title stringlengths 18 149 | paper_url stringlengths 43 46 | authors listlengths 1 27 | abstract large_stringlengths 501 2.03k | anthology_id stringlengths 17 20 | doi stringlengths 29 32 ⌀ | award stringclasses 0
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IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions | https://aclanthology.org/2023.emnlp-main.1/ | [
"Zhebin Zhang",
"Xinyu Zhang",
"Yuanhang Ren",
"Saijiang Shi",
"Meng Han",
"Yongkang Wu",
"Ruofei Lai",
"Zhao Cao"
] | Retrieval-Augmented Generation (RAG), by incorporating external knowledge with parametric memory of language models, has become the state-of-the-art architecture for open-domain QA tasks. However, common knowledge bases are inherently constrained by limited coverage and noisy information, making retrieval-based approac... | 2023.emnlp-main.1 | 10.18653/v1/2023.emnlp-main.1 | null | 2311.18397 | title_snapshot | [
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Absolute Position Embedding Learns Sinusoid-like Waves for Attention Based on Relative Position | https://aclanthology.org/2023.emnlp-main.2/ | [
"Yuji Yamamoto",
"Takuya Matsuzaki"
] | Attention weight is a clue to interpret how a Transformer-based model makes an inference. In some attention heads, the attention focuses on the neighbors of each token. This allows the output vector of each token to depend on the surrounding tokens and contributes to make the inference context-dependent. We analyze the... | 2023.emnlp-main.2 | 10.18653/v1/2023.emnlp-main.2 | null | null | null | [
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Chinese Lexical Substitution: Dataset and Method | https://aclanthology.org/2023.emnlp-main.3/ | [
"Jipeng Qiang",
"Kang Liu",
"Ying Li",
"Yun Li",
"Yi Zhu",
"Yun-Hao Yuan",
"Xiaocheng Hu",
"Xiaoye Ouyang"
] | Existing lexical substitution (LS) benchmarks were collected by asking human annotators to think of substitutes from memory, resulting in benchmarks with limited coverage and relatively small scales. To overcome this problem, we propose a novel annotation method to construct an LS dataset based on human and machine col... | 2023.emnlp-main.3 | 10.18653/v1/2023.emnlp-main.3 | null | null | null | [
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Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting | https://aclanthology.org/2023.emnlp-main.4/ | [
"Chenkai Sun",
"Jinning Li",
"Yi Fung",
"Hou Chan",
"Tarek Abdelzaher",
"ChengXiang Zhai",
"Heng Ji"
] | Automatic response forecasting for news media plays a crucial role in enabling content producers to efficiently predict the impact of news releases and prevent unexpected negative outcomes such as social conflict and moral injury. To effectively forecast responses, it is essential to develop measures that leverage the ... | 2023.emnlp-main.4 | 10.18653/v1/2023.emnlp-main.4 | null | 2310.13297 | title_snapshot | [
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Fine-grained Conversational Decoding via Isotropic and Proximal Search | https://aclanthology.org/2023.emnlp-main.5/ | [
"Yuxuan Yao",
"Han Wu",
"Qiling Xu",
"Linqi Song"
] | General-purpose text decoding approaches are usually adopted for dialogue response generation. Although the quality of the generated responses can be improved with dialogue-specific encoding methods, conversational decoding methods are still under-explored. Inspired by SimDRC that a good dialogue feature space should f... | 2023.emnlp-main.5 | 10.18653/v1/2023.emnlp-main.5 | null | 2310.08130 | title_snapshot | [
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Holistic Inter-Annotator Agreement and Corpus Coherence Estimation in a Large-scale Multilingual Annotation Campaign | https://aclanthology.org/2023.emnlp-main.6/ | [
"Nicolas Stefanovitch",
"Jakub Piskorski"
] | In this paper we report on the complexity of persuasion technique annotation in the context of a large multilingual annotation campaign involving 6 languages and approximately 40 annotators. We highlight the techniques that appear to be difficult for humans to annotate and elaborate on our findings on the causes of thi... | 2023.emnlp-main.6 | 10.18653/v1/2023.emnlp-main.6 | null | null | null | [
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PHD: Pixel-Based Language Modeling of Historical Documents | https://aclanthology.org/2023.emnlp-main.7/ | [
"Nadav Borenstein",
"Phillip Rust",
"Desmond Elliott",
"Isabelle Augenstein"
