pwc-archive/papers-with-abstracts huggingface dataset:
Robust Utility-Preserving Text Anonymization Based on Large Language Models
Authors: Tianyu Yang, Xiaodan Zhu, Iryna Gurevych
Date: 2024-07-16 00:00:00
Tasks: Text Anonymization

Text anonymization is crucial for sharing sensitive data while maintaining privacy. Existing techniques face the emerging challenges of re-identification attack ability of Large Language Models (LLMs), which have shown advanced capability in memorizing detailed information and patterns as well as connecting disparate pieces of information. In defending against LLM-based re-identification attacks, anonymization could jeopardize the utility of the resulting anonymized data in downstream tasks -- the trade-off between privacy and data utility requires deeper understanding within the context of LLMs. This paper proposes a framework composed of three LLM-based components -- a privacy evaluator, a utility evaluator, and an optimization component, which work collaboratively to perform anonymization. To provide a practical model for large-scale and real-time environments, we distill the anonymization capabilities into a lightweight model using Direct Preference Optimization (DPO). Extensive experiments demonstrate that the proposed models outperform baseline models, showing robustness in reducing the risk of re-identification while preserving greater data utility in downstream tasks. Our code and dataset are available at https://github.com/UKPLab/arxiv2024-rupta.
How Are LLMs Mitigating Stereotyping Harms? Learning from Search Engine Studies
Authors: Alina Leidinger, Richard Rogers
Date: 2024-07-16 00:00:00

With the widespread availability of LLMs since the release of ChatGPT and increased public scrutiny, commercial model development appears to have focused their efforts on 'safety' training concerning legal liabilities at the expense of social impact evaluation. This mimics a similar trend which we could observe for search engine autocompletion some years prior. We draw on scholarship from NLP and search engine auditing and present a novel evaluation task in the style of autocompletion prompts to assess stereotyping in LLMs. We assess LLMs by using four metrics, namely refusal rates, toxicity, sentiment and regard, with and without safety system prompts. Our findings indicate an improvement to stereotyping outputs with the system prompt, but overall a lack of attention by LLMs under study to certain harms classified as toxic, particularly for prompts about peoples/ethnicities and sexual orientation. Mentions of intersectional identities trigger a disproportionate amount of stereotyping. Finally, we discuss the implications of these findings about stereotyping harms in light of the coming intermingling of LLMs and search and the choice of stereotyping mitigation policy to adopt. We address model builders, academics, NLP practitioners and policy makers, calling for accountability and awareness concerning stereotyping harms, be it for training data curation, leader board design and usage, or social impact measurement.
MINI-LLM: Memory-Efficient Structured Pruning for Large Language Models
Authors: Hongrong Cheng, Miao Zhang, Javen Qinfeng Shi
Date: 2024-07-16 00:00:00
Tasks: GPU, Multiple-choice

As Large Language Models (LLMs) grow dramatically in size, there is an increasing trend in compressing and speeding up these models. Previous studies have highlighted the usefulness of gradients for importance scoring in neural network compressing, especially in pruning medium-size networks. However, the substantial memory requirements involved in calculating gradients with backpropagation impede the utilization of gradients in guiding LLM pruning. As a result, most pruning strategies for LLMs rely on gradient-free criteria, such as weight magnitudes or a mix of magnitudes and activations. In this paper, we devise a hybrid pruning criterion, which appropriately integrates magnitude, activation, and gradient to capitalize on feature map sensitivity for pruning LLMs. To overcome memory requirement barriers, we estimate gradients using only forward passes. Based on this, we propose a Memory-effIcieNt structured prunIng procedure for LLMs (MINI-LLM) to remove no-critical channels and multi-attention heads. Experimental results demonstrate the superior performance of MINI-LLM over existing gradient-free methods on three LLMs: LLaMA, BLOOM, and OPT across various downstream tasks (classification, multiple-choice, and generation), while MINI-LLM maintains a GPU memory footprint akin to gradient-free methods.
ECoh: Turn-level Coherence Evaluation for Multilingual Dialogues
Authors: John Mendonça, Isabel Trancoso, Alon Lavie
Date: 2024-07-16 00:00:00
Tasks: Coherence Evaluation, Dialogue Evaluation

