ymoslem/acl-abstracts huggingface dataset:
XATU: A Fine-grained Instruction-based Benchmark for Explainable Text Updates (2024)

Text editing is a crucial task of modifying text to better align with user intents. However, existing text editing benchmark datasets contain only coarse-grained instructions and lack explainability, thus resulting in outputs that deviate from the intended changes outlined in the gold reference. To comprehensively investigate the text editing capabilities of large language models (LLMs), this paper introduces XATU, the first benchmark specifically designed for fine-grained instruction-based explainable text editing. XATU considers finer-grained text editing tasks of varying difficulty (simplification, grammar check, fact-check, etc.), incorporating lexical, syntactic, semantic, and knowledge-intensive edit aspects. To enhance interpretability, we combine LLM-based annotation and human annotation, resulting in a benchmark that includes fine-grained instructions and gold-standard edit explanations. By evaluating existing LLMs against our benchmark, we demonstrate the effectiveness of instruction tuning and the impact of underlying architecture across various editing tasks. Furthermore, extensive experimentation reveals the significant role of explanations in fine-tuning language models for text editing tasks. The benchmark will be open-sourced to support reproduction and facilitate future research at https://github.com/megagonlabs/xatu.
XVD: Cross-Vocabulary Differentiable Training for Generative Adversarial Attacks (2024)

An adversarial attack to a text classifier consists of an input that induces the classifier into an incorrect class prediction, while retaining all the linguistic properties of correctly-classified examples. A popular class of adversarial attacks exploits the gradients of the victim classifier to train a dedicated generative model to produce effective adversarial examples. However, this training signal alone is not sufficient to ensure other desirable properties of the adversarial attacks, such as similarity to non-adversarial examples, linguistic fluency, grammaticality, and so forth. For this reason, in this paper we propose a novel training objective which leverages a set of pretrained language models to promote such properties in the adversarial generation. A core component of our approach is a set of vocabulary-mapping matrices which allow cascading the generative model to any victim or component model of choice, while retaining differentiability end-to-end. The proposed approach has been tested in an ample set of experiments covering six text classification datasets, two victim models, and four baselines. The results show that it has been able to produce effective adversarial attacks, outperforming the compared generative approaches in a majority of cases and proving highly competitive against established token-replacement approaches.
Your Stereotypical Mileage May Vary: Practical Challenges of Evaluating Biases in Multiple Languages and Cultural Contexts (2024)

Warning: This paper contains explicit statements of offensive stereotypes which may be upsetting The study of bias, fairness and social impact in Natural Language Processing (NLP) lacks resources in languages other than English. Our objective is to support the evaluation of bias in language models in a multilingual setting. We use stereotypes across nine types of biases to build a corpus containing contrasting sentence pairs, one sentence that presents a stereotype concerning an underadvantaged group and another minimally changed sentence, concerning a matching advantaged group. We build on the French CrowS-Pairs corpus and guidelines to provide translations of the existing material into seven additional languages. In total, we produce 11,139 new sentence pairs that cover stereotypes dealing with nine types of biases in seven cultural contexts. We use the final resource for the evaluation of relevant monolingual and multilingual masked language models. We find that language models in all languages favor sentences that express stereotypes in most bias categories. The process of creating a resource that covers a wide range of language types and cultural settings highlights the difficulty of bias evaluation, in particular comparability across languages and contexts.
ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus (2024)

We present ZAEBUC-Spoken, a multilingual multidialectal Arabic-English speech corpus. The corpus comprises twelve hours of Zoom meetings involving multiple speakers role-playing a work situation where Students brainstorm ideas for a certain topic and then discuss it with an Interlocutor. The meetings cover different topics and are divided into phases with different language setups. The corpus presents a challenging set for automatic speech recognition (ASR), including two languages (Arabic and English) with Arabic spoken in multiple variants (Modern Standard Arabic, Gulf Arabic, and Egyptian Arabic) and English used with various accents. Adding to the complexity of the corpus, there is also code-switching between these languages and dialects. As part of our work, we take inspiration from established sets of transcription guidelines to present a set of guidelines handling issues of conversational speech, code-switching and orthography of both languages. We further enrich the corpus with two layers of annotations; (1) dialectness level annotation for the portion of the corpus where mixing occurs between different variants of Arabic, and (2) automatic morphological annotations, including tokenization, lemmatization, and part-of-speech tagging.
ZeLa: Advancing Zero-Shot Multilingual Semantic Parsing with Large Language Models and Chain-of-Thought Strategies (2024)

