ymoslem/acl-abstracts huggingface dataset:
Can LLMs get help from other LLMs without revealing private information? (2024)

Cascades are a common type of machine learning systems in which a large, remote model can be queried if a local model is not able to accurately label a user`s data by itself. Serving stacks for large language models (LLMs) increasingly use cascades due to their ability to preserve task performance while dramatically reducing inference costs. However, applying cascade systems in situations where the local model has access to sensitive data constitutes a significant privacy risk for users since such data could be forwarded to the remote model. In this work, we show the feasibility of applying cascade systems in such setups by equipping the local model with privacy-preserving techniques that reduce the risk of leaking private information when querying the remote model. To quantify information leakage in such setups, we introduce two privacy measures. We then propose a system that leverages the recently introduced social learning paradigm in which LLMs collaboratively learn from each other by exchanging natural language. Using this paradigm, we demonstrate on several datasets that our methods minimize the privacy loss while at the same time improving task performance compared to a non-cascade baseline.
Cloaked Classifiers: Pseudonymization Strategies on Sensitive Classification Tasks (2024)

Protecting privacy is essential when sharing data, particularly in the case of an online radicalization dataset that may contain personal information. In this paper, we explore the balance between preserving data usefulness and ensuring robust privacy safeguards, since regulations like the European GDPR shape how personal information must be handled. We share our method for manually pseudonymizing a multilingual radicalization dataset, ensuring performance comparable to the original data. Furthermore, we highlight the importance of establishing comprehensive guidelines for processing sensitive NLP data by sharing our complete pseudonymization process, our guidelines, the challenges we encountered as well as the resulting dataset.
Improving Authorship Privacy: Adaptive Obfuscation with the Dynamic Selection of Techniques (2024)

Authorship obfuscation, the task of rewriting text to protect the original author`s identity, is becoming increasingly important due to the rise of advanced NLP tools for authorship attribution techniques. Traditional methods for authorship obfuscation face significant challenges in balancing content preservation, fluency, and style concealment. This paper introduces a novel approach, the Obfuscation Strategy Optimizer (OSO), which dynamically selects the optimal obfuscation technique based on a combination of metrics including embedding distance, meaning similarity, and fluency. By leveraging an ensemble of language models OSO achieves superior performance in preserving the original content`s meaning and grammatical fluency while effectively concealing the author`s unique writing style. Experimental results demonstrate that the OSO outperforms existing methods and approaches the performance of larger language models. Our evaluation framework incorporates adversarial testing against state-of-the-art attribution systems to validate the robustness of the obfuscation techniques. We release our code publicly at https://github.com/BBN-E/ObfuscationStrategyOptimizer
Deconstructing Classifiers: Towards A Data Reconstruction Attack Against Text Classification Models (2024)

Natural language processing (NLP) models have become increasingly popular in real-world applications, such as text classification. However, they are vulnerable to privacy attacks, including data reconstruction attacks that aim to extract the data used to train the model. Most previous studies on data reconstruction attacks have focused on LLM, while classification models were assumed to be more secure. In this work, we propose a new targeted data reconstruction attack called the Mix And Match attack, which takes advantage of the fact that most classification models are based on LLM. The Mix And Match attack uses the base model of the target model to generate candidate tokens and then prunes them using the classification head. We extensively demonstrate the effectiveness of the attack using both random and organic canaries. This work highlights the importance of considering the privacy risks associated with data reconstruction attacks in classification models and offers insights into possible leakages.
PrivaT5: A Generative Language Model for Privacy Policies (2024)

In the era of of digital privacy, users often neglect to read privacy policies due to their complexity. To bridge this gap, NLP models have emerged to assist in understanding privacy policies. While recent generative language models like BART and T5 have shown prowess in text generation and discriminative tasks being framed as generative ones, their application to privacy policy domain tasks remains unexplored. To address that, we introduce PrivaT5, a T5-based model that is further pre-trained on privacy policy text. We evaluate PrivaT5 over a diverse privacy policy related tasks and notice its superior performance over T5, showing the utility of continued domain-specific pre-training. Our results also highlight challenges faced by these generative models in complex structured output label space, especially in sequence tagging tasks, where they fall short compared to lighter encoder-only models.
Reinforcement Learning-Driven LLM Agent for Automated Attacks on LLMs (2024)