Despite being heralded as the new standard for dialogue evaluation, the closed-source nature of GPT-4 poses challenges for the community. Motivated by the need for lightweight, open source, and multilingual dialogue evaluators, this paper introduces GenResCoh (Generated Responses targeting Coherence). GenResCoh is a novel LLM generated dataset comprising over 130k negative and positive responses and accompanying explanations seeded from XDailyDialog and XPersona covering English, French, German, Italian, and Chinese. Leveraging GenResCoh, we propose ECoh (Evaluation of Coherence), a family of evaluators trained to assess response coherence across multiple languages. Experimental results demonstrate that ECoh achieves multilingual detection capabilities superior to the teacher model (GPT-3.5-Turbo) on GenResCoh, despite being based on a much smaller architecture. Furthermore, the explanations provided by ECoh closely align in terms of quality with those generated by the teacher model.
A Comprehensive Evaluation of Large Language Models on Temporal Event Forecasting
Authors: He Chang, Chenchen Ye, Zhulin Tao, Jie Wu, Zhengmao Yang, Yunshan Ma, Xianglin Huang, Tat-Seng Chua
Date: 2024-07-16 00:00:00
Tasks: Mathematical Reasoning, Question Answering, RAG, Retrieval, Retrieval-augmented Generation

Recently, Large Language Models (LLMs) have demonstrated great potential in various data mining tasks, such as knowledge question answering, mathematical reasoning, and commonsense reasoning. However, the reasoning capability of LLMs on temporal event forecasting has been under-explored. To systematically investigate their abilities in temporal event forecasting, we conduct a comprehensive evaluation of LLM-based methods for temporal event forecasting. Due to the lack of a high-quality dataset that involves both graph and textual data, we first construct a benchmark dataset, named MidEast-TE-mini. Based on this dataset, we design a series of baseline methods, characterized by various input formats and retrieval augmented generation (RAG) modules. From extensive experiments, we find that directly integrating raw texts into the input of LLMs does not enhance zero-shot extrapolation performance. In contrast, fine-tuning LLMs with raw texts can significantly improve performance. Additionally, LLMs enhanced with retrieval modules can effectively capture temporal relational patterns hidden in historical events. However, issues such as popularity bias and the long-tail problem persist in LLMs, particularly in the retrieval-augmented generation (RAG) method. These findings not only deepen our understanding of LLM-based event forecasting methods but also highlight several promising research directions. We consider that this comprehensive evaluation, along with the identified research opportunities, will significantly contribute to future research on temporal event forecasting through LLMs.
AdaptEval: Evaluating Large Language Models on Domain Adaptation for Text Summarization
Authors: Anum Afzal, Ribin Chalumattu, Florian Matthes, Laura Mascarell
Date: 2024-07-16 00:00:00
Tasks: Abstractive Text Summarization, Domain Adaptation, In-Context Learning, Text Summarization

Despite the advances in the abstractive summarization task using Large Language Models (LLM), there is a lack of research that asses their abilities to easily adapt to different domains. We evaluate the domain adaptation abilities of a wide range of LLMs on the summarization task across various domains in both fine-tuning and in-context learning settings. We also present AdaptEval, the first domain adaptation evaluation suite. AdaptEval includes a domain benchmark and a set of metrics to facilitate the analysis of domain adaptation. Our results demonstrate that LLMs exhibit comparable performance in the in-context learning setting, regardless of their parameter scale.
MMSD-Net: Towards Multi-modal Stuttering Detection
Authors: Liangyu Nie, Sudarsana Reddy Kadiri, Ruchit Agrawal
Date: 2024-07-16 00:00:00

