulting in substantial improvements over these strong baselines across a number of settings and domains. Remarkably, these improvements can reach up to 1.6 BLEU points.
TOD-Flow: Modeling the Structure of Task-Oriented Dialogues

Task-Oriented Dialogue (TOD) systems have become crucial components in interactive artificial intelligence applications. While recent advances have capitalized on pre-trained language models (PLMs), they exhibit limitations regarding transparency and controllability. To address these challenges, we propose a novel approach focusing on inferring the TOD-flow graph from dialogue data annotated with dialog acts, uncovering the underlying task structure in the form of a graph. The inferred TOD-flow graph can be easily integrated with any dialogue model to improve its prediction performance, transparency, and controllability. Our TOD-flow graph learns what a model can, should, and should not predict, effectively reducing the search space and providing a rationale for the model’s prediction. We show that the proposed TOD-flow graph better resemble human-annotated graphs compared to prior approaches. Furthermore, when combined with several dialogue policies and end-to-end dialogue models, we demonstrate that our approach significantly improves dialog act classification and end-to-end response generation performance in the MultiWOZ and SGD benchmarks.
A Self-training Framework for Automated Medical Report Generation

Medical report generation, focusing on automatically generating accurate clinical findings from medical images, is an important medical artificial intelligence task. It reduces the workload of physicians in writing reports. Many of the current methods depend heavily on labeled datasets that include a large amount of image-report pairs, but such datasets labeled by physicians are hard to acquire in clinical practice. To this end, in this paper, we introduce a self-training framework named REMOTE (i.e., Revisiting sElf-training for Medical repOrT gEneration) to exploit the unlabeled medical images and a reference-free evaluation metric MedCLIPScore to augment a small-scale medical report generation dataset for training accurate medical report generation model. Experiments and analysis conducted on the MIMIC-CXR and IU-Xray benchmark datasets demonstrate that, our REMOTE framework, using 1% labeled training data, achieves competitive performance with previous fully-supervised models that are trained on entire training data.
Conceptual structure coheres in human cognition but not in large language models

Neural network models of language have long been used as a tool for developing hypotheses about conceptual representation in the mind and brain. For many years, such use involved extracting vector-space representations of words and using distances among these to predict or understand human behavior in various semantic tasks. In contemporary language models, however, it is possible to interrogate the latent structure of conceptual representations using methods nearly identical to those commonly used with human participants. The current work uses three common techniques borrowed from cognitive psychology to estimate and compare lexical-semantic structure in both humans and a well-known large language model, the DaVinci variant of GPT-3. In humans, we show that conceptual structure is robust to differences in culture, language, and method of estimation. Structures estimated from the LLM behavior, while individually fairly consistent with those estimated from human behavior, depend much more upon the particular task used to generate behavior responses–responses generated by the very same model in the three tasks yield estimates of conceptual structure that cohere less with one another than do human structure estimates. The results suggest one important way that knowledge inhering in contemporary LLMs can differ from human cognition.