] | The digitisation of historical documents has provided historians with unprecedented research opportunities. Yet, the conventional approach to analysing historical documents involves converting them from images to text using OCR, a process that overlooks the potential benefits of treating them as images and introduces h... | 2023.emnlp-main.7 | 10.18653/v1/2023.emnlp-main.7 | null | 2310.18343 | title_snapshot | [
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Primacy Effect of ChatGPT | https://aclanthology.org/2023.emnlp-main.8/ | [
"Yiwei Wang",
"Yujun Cai",
"Muhao Chen",
"Yuxuan Liang",
"Bryan Hooi"
] | Instruction-tuned large language models (LLMs), such as ChatGPT, have led to promising zero-shot performance in discriminative natural language understanding (NLU) tasks. This involves querying the LLM using a prompt containing the question, and the candidate labels to choose from. The question-answering capabilities o... | 2023.emnlp-main.8 | 10.18653/v1/2023.emnlp-main.8 | null | 2310.13206 | title_snapshot | [
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Evaluating the Rationale Understanding of Critical Reasoning in Logical Reading Comprehension | https://aclanthology.org/2023.emnlp-main.9/ | [
"Akira Kawabata",
"Saku Sugawara"
] | To precisely evaluate a language model’s capability for logical reading comprehension, we present a dataset for testing the understanding of the rationale behind critical reasoning. For questions taken from an existing multiple-choice logical reading comprehension dataset, we crowdsource rationale texts that explain wh... | 2023.emnlp-main.9 | 10.18653/v1/2023.emnlp-main.9 | null | 2311.18353 | title_snapshot | [
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Evaluating and Modeling Attribution for Cross-Lingual Question Answering | https://aclanthology.org/2023.emnlp-main.10/ | [
"Benjamin Muller",
"John Wieting",
"Jonathan H. Clark",
"Tom Kwiatkowski",
"Sebastian Ruder",
"Livio Baldini Soares",
"Roee Aharoni",
"Jonathan Herzig",
"Xinyi Wang"
] | Trustworthy answer content is abundant in many high-resource languages and is instantly accessible through question answering systems — yet this content can be hard to access for those that do not speak these languages. The leap forward in cross-lingual modeling quality offered by generative language models offers much... | 2023.emnlp-main.10 | 10.18653/v1/2023.emnlp-main.10 | null | 2305.14332 | title_snapshot | [
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Better Quality Pre-training Data and T5 Models for African Languages | https://aclanthology.org/2023.emnlp-main.11/ | [
"Akintunde Oladipo",
"Mofetoluwa Adeyemi",
"Orevaoghene Ahia",
"Abraham Toluwalase Owodunni",
"Odunayo Ogundepo",
"David Ifeoluwa Adelani",
"Jimmy Lin"
] | In this study, we highlight the importance of enhancing the quality of pretraining data in multilingual language models. Existing web crawls have demonstrated quality issues, particularly in the context of low-resource languages. Consequently, we introduce a new multilingual pretraining corpus for 16 African languages,... | 2023.emnlp-main.11 | 10.18653/v1/2023.emnlp-main.11 | null | null | null | [
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Sparse Universal Transformer | https://aclanthology.org/2023.emnlp-main.12/ | [
"Shawn Tan",
"Yikang Shen",
"Zhenfang Chen",
"Aaron Courville",
"Chuang Gan"
] | The Universal Transformer (UT) is a variant of the Transformer that shares parameters across its layers and is Turing-complete under certain assumptions. Empirical evidence also shows that UTs have better compositional generalization than Vanilla Transformers (VTs) in formal language tasks. The parameter-sharing also a... | 2023.emnlp-main.12 | 10.18653/v1/2023.emnlp-main.12 | null | 2310.07096 | title_snapshot | [
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Theory of Mind for Multi-Agent Collaboration via Large Language Models | https://aclanthology.org/2023.emnlp-main.13/ | [
"Huao Li",
"Yu Chong",
"Simon Stepputtis",
"Joseph Campbell",
"Dana Hughes",
"Charles Lewis",
"Katia Sycara"
] | While Large Language Models (LLMs) have demonstrated impressive accomplishments in both reasoning and planning, their abilities in multi-agent collaborations remains largely unexplored. This study evaluates LLM-based agents in a multi-agent cooperative text game with Theory of Mind (ToM) inference tasks, comparing thei... | 2023.emnlp-main.13 | 10.18653/v1/2023.emnlp-main.13 | null | 2310.10701 | title_snapshot | [