In recent years, there have been significant advancements in semantic parsing tasks, thanks to the introduction of pre-trained language models. However, a substantial gap persists between English and other languages due to the scarcity of annotated data. One promising strategy to bridge this gap involves augmenting multilingual datasets using labeled English data and subsequently leveraging this augmented dataset for training semantic parsers (known as zero-shot multilingual semantic parsing). In our study, we propose a novel framework to effectively perform zero-shot multilingual semantic parsing under the support of large language models (LLMs). Given data annotated pairs (sentence, semantic representation) in English, our proposed framework automatically augments data in other languages via multilingual chain-of-thought (CoT) prompting techniques that progressively construct the semantic form in these languages. By breaking down the entire semantic representation into sub-semantic fragments, our CoT prompting technique simplifies the intricate semantic structure at each step, thereby facilitating the LLMs in generating accurate outputs more efficiently. Notably, this entire augmentation process is achieved without the need for any demonstration samples in the target languages (zero-shot learning). In our experiments, we demonstrate the effectiveness of our method by evaluating it on two well-known multilingual semantic parsing datasets: MTOP and MASSIVE.
ZenPropaganda: A Comprehensive Study on Identifying Propaganda Techniques in Russian Coronavirus-Related Media (2024)

The topic of automatic detection of manipulation and propaganda in the media is not a novel issue; however, it remains an urgent concern that necessitates continuous research focus. The topic is studied within the framework of various papers, competitions and shared tasks, which provide different techniques definitions and include the analysis of text data, images, as well as multi-lingual sources. In this study, we propose a novel multi-level classification scheme for identifying propaganda techniques. We introduce a new Russian dataset ZenPropaganda consisting of coronavirus-related texts collected from Vkontakte and Yandex.Zen platforms, which have been expertly annotated with fine-grained labeling of manipulative spans. We further conduct a comprehensive analysis by comparing our dataset with existing related ones and evaluate the performance of state-of-the-art approaches that have been proposed for them. Furthermore, we provide a detailed discussion of our findings, which can serve as a valuable resource for future research in this field.
Zero- and Few-Shot Prompting with LLMs: A Comparative Study with Fine-tuned Models for Bangla Sentiment Analysis (2024)

The rapid expansion of the digital world has propelled sentiment analysis into a critical tool across diverse sectors such as marketing, politics, customer service, and healthcare. While there have been significant advancements in sentiment analysis for widely spoken languages, low-resource languages, such as Bangla, remain largely under-researched due to resource constraints. Furthermore, the recent unprecedented performance of Large Language Models (LLMs) in various applications highlights the need to evaluate them in the context of low-resource languages. In this study, we present a sizeable manually annotated dataset encompassing 33,606 Bangla news tweets and Facebook comments. We also investigate zero- and few-shot in-context learning with several language models, including Flan-T5, GPT-4, and Bloomz, offering a comparative analysis against fine-tuned models. Our findings suggest that monolingual transformer-based models consistently outperform other models, even in zero and few-shot scenarios. To foster continued exploration, we intend to make this dataset and our research tools publicly available to the broader research community.
Zero-shot Cross-lingual Automated Essay Scoring (2024)

Due to the difficulty of creating high-quality labelled training data for different languages, the low-resource problem is crucial yet challenging for automated essay scoring (AES). However, little attention has been paid to addressing this challenge. In this paper, we propose a novel zero-shot cross-lingual scoring method from the perspectives of pretrained multilingual representation and writing quality alignment to score essays in unseen languages. Specifically, we adopt multilingual pretrained language models as the encoder backbone to deeply and comprehensively represent multilingual essays. Motivated by the fact that the scoring knowledge for evaluating writing quality is comparable across different languages, we introduce an innovative strategy for aligning essays in a language-independent manner. The proposed strategy aims to capture shared knowledge from diverse languages, thereby enhancing the representation of essays written in unseen languages with respect to their quality. We include essay datasets in six languages (Czech, German, English, Spanish, Italian and Portuguese) to establish extensive experiments, and the results demonstrate that our method achieves state-of-the-art cross-lingual scoring performance.
Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning (2024)

Event Causality Identification (ECI) refers to the detection of causal relations between events in texts. However, most existing studies focus on sentence-level ECI with high-resource languages, leaving more challenging document-level ECI (DECI) with low-resource languages under-explored. In this paper, we propose a Heterogeneous Graph Interaction Model with Multi-granularity Contrastive Transfer Learning (GIMC) for zero-shot cross-lingual document-level ECI. Specifically, we introduce a heterogeneous graph interaction network to model the long-distance dependencies between events that are scattered over a document. Then, to improve cross-lingual transferability of causal knowledge learned from the source language, we propose a multi-granularity contrastive transfer learning module to align the causal representations across languages. Extensive experiments show our framework outperforms the previous state-of-the-art model by 9.4{\%} and 8.2{\%} of average F1 score on monolingual and multilingual scenarios respectively. Notably, in the multilingual scenario, our zero-shot framework even exceeds GPT-3.5 with few-shot learning by 24.3{\%} in overall performance.
Zero-shot Event Detection Using a Textual Entailment Model as an Enhanced Annotator (2024)