Recently, there has been a growing focus on conducting attacks on large language models (LLMs) to assess LLMs' safety. Yet, existing attack methods face challenges, including the need to access model weights or merely ensuring LLMs output harmful information without controlling the specific content of their output. Exactly control of the LLM output can produce more inconspicuous attacks which could reveal a new page for LLM security. To achieve this, we propose RLTA: the Reinforcement Learning Targeted Attack, a framework that is designed for attacking language models (LLMs) and is adaptable to both white box (weight accessible) and black box (weight inaccessible) scenarios. It is capable of automatically generating malicious prompts that trigger target LLMs to produce specific outputs. We demonstrate RLTA in two different scenarios: LLM trojan detection and jailbreaking. The comprehensive experimental results show the potential of RLTA in enhancing the security measures surrounding contemporary LLMs.
A Privacy-preserving Approach to Ingest Knowledge from Proprietary Web-based to Locally Run Models for Medical Progress Note Generation (2024)

Clinical documentation is correlated with increasing clinician burden, leading to the rise of automated methods to generate medical notes. Due to the sensitive nature of patient electronic health records (EHRs), locally run models are preferred for a variety of reasons including privacy, bias, and cost. However, most open-source locally run models (including medical-specific) are much smaller with limited input context size compared to the more powerful closed-source large language models (LLMs) generally available through web APIs (Application Programming Interfaces). In this paper, we propose a framework to harness superior reasoning capabilities and medical knowledge from closed-source online LLMs in a privacy-preserving manner and seamlessly incorporate it into locally run models. Specifically, we leverage a web-based model to distill the vast patient information available in EHRs into a clinically relevant subset without sending sensitive patient health information online and use this distilled knowledge to generate progress notes by a locally run model. Our ablation results indicate that the proposed framework improves the performance of the Mixtral model on progress note generation by 4.6 points on ROUGE (a text-matching based metric) and 7.56 points on MEDCON F1 (a metric that measures the clinical concepts overlap).
Beyond the Hype: Identifying and Analyzing Math Word Problem-Solving Challenges for Large Language Models (2024)

Despite not being explicitly trained for this purpose, models like Mistral and LLaMA have demonstrated impressive results across numerous tasks, including generating solutions to Mathematical Word Problems (MWPs). A MWP involves translating a textual description into a mathematical model or equation that solving it. However, these models face challenges in accurately interpreting and utilizing the numerical information present in the MWP statements, which can lead to errors in the generated solutions. To better understand the limitations of LLMs, we analyzed the MWP where models failed to accurately solve problems from the SVAMP dataset. By categorizing these MWPs, we identify specific types of problems where the models are most prone to errors, providing insights into the underlying challenges faced by LLMs in problem-solving scenarios and open new modeling opportunities. By understanding the expected errors, researchers can design strategies to adequately model problems more effectively and choose the most suitable LLM for solving them taking into account each model`s strengths and weaknesses.
Enhancing Situation Awareness through Model-Based Explanation Generation (2024)

Robots are often deployed in remote locations for tasks such as exploration, where users cannot directly perceive the agent and its environment. For Human-In-The-Loop applications, operators must have a comprehensive understanding of the robot`s current state and its environment to take necessary actions and effectively assist the agent. In this work, we compare different explanation styles to determine the most effective way to convey real-time updates to users. Additionally, we formulate these explanation styles as separate fine-tuning tasks and assess the effectiveness of large language models in delivering in-mission updates to maintain situation awareness. The code and dataset for this work are available at:{---}{---}{---}
Deciphering Political Entity Sentiment in News with Large Language Models: Zero-Shot and Few-Shot Strategies (2024)

Sentiment analysis plays a pivotal role in understanding public opinion, particularly in the political domain where the portrayal of entities in news articles influences public perception. In this paper, we investigate the effectiveness of Large Language Models (LLMs) in predicting entity-specific sentiment from political news articles. Leveraging zero-shot and few-shot strategies, we explore the capability of LLMs to discern sentiment towards political entities in news content. Employing a chain-of-thought (COT) approach augmented with rationale in few-shot in-context learning, we assess whether this method enhances sentiment prediction accuracy. Our evaluation on sentiment-labeled datasets demonstrates that LLMs, outperform fine-tuned BERT models in capturing entity-specific sentiment. We find that learning in-context significantly improves model performance, while the self-consistency mechanism enhances consistency in sentiment prediction. Despite the promising results, we observe inconsistencies in the effectiveness of the COT prompting method. Overall, our findings underscore the potential of LLMs in entity-centric sentiment analysis within the political news domain and highlight the importance of suitable prompting strategies and model architectures.
Event Detection in the Socio Political Domain (2024)

In this paper we present two approaches for detection of socio political events: the first is based on manually crafted keyword combinations and the second one is based on a BERT classifier. We compare the performance of the two systems on a dataset of socio-political events. Interestingly, the systems demonstrate complementary performance: both showing their best accuracy on non overlapping sets of event types. In the evaluation section we provide insights on the effect of taxonomy mapping on the event detection evaluation. We also review in the related work section the most important resources and approaches for event extraction in the recent years.
Multi-Dimensional Insights: Annotated Dataset of Stance, Sentiment, and Emotion in Facebook Comments on Tunisia`s July 25 Measures (2024)