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Establishing Trustworthiness: Rethinking Tasks and Model Evaluation | https://aclanthology.org/2023.emnlp-main.14/ | [
"Robert Litschko",
"Max Müller-Eberstein",
"Rob van der Goot",
"Leon Weber-Genzel",
"Barbara Plank"
] | Language understanding is a multi-faceted cognitive capability, which the Natural Language Processing (NLP) community has striven to model computationally for decades. Traditionally, facets of linguistic intelligence have been compartmentalized into tasks with specialized model architectures and corresponding evaluatio... | 2023.emnlp-main.14 | 10.18653/v1/2023.emnlp-main.14 | null | 2310.05442 | title_snapshot | [
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Let’s Think Frame by Frame with VIP: A Video Infilling and Prediction Dataset for Evaluating Video Chain-of-Thought | https://aclanthology.org/2023.emnlp-main.15/ | [
"Vaishnavi Himakunthala",
"Andy Ouyang",
"Daniel Rose",
"Ryan He",
"Alex Mei",
"Yujie Lu",
"Chinmay Sonar",
"Michael Saxon",
"William Wang"
] | Despite exciting recent results showing vision-language systems’ capacity to reason about images using natural language, their capacity for video reasoning remains underexplored. We motivate framing video reasoning as the sequential understanding of a small number of keyframes, thereby leveraging the power and robustne... | 2023.emnlp-main.15 | 10.18653/v1/2023.emnlp-main.15 | null | 2305.13903 | title_snapshot | [
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GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP | https://aclanthology.org/2023.emnlp-main.16/ | [
"Md Tawkat Islam Khondaker",
"Abdul Waheed",
"El Moatez Billah Nagoudi",
"Muhammad Abdul-Mageed"
] | ChatGPT’s emergence heralds a transformative phase in NLP, particularly demonstrated through its excellent performance on many English benchmarks. However, the model’s efficacy across diverse linguistic contexts remains largely uncharted territory. This work aims to bridge this knowledge gap, with a primary focus on as... | 2023.emnlp-main.16 | 10.18653/v1/2023.emnlp-main.16 | null | 2305.14976 | title_snapshot | [
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Dual-Channel Span for Aspect Sentiment Triplet Extraction | https://aclanthology.org/2023.emnlp-main.17/ | [
"Pan Li",
"Ping Li",
"Kai Zhang"
] | Aspect Sentiment Triplet Extraction (ASTE) is one of the compound tasks of fine-grained aspect-based sentiment analysis (ABSA), aiming at extracting the triplets of aspect terms, corresponding opinion terms and the associated sentiment orientation. Recent efforts in exploiting span-level semantic interaction shown supe... | 2023.emnlp-main.17 | 10.18653/v1/2023.emnlp-main.17 | null | null | null | [
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Cultural Concept Adaptation on Multimodal Reasoning | https://aclanthology.org/2023.emnlp-main.18/ | [
"Zhi Li",
"Yin Zhang"
] | Developing cultural adaptation methods is important, which can improve the model performance on the low-resource ones and provide more equitable opportunities for everyone to benefit from advanced technology. Past methods primarily focused on multilingual and multimodal capabilities, and the improvement of multicultura... | 2023.emnlp-main.18 | 10.18653/v1/2023.emnlp-main.18 | null | null | null | [
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Understanding Compositional Data Augmentation in Typologically Diverse Morphological Inflection | https://aclanthology.org/2023.emnlp-main.19/ | [
"Farhan Samir",
"Miikka Silfverberg"
] | Data augmentation techniques are widely used in low-resource automatic morphological inflection to address the issue of data sparsity. However, the full implications of these techniques remain poorly understood. In this study, we aim to shed light on the theoretical aspects of the data augmentation strategy StemCorrupt... | 2023.emnlp-main.19 | 10.18653/v1/2023.emnlp-main.19 | null | 2305.13658 | title_snapshot | [
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Evaluating Object Hallucination in Large Vision-Language Models | https://aclanthology.org/2023.emnlp-main.20/ | [
"Yifan Li",
"Yifan Du",
"Kun Zhou",
"Jinpeng Wang",
"Xin Zhao",
"Ji-Rong Wen"
] | Inspired by the superior language abilities of large language models (LLM), large vision-language models (LVLM) have been recently proposed by integrating powerful LLMs for improving the performance on complex multimodal tasks. Despite the promising progress on LVLMs, we find that they suffer from object hallucinations... | 2023.emnlp-main.20 | 10.18653/v1/2023.emnlp-main.20 | null | 2305.10355 | title_snapshot | [