Stuttering is a common speech impediment that is caused by irregular disruptions in speech production, affecting over 70 million people across the world. Standard automatic speech processing tools do not take speech ailments into account and are thereby not able to generate meaningful results when presented with stuttered speech as input. The automatic detection of stuttering is an integral step towards building efficient, context-aware speech processing systems. While previous approaches explore both statistical and neural approaches for stuttering detection, all of these methods are uni-modal in nature. This paper presents MMSD-Net, the first multi-modal neural framework for stuttering detection. Experiments and results demonstrate that incorporating the visual signal significantly aids stuttering detection, and our model yields an improvement of 2-17% in the F1-score over existing state-of-the-art uni-modal approaches.
Scientific QA System with Verifiable Answers
Authors: Adela Ljajić, Miloš Košprdić, Bojana Bašaragin, Darija Medvecki, Lorenzo Cassano, Nikola Milošević
Date: 2024-07-16 00:00:00
Tasks: Articles, Information Retrieval, Misinformation, Natural Language Inference, Question Answering, RAG, Retrieval, Retrieval-augmented Generation

In this paper, we introduce the VerifAI project, a pioneering open-source scientific question-answering system, designed to provide answers that are not only referenced but also automatically vetted and verifiable. The components of the system are (1) an Information Retrieval system combining semantic and lexical search techniques over scientific papers (PubMed), (2) a Retrieval-Augmented Generation (RAG) module using fine-tuned generative model (Mistral 7B) and retrieved articles to generate claims with references to the articles from which it was derived, and (3) a Verification engine, based on a fine-tuned DeBERTa and XLM-RoBERTa models on Natural Language Inference task using SciFACT dataset. The verification engine cross-checks the generated claim and the article from which the claim was derived, verifying whether there may have been any hallucinations in generating the claim. By leveraging the Information Retrieval and RAG modules, Verif.ai excels in generating factual information from a vast array of scientific sources. At the same time, the Verification engine rigorously double-checks this output, ensuring its accuracy and reliability. This dual-stage process plays a crucial role in acquiring and confirming factual information, significantly enhancing the information landscape. Our methodology could significantly enhance scientists' productivity, concurrently fostering trust in applying generative language models within scientific domains, where hallucinations and misinformation are unacceptable.
Trust No Bot: Discovering Personal Disclosures in Human-LLM Conversations in the Wild
Authors: Niloofar Mireshghallah, Maria Antoniak, Yash More, Yejin Choi, Golnoosh Farnadi
Date: 2024-07-16 00:00:00
Tasks: Chatbot

Measuring personal disclosures made in human-chatbot interactions can provide a better understanding of users' AI literacy and facilitate privacy research for large language models (LLMs). We run an extensive, fine-grained analysis on the personal disclosures made by real users to commercial GPT models, investigating the leakage of personally identifiable and sensitive information. To understand the contexts in which users disclose to chatbots, we develop a taxonomy of tasks and sensitive topics, based on qualitative and quantitative analysis of naturally occurring conversations. We discuss these potential privacy harms and observe that: (1) personally identifiable information (PII) appears in unexpected contexts such as in translation or code editing (48% and 16% of the time, respectively) and (2) PII detection alone is insufficient to capture the sensitive topics that are common in human-chatbot interactions, such as detailed sexual preferences or specific drug use habits. We believe that these high disclosure rates are of significant importance for researchers and data curators, and we call for the design of appropriate nudging mechanisms to help users moderate their interactions.
States Hidden in Hidden States: LLMs Emerge Discrete State Representations Implicitly
Authors: JunHao Chen, Shengding Hu, Zhiyuan Liu, Maosong Sun
Date: 2024-07-16 00:00:00

Large Language Models (LLMs) exhibit various emergent abilities. Among these abilities, some might reveal the internal working mechanisms of models. In this paper, we uncover a novel emergent capability in models: the intrinsic ability to perform extended sequences of calculations without relying on chain-of-thought step-by-step solutions. Remarkably, the most advanced models can directly output the results of two-digit number additions with lengths extending up to 15 addends. We hypothesize that the model emerges Implicit Discrete State Representations (IDSRs) within its hidden states and performs symbolic calculations internally. To test this hypothesis, we design a sequence of experiments that look into the hidden states. Specifically, we first confirm that IDSRs exist. Then, we provide interesting observations about the formation of IDSRs from layer, digit, and sequence perspectives. Finally, we confirm that models indeed use IDSRs to produce the final answers. However, we also discover that these state representations are far from lossless in current open-sourced models, leading to inaccuracies in their final performance. Our work presents a novel exploration of LLMs' symbolic calculation abilities and the underlying mechanisms.
SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questions
Authors: Shicheng Liu, Sina J. Semnani, Harold Triedman, Jialiang Xu, Isaac Dan Zhao, Monica S. Lam
Date: 2024-07-16 00:00:00
Tasks: In-Context Learning, Knowledge Base Question Answering, Question Answering