Zero-shot event detection is a challenging task. Recent research work proposed to use a pre-trained textual entailment (TE) model on this task. However, those methods treated the TE model as a frozen annotator. We treat the TE model as an annotator that can be enhanced. We propose to use TE models to annotate large-scale unlabeled text and use annotated data to finetune the TE model, yielding an improved TE model. Finally, the improved TE model is used for inference on the test set. To improve the efficiency, we propose to use keywords to filter out sentences with a low probability of expressing event(s). To improve the coverage of keywords, we expand limited number of seed keywords using WordNet, so that we can use the TE model to annotate unlabeled text efficiently. The experimental results show that our method can outperform other baselines by 15{\%} on the ACE05 dataset.
Zero-shot Learning for Multilingual Discourse Relation Classification (2024)

Classifying discourse relations is known as a hard task, relying on complex indices. On the other hand, discourse-annotated data is scarce, especially for languages other than English: many corpora, of limited size, exist for several languages but the domain is split between different theoretical frameworks that have a huge impact on the nature of the textual spans to be linked, and the label set used. Moreover, each annotation project implements modifications compared to the theoretical background and other projects. These discrepancies hinder the development of systems taking advantage of all the available data to tackle data sparsity and work on transfer between languages is very limited, almost nonexistent between frameworks, while it could improve our understanding of some theoretical aspects and enhance many applications. In this paper, we propose the first experiments on zero-shot learning for discourse relation classification and investigate several paths in the way source data can be combined, either based on languages, frameworks, or similarity measures. We demonstrate how difficult transfer is for the task at hand, and that the most impactful factor is label set divergence, where the notion of underlying framework possibly conceals crucial disagreements.
Zero-Shot Spoken Language Understanding via Large Language Models: A Preliminary Study (2024)

Zero-shot Spoken Language Understanding (SLU) aims to enable task-oriented dialogue systems to understand user needs without training data. Challenging but worthwhile, zero-shot SLU reduces the time and effort that data labeling takes. Recent advancements in large language models (LLMs), such as GPT3.5 and ChatGPT, have shown promising results in zero-shot settings, which motivates us to explore prompt-based methods. In this study, we investigate whether strong SLU models can be constructed by directly prompting LLMs. Specifically, we propose a simple yet effective two-stage framework dubbed GPT-SLU, which transforms the SLU task into a question-answering problem. Powered by multi-stage mutual guided prompts, GPT-SLU can leverage the correlations between two subtasks in SLU to achieve better predictions, which is greatly explored in the traditional fine-tuning paradigm. Experimental results on three SLU benchmark datasets demonstrate the significant potential of LLMs for zero-shot SLU. Comprehensive analyses validate the effectiveness of our proposed framework and also indicate that there is still room for further improvement of LLMs in SLU scenarios.
From Multimodal LLM to Human-level AI: Modality, Instruction, Reasoning, Efficiency and beyond (2024)

Artificial intelligence (AI) encompasses knowledge acquisition and real-world grounding across various modalities. As a multidisciplinary research field, multimodal large language models (MLLMs) have recently garnered growing interest in both academia and industry, showing an unprecedented trend to achieve human-level AI via MLLMs. These large models offer an effective vehicle for understanding, reasoning, and planning by integrating and modeling diverse information modalities, including language, visual, auditory, and sensory data. This tutorial aims to deliver a comprehensive review of cutting-edge research in MLLMs, focusing on four key areas: MLLM architecture design, instructional learning, multimodal reasoning, and the efficiency of MLLMs. We will explore technical advancements, synthesize key challenges, and discuss potential avenues for future research.
Geo-Cultural Representation and Inclusion in Language Technologies (2024)

Training and evaluation of language models are increasingly relying on semi-structured data that is annotated by humans, along with techniques such as RLHF growing in usage across the board. As a result, both the data and the human perspectives involved in this process play a key role in what is taken as ground truth by our models. As annotation tasks are becoming increasingly more subjective and culturally complex, it is unclear how much of their socio-cultural identity annotators use to respond to tasks. We also currently do not have ways to integrate rich and diverse community perspectives into our language technologies. Accounting for such cross-cultural differences in interacting with technology is an increasingly crucial step for evaluating AI harms holistically. Without this, the state of the art of the AI models being deployed is at risk of causing unprecedented biases at a global scale. In this tutorial, we will take an interactive approach by utilizing some different types of annotation tasks to investigate together how our different socio-cultural perspectives and lived experiences influence what we consider as appropriate representations of global concepts.
Meaning Representations for Natural Languages: Design, Models and Applications (2024)