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Event Ontology Completion with Hierarchical Structure Evolution Networks | https://aclanthology.org/2023.emnlp-main.21/ | [
"Pengfei Cao",
"Yupu Hao",
"Yubo Chen",
"Kang Liu",
"Jiexin Xu",
"Huaijun Li",
"Xiaojian Jiang",
"Jun Zhao"
] | Traditional event detection methods require predefined event schemas. However, manually defining event schemas is expensive and the coverage of schemas is limited. To this end, some works study the event type induction (ETI) task, which discovers new event types via clustering. However, the setting of ETI suffers from ... | 2023.emnlp-main.21 | 10.18653/v1/2023.emnlp-main.21 | null | null | null | [
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Parameter-efficient Tuning for Large Language Model without Calculating Its Gradients | https://aclanthology.org/2023.emnlp-main.22/ | [
"Feihu Jin",
"Jiajun Zhang",
"Chengqing Zong"
] | Fine-tuning all parameters of large language models (LLMs) requires significant computational resources and is time-consuming. Recent parameter-efficient tuning methods such as Adapter tuning, Prefix tuning, and LoRA allow for updating a small subset of parameters in large language models. However, they can only save a... | 2023.emnlp-main.22 | 10.18653/v1/2023.emnlp-main.22 | null | null | null | [
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Discourse Structures Guided Fine-grained Propaganda Identification | https://aclanthology.org/2023.emnlp-main.23/ | [
"Yuanyuan Lei",
"Ruihong Huang"
] | Propaganda is a form of deceptive narratives that instigate or mislead the public, usually with a political purpose. In this paper, we aim to identify propaganda in political news at two fine-grained levels: sentence-level and token-level. We observe that propaganda content is more likely to be embedded in sentences th... | 2023.emnlp-main.23 | 10.18653/v1/2023.emnlp-main.23 | null | 2310.18544 | title_snapshot | [
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CompoundPiece: Evaluating and Improving Decompounding Performance of Language Models | https://aclanthology.org/2023.emnlp-main.24/ | [
"Benjamin Minixhofer",
"Jonas Pfeiffer",
"Ivan Vulić"
] | While many languages possess processes of joining two or more words to create compound words, previous studies have been typically limited only to languages with excessively productive compound formation (e.g., German, Dutch) and there is no public dataset containing compound and non-compound words across a large numbe... | 2023.emnlp-main.24 | 10.18653/v1/2023.emnlp-main.24 | null | 2305.14214 | title_snapshot | [
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Improving Image Captioning via Predicting Structured Concepts | https://aclanthology.org/2023.emnlp-main.25/ | [
"Ting Wang",
"Weidong Chen",
"Yuanhe Tian",
"Yan Song",
"Zhendong Mao"
] | Having the difficulty of solving the semantic gap between images and texts for the image captioning task, conventional studies in this area paid some attention to treating semantic concepts as a bridge between the two modalities and improved captioning performance accordingly. Although promising results on concept pred... | 2023.emnlp-main.25 | 10.18653/v1/2023.emnlp-main.25 | null | 2311.08223 | title_snapshot | [
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GATITOS: Using a New Multilingual Lexicon for Low-resource Machine Translation | https://aclanthology.org/2023.emnlp-main.26/ | [
"Alexander Jones",
"Isaac Caswell",
"Orhan Firat",
"Ishank Saxena"
] | Modern machine translation models and language models are able to translate without having been trained on parallel data, greatly expanding the set of languages that they can serve. However, these models still struggle in a variety of predictable ways, a problem that cannot be overcome without at least some trusted bil... | 2023.emnlp-main.26 | 10.18653/v1/2023.emnlp-main.26 | null | null | null | [
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Continually Improving Extractive QA via Human Feedback | https://aclanthology.org/2023.emnlp-main.27/ | [
"Ge Gao",
"Hung-Ting Chen",
"Yoav Artzi",
"Eunsol Choi"