Large Language Models (LLMs) have led to significant improvements in the Knowledge Base Question Answering (KBQA) task. However, datasets used in KBQA studies do not capture the true complexity of KBQA tasks. They either have simple questions, use synthetically generated logical forms, or are based on small knowledge base (KB) schemas. We introduce the SPINACH dataset, an expert-annotated KBQA dataset collected from discussions on Wikidata's "Request a Query" forum with 320 decontextualized question-SPARQL pairs. The complexity of these in-the-wild queries calls for a KBQA system that can dynamically explore large and often incomplete schemas and reason about them, as it is infeasible to create a comprehensive training dataset. We also introduce an in-context learning KBQA agent, also called SPINACH, that mimics how a human expert would write SPARQLs to handle challenging questions. SPINACH achieves a new state of the art on the QALD-7, QALD-9 Plus and QALD-10 datasets by 31.0%, 27.0%, and 10.0% in $F_1$, respectively, and coming within 1.6% of the fine-tuned LLaMA SOTA model on WikiWebQuestions. On our new SPINACH dataset, the SPINACH agent outperforms all baselines, including the best GPT-4-based KBQA agent, by at least 38.1% in $F_1$.
Representation Bias in Political Sample Simulations with Large Language Models
Authors: Weihong Qi, Hanjia Lyu, Jiebo Luo
Date: 2024-07-16 00:00:00

This study seeks to identify and quantify biases in simulating political samples with Large Language Models, specifically focusing on vote choice and public opinion. Using the GPT-3.5-Turbo model, we leverage data from the American National Election Studies, German Longitudinal Election Study, Zuobiao Dataset, and China Family Panel Studies to simulate voting behaviors and public opinions. This methodology enables us to examine three types of representation bias: disparities based on the the country's language, demographic groups, and political regime types. The findings reveal that simulation performance is generally better for vote choice than for public opinions, more accurate in English-speaking countries, more effective in bipartisan systems than in multi-partisan systems, and stronger in democratic settings than in authoritarian regimes. These results contribute to enhancing our understanding and developing strategies to mitigate biases in AI applications within the field of computational social science.
Revisiting the Impact of Pursuing Modularity for Code Generation
Authors: Deokyeong Kang, Ki Jung Seo, Taeuk Kim
Date: 2024-07-16 00:00:00
Tasks: Code Generation

Modular programming, which aims to construct the final program by integrating smaller, independent building blocks, has been regarded as a desirable practice in software development. However, with the rise of recent code generation agents built upon large language models (LLMs), a question emerges: is this traditional practice equally effective for these new tools? In this work, we assess the impact of modularity in code generation by introducing a novel metric for its quantitative measurement. Surprisingly, unlike conventional wisdom on the topic, we find that modularity is not a core factor for improving the performance of code generation models. We also explore potential explanations for why LLMs do not exhibit a preference for modular code compared to non-modular code.
InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains
Authors: Yinzhu Quan, Zefang Liu
Date: 2024-07-16 00:00:00
Tasks: Decision Making, Language Modeling, Language Modelling, Large Language Model, Management, Zero-Shot Learning