On July 25, 2021, Tunisian President Kais Saied announced the suspension of parliament and dismissal of Prime Minister Hichem Mechichi, a move that sparked intense public debate. This study investigates Tunisian public opinion regarding these events by analyzing a corpus of 7,535 Facebook comments collected from the official Tunisian presidency page, specifically the post announcing the July 25 measures. A team of three annotators labeled a subset of 5,000 comments, categorizing each comment`s political stance (supportive, opposing, or neutral), sentiment (positive, negative, or neutral), emotions, presence of hate speech, aggressive tone, and racism. The inter-annotator agreement, measured by Cohen`s kappa, was 0.61, indicating substantial consensus. The analysis reveals that a majority of commenters supported President Saied`s actions, outnumbering those who opposed or took a neutral stance. Moreover, the overall sentiment expressed in the comments was predominantly positive. This study provides valuable insights into the complex landscape of public opinion in Tunisia during a crucial moment in the country`s ongoing political transformation, highlighting the role of social media as a platform for political discourse and engagement.
Masking Explicit Pro-Con Expressions for Development of a Stance Classification Dataset on Assembly Minutes (2024)

In this paper, a new dataset for Stance Classification based on assembly minutes is introduced. We develop it by using publicity available minutes taken from diverse Japanese local governments including prefectural, city, and town assemblies. In order to make the task to predict a stance from content of a politician`s utterance without explicit stance expressions, predefined words that directly convey the speaker`s stance in the utterance are replaced by a special token. Those masked words are also used to assign a golden label, either agreement or disagreement, to the utterance. Finally, we constructed total 15,018 instances automatically from 47 Japanese local governments. The dataset is used in the shared Stance Classification task evaluated in the NTCIR-17 QA-Lab-PoliInfo-4, and is now publicity available. Since the construction method of the dataset is automatic, we can still apply it to obtain more instances from the other Japanese local governments.
Analysing Pathos in User-Generated Argumentative Text (2024)

While persuasion has been extensively examined in the context of politicians' speeches, there exists a notable gap in the understanding of the pathos role in user-generated argumentation. This paper presents an exploratory study into the pathos dimension of user-generated arguments and formulates ideas on how pathos could be incorporated in argument mining. Using existing sentiment and emotion detection tools, this research aims to obtain insights into the role of emotion in argumentative public discussion on controversial topics, explores the connection between sentiment and stance, and detects frequent emotion-related words for a given topic.
Knowledge Graph Representation for Political Information Sources (2024)

With the rise of computational social science, many scholars utilize data analysis and natural language processing tools to analyze social media, news articles, and other accessible data sources for examining political and social discourse. Particularly, the study of the emergence of echo-chambers due to the dissemination of specific information has become a topic of interest in mixed methods research areas. In this paper, we analyze data collected from two news portals, Breitbart News (BN) and New York Times (NYT) to prove the hypothesis that the formation of echo-chambers can be partially explained on the level of an individual information consumption rather than a collective topology of individuals' social networks. Our research findings are presented through knowledge graphs, utilizing a dataset spanning 11.5 years gathered from BN and NYT media portals. We demonstrate that the application of knowledge representation techniques to the aforementioned news streams highlights, contrary to common assumptions, shows relative {\textquotedblleft}internal{\textquotedblright} neutrality of both sources and polarizing attitude towards a small fraction of entities. Additionally, we argue that such characteristics in information sources lead to fundamental disparities in audience worldviews, potentially acting as a catalyst for the formation of echo-chambers.
Analyzing Conflict Through Data: A Dataset on the Digital Framing of Sheikh Jarrah Evictions (2024)

This study empirically investigates the role of social media in tracing the evolution of the May 2021 Israeli-Palestinian crisis, centered on the Sheikh Jarrah evictions. Analyzing a dataset of 370,747 English tweets from 120,173 users from May 9-21, 2021, the research employs a mixed-methods approach combining computational techniques and qualitative content analysis. Findings support the hypothesis that social media interactions reliably map crisis dynamics, as evidenced by hashtags like {\#}SaveSheikhJarrah corresponding to critical shifts, though virality did not correlate with hashtag use. In contrast to prior sentiment-focused studies, the context-driven analysis reveals influencers and state actors shaping polarized narratives along geopolitical lines, with high-profile voices backing Palestinian solidarity while Israeli state accounts endorsed military operations. Evidence of a transcontinental cybercampaign emerged, albeit with limitations due to the English language scope and potential biases from data collection and keyword choices. The study contributes empirical insights into the mediatization of armed conflicts through social media`s competing narratives and information flows within the Israeli-Palestinian context. Recommendations for future multilingual, multi-platform analyses are provided to address limitations.
Semi-Automatic Topic Discovery and Classification for Epidemic Intelligence via Large Language Models (2024)