This tutorial reviews the design of common meaning representations, SoTA models for predicting meaning representations, and the applications of meaning representations in a wide range of downstream NLP tasks and real-world applications. Reporting by a diverse team of NLP researchers from academia and industry with extensive experience in designing, building and using meaning representations, our tutorial has three components: (1) an introduction to common meaning representations, including basic concepts and design challenges; (2) a review of SoTA methods on building models for meaning representations; and (3) an overview of applications of meaning representations in downstream NLP tasks and real-world applications. We propose a cutting-edge, full-day tutorial for all stakeholders in the AI community, including NLP researchers, domain-specific practitioners, and students
Navigating the Modern Evaluation Landscape: Considerations in Benchmarks and Frameworks for Large Language Models (LLMs) (2024)

General-Purpose Language Models have changed the world of Natural Language Processing, if not the world itself. The evaluation of such versatile models, while supposedly similar to evaluation of generation models before them, in fact presents a host of new evaluation challenges and opportunities. In this Tutorial, we will start from the building blocks of evaluation. The tutorial welcomes people from diverse backgrounds and assumes little familiarity with metrics, datasets, prompts and benchmarks. It will lay the foundations and explain the basics and their importance, while touching on the major points and breakthroughs of the recent era of evaluation. It will also compare traditional evaluation methods {--} which are still widely used {--} to newly developed methods. We will contrast new to old approaches, from evaluating on many-task benchmarks rather than on dedicated datasets to efficiency constraints, and from testing stability and prompts on in-context learning to using the models themselves as evaluation metrics. Finally, the tutorial will cover practical issues, ranging from reviewing widely-used benchmarks and prompt banks to efficient evaluation.
Mining, Assessing, and Improving Arguments in NLP and the Social Sciences (2024)

Computational argumentation is an interdisciplinary research field, connecting Natural Language Processing (NLP) to other disciplines such as the social sciences. The focus of recent research has concentrated on \textit{argument quality assessment}: what makes an argument good or bad? We present a tutorial which is an updated edition of the EACL 2023 tutorial presented by the same authors. As in the previous version, the tutorial will have a strong interdisciplinary and interactive nature, and will be structured along three main coordinates: (1) the notions of argument quality (AQ) across disciplines (how do we recognize good and bad arguments?), with a particular focus on the interface between Argument Mining (AM) and Deliberation Theory; (2) the modeling of subjectivity (who argues to whom; what are their beliefs?); and (3) the generation of improved arguments (what makes an argument better?). The tutorial will also touch upon a series of topics that are particularly relevant for the LREC-COLING audience (the issue of resource quality for the assessment of AQ; the interdisciplinary application of AM and AQ in a text-as-data approach to Political Science), in line with the developments in NLP (LLMs for AQ assessment), and relevant for the societal applications of AQ assessment (bias and debiasing). We will involve the participants in two annotation studies on the assessment and the improvement of quality.
Knowledge Editing for Large Language Models (2024)

Even with their impressive abilities, Large Language Models (LLMs) such as ChatGPT are not immune to issues of factual or logically consistent. Concretely, the key concern is how to seamlessly update those LLMs to correct mistakes without resorting to an exhaustive retraining or continuous training procedure, both of which can demand significant computational resources and time. Thus, the capability to edit LLMs offers an efficient solution to alter a model`s behavior, notably within a distinct area of interest, without negatively impacting its performance on other tasks. Through this tutorial, we strive to acquaint interested NLP researchers with recent and emerging techniques for editing LLMs. Specifically, we aim to present a systematic and current overview of cutting-edge methods, supplemented with practical tools, and unveil new research opportunities for our audiences. All the valuable resources can be accessed at https://github.com/zjunlp/KnowledgeEditingPapers.
The DBpedia Databus Tutorial: Increase the Visibility and Usability of Your Data (2024)

This tutorial introduces DBpedia Databus (https://databus.dbpedia.org), a FAIR data publishing platform, to address challenges faced by data producers and consumers. It covers data organization, publishing, and consumption on the DBpedia Databus, with an exclusive focus on Linguistic Knowledge Graphs. The tutorial offers practical insights for knowledge graph stakeholders, aiding data integration and accessibility in the Linked Open Data community. Designed for a diverse audience, it fosters hands-on learning to familiarize participants with the DBpedia Databus technology.