Supply chain management (SCM) involves coordinating the flow of goods, information, and finances across various entities to deliver products efficiently. Effective inventory management is crucial in today's volatile and uncertain world. Previous research has demonstrated the superiority of heuristic methods and reinforcement learning applications in inventory management. However, the application of large language models (LLMs) as autonomous agents in multi-agent systems for inventory management remains underexplored. This study introduces a novel approach using LLMs to manage multi-agent inventory systems. Leveraging their zero-shot learning capabilities, our model, InvAgent, enhances resilience and improves efficiency across the supply chain network. Our contributions include utilizing LLMs for zero-shot learning to enable adaptive and informed decision-making without prior training, providing explainability and clarity through chain-of-thought, and demonstrating dynamic adaptability to varying demand scenarios while reducing costs and preventing stockouts. Extensive evaluations across different scenarios highlight the efficiency of our model in SCM.
Estimating Agreement by Chance for Sequence Annotation
Authors: Diya Li, Carolyn Rosé, Ao Yuan, Chunxiao Zhou
Date: 2024-07-16 00:00:00

In the field of natural language processing, correction of performance assessment for chance agreement plays a crucial role in evaluating the reliability of annotations. However, there is a notable dearth of research focusing on chance correction for assessing the reliability of sequence annotation tasks, despite their widespread prevalence in the field. To address this gap, this paper introduces a novel model for generating random annotations, which serves as the foundation for estimating chance agreement in sequence annotation tasks. Utilizing the proposed randomization model and a related comparison approach, we successfully derive the analytical form of the distribution, enabling the computation of the probable location of each annotated text segment and subsequent chance agreement estimation. Through a combination simulation and corpus-based evaluation, we successfully assess its applicability and validate its accuracy and efficacy.
A Pilot Study of GSLM-based Simulation of Foreign Accentuation Only Using Native Speech Corpora
Authors: Kentaro Onda, Joonyong Park, Nobuaki Minematsu, Daisuke Saito
Date: 2024-07-16 00:00:00
Tasks: Language Modeling, Language Modelling

We propose a method of simulating the human process of foreign accentuation using Generative Spoken Language Model (GSLM) only with native speech corpora. When one listens to spoken words of a foreign language and repeats them, the repeated speech is often with the accent of that listener's L1. This is said to be because the spoken words are mentally represented as a sequence of phonological units of the L1, and those units are used for oral reproduction. We simulate this process by inputting speech of language A into GSLM of language B to add B's accent onto the input speech. The process of running ASR of the L1 for foreign input speech and giving the ASR result to TTS of the L1 can be viewed as a naive implementation of this approach. The results of our experiments show that the synthesized accent of the output speech is highly natural, compared to real samples of A generated by speakers whose L1 is B, and that the degree of accentuation is controllable.
Ancient Korean Archive Translation: Comparison Analysis on Statistical phrase alignment, LLM in-context learning, and inter-methodological approach
Authors: Sojung Lucia Kim, Taehong Jang, Joonmo Ahn
Date: 2024-07-16 00:00:00
Tasks: In-Context Learning, Machine Translation, Sentence, Translation

This study aims to compare three methods for translating ancient texts with sparse corpora: (1) the traditional statistical translation method of phrase alignment, (2) in-context LLM learning, and (3) proposed inter methodological approach - statistical machine translation method using sentence piece tokens derived from unified set of source-target corpus. The performance of the proposed approach in this study is 36.71 in BLEU score, surpassing the scores of SOLAR-10.7B context learning and the best existing Seq2Seq model. Further analysis and discussion are presented.
Beyond Binary: Multiclass Paraphasia Detection with Generative Pretrained Transformers and End-to-End Models
Authors: Matthew Perez, Aneesha Sampath, Minxue Niu, Emily Mower Provost
Date: 2024-07-16 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Aphasia is a language disorder that can lead to speech errors known as paraphasias, which involve the misuse, substitution, or invention of words. Automatic paraphasia detection can help those with Aphasia by facilitating clinical assessment and treatment planning options. However, most automatic paraphasia detection works have focused solely on binary detection, which involves recognizing only the presence or absence of a paraphasia. Multiclass paraphasia detection represents an unexplored area of research that focuses on identifying multiple types of paraphasias and where they occur in a given speech segment. We present novel approaches that use a generative pretrained transformer (GPT) to identify paraphasias from transcripts as well as two end-to-end approaches that focus on modeling both automatic speech recognition (ASR) and paraphasia classification as multiple sequences vs. a single sequence. We demonstrate that a single sequence model outperforms GPT baselines for multiclass paraphasia detection.