This paper introduces a novel framework to harness Large Language Models (LLMs) for Epidemic Intelligence, focusing on identifying and categorizing emergent socio-political phenomena within health crises, with a spotlight on the COVID-19 pandemic. Our approach diverges from traditional methods, such as Topic Models, by providing explicit support to analysts through the identification of distinct thematic areas and the generation of clear, actionable statements for each topic. This supports a Zero-shot Classification mechanism, enabling effective matching of news articles to fine-grain topics without the need for model fine-tuning. The framework is designed to be as transparent as possible, producing linguistically informed insights to make the analysis more accessible to analysts who may not be familiar with every subject matter of inherently emerging phenomena. This process not only enhances the precision and relevance of the extracted Epidemic Intelligence but also fosters a collaborative environment where system linguistic abilities and the analyst`s domain expertise are integrated.
Towards quantifying politicization in foreign aid project reports (2024)

We aim to develop a metric of politicization by investigating whether this concept can be operationalized computationally using document embeddings. We are interested in measuring the extent to which foreign aid is politicized. Textual reports of foreign aid projects are often made available by donor governments, but these are large and unstructured. By embedding them in vector space, we can compute similarities between sets of known politicized keywords and the foreign aid reports. We present a pilot study where we apply this metric to USAID reports.
Echo-chambers and Idea Labs: Communication Styles on Twitter (2024)

This paper investigates the communication styles and structures of Twitter (X) communities within the vaccination context. While mainstream research primarily focuses on the echo-chamber phenomenon, wherein certain ideas are reinforced and participants are isolated from opposing opinions, this study reveals the presence of diverse communication styles across various communities. In addition to the communities exhibiting echo-chamber behavior, this research uncovers communities with distinct communication patterns. By shedding light on the nuanced nature of communication within social networks, this study emphasizes the significance of understanding the diversity of perspectives within online communities.
RoleCraft-GLM: Advancing Personalized Role-Playing in Large Language Models (2024)

This study presents RoleCraft-GLM, an innovative framework aimed at enhancing personalized role-playing with Large Language Models (LLMs). RoleCraft-GLM addresses the key issue of lacking personalized interactions in conversational AI, and offers a solution with detailed and emotionally nuanced character portrayals. We contribute a unique conversational dataset that shifts from conventional celebrity-centric characters to diverse, non-celebrity personas, thus enhancing the realism and complexity of language modeling interactions. Additionally, our approach includes meticulous character development, ensuring dialogues are both realistic and emotionally resonant. The effectiveness of RoleCraft-GLM is validated through various case studies, highlighting its versatility and skill in different scenarios. Our framework excels in generating dialogues that accurately reflect characters' personality traits and emotions, thereby boosting user engagement. In conclusion, RoleCraft-GLM marks a significant leap in personalized AI interactions, and paves the way for more authentic and immersive AI-assisted role-playing experiences by enabling more nuanced and emotionally rich dialogues.
How to use Language Models for Synthetic Text Generation in Cerebrovascular Disease-specific Medical Reports (2024)

The quantity and quality of data have a significant impact on the performance of artificial intelligence (AI). However, in the biomedical domain, data often contains sensitive information such as personal details, making it challenging to secure enough data for medical AI. Consequently, there is a growing interest in synthetic data generation for medical AI. However, research has primarily focused on medical images, with little given to text-based data such as medical records. Therefore, this study explores the application of language models (LMs) for synthetic text generation in low-resource domains like medical records. It compares the results of synthetic text generation based on different LMs. To achieve this, we focused on two criteria for LM-based synthetic text generation of medical records using two keywords entered by the user: 1) the impact of the LM`s knowledge, 2) the impact of the LM`s size. Additionally, we objectively evaluated the generated synthetic text, including representative metrics such as BLUE and ROUGE, along with clinician`s evaluations.
Assessing Generalization for Subpopulation Representative Modeling via In-Context Learning (2024)

This study evaluates the ability of Large Language Model (LLM)-based Subpopulation Representative Models (SRMs) to generalize from empirical data, utilizing in-context learning with data from the 2016 and 2020 American National Election Studies. We explore generalization across response variables and demographic subgroups. While conditioning with empirical data improves performance on the whole, the benefit of in-context learning varies considerably across demographics, sometimes hurting performance for one demographic while helping performance for others. The inequitable benefits of in-context learning for SRM present a challenge for practitioners implementing SRMs, and for decision-makers who might come to rely on them. Our work highlights a need for fine-grained benchmarks captured from diverse subpopulations that test not only fidelity but generalization.