] | We study continually improving an extractive question answering (QA) system via human user feedback. We design and deploy an iterative approach, where information-seeking users ask questions, receive model-predicted answers, and provide feedback. We conduct experiments involving thousands of user interactions under div... | 2023.emnlp-main.27 | 10.18653/v1/2023.emnlp-main.27 | null | 2305.12473 | title_snapshot | [
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Using Interpretation Methods for Model Enhancement | https://aclanthology.org/2023.emnlp-main.28/ | [
"Zhuo Chen",
"Chengyue Jiang",
"Kewei Tu"
] | In the age of neural natural language processing, there are plenty of works trying to derive interpretations of neural models. Intuitively, when gold rationales exist during training, one can additionally train the model to match its interpretation with the rationales. However, this intuitive idea has not been fully ex... | 2023.emnlp-main.28 | 10.18653/v1/2023.emnlp-main.28 | null | 2404.02068 | title_snapshot | [
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An Expression Tree Decoding Strategy for Mathematical Equation Generation | https://aclanthology.org/2023.emnlp-main.29/ | [
"Wenqi Zhang",
"Yongliang Shen",
"Qingpeng Nong",
"Zeqi Tan",
"Yanna Ma",
"Weiming Lu"
] | Generating mathematical equations from natural language requires an accurate understanding of the relations among math expressions. Existing approaches can be broadly categorized into token-level and expression-level generation. The former treats equations as a mathematical language, sequentially generating math tokens... | 2023.emnlp-main.29 | 10.18653/v1/2023.emnlp-main.29 | null | 2310.09619 | title_snapshot | [
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Bootstrapping Small & High Performance Language Models with Unmasking-Removal Training Policy | https://aclanthology.org/2023.emnlp-main.30/ | [
"Yahan Yang",
"Elior Sulem",
"Insup Lee",
"Dan Roth"
] | BabyBERTa, a language model trained on small-scale child-directed speech while none of the words are unmasked during training, has been shown to achieve a level of grammaticality comparable to that of RoBERTa-base, which is trained on 6,000 times more words and 15 times more parameters. Relying on this promising result... | 2023.emnlp-main.30 | 10.18653/v1/2023.emnlp-main.30 | null | null | null | [
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Diversity Enhanced Narrative Question Generation for Storybooks | https://aclanthology.org/2023.emnlp-main.31/ | [
"Hokeun Yoon",
"JinYeong Bak"
] | Question generation (QG) from a given context can enhance comprehension, engagement, assessment, and overall efficacy in learning or conversational environments. Despite recent advancements in QG, the challenge of enhancing or measuring the diversity of generated questions often remains unaddressed. In this paper, we i... | 2023.emnlp-main.31 | 10.18653/v1/2023.emnlp-main.31 | null | 2310.16446 | title_snapshot | [
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Debiasing Made State-of-the-art: Revisiting the Simple Seed-based Weak Supervision for Text Classification | https://aclanthology.org/2023.emnlp-main.32/ | [
"Chengyu Dong",
"Zihan Wang",
"Jingbo Shang"
] | Recent advances in weakly supervised text classification mostly focus on designing sophisticated methods to turn high-level human heuristics into quality pseudo-labels. In this paper, we revisit the seed matching-based method, which is arguably the simplest way to generate pseudo-labels, and show that its power was gre... | 2023.emnlp-main.32 | 10.18653/v1/2023.emnlp-main.32 | null | 2305.14794 | title_snapshot | [
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How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoning | https://aclanthology.org/2023.emnlp-main.33/ | [
"Hang Chen",
"Xinyu Yang",
"Jing Luo",
"Wenjing Zhu"
] | Our investigation into the Affective Reasoning in Conversation (ARC) task highlights the challenge of causal discrimination. Almost all existing models, including large language models (LLMs), excel at capturing semantic correlations within utterance embeddings but fall short in determining the specific causal relation... | 2023.emnlp-main.33 | 10.18653/v1/2023.emnlp-main.33 | null | 2305.02615 | title_snapshot | [
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Compressing and Debiasing Vision-Language Pre-Trained Models for Visual Question Answering | https://aclanthology.org/2023.emnlp-main.34/ | [
"Qingyi Si",
"Yuanxin Liu",
"Zheng Lin",
"Peng Fu",
"Yanan Cao",
"Weiping Wang"
] | Despite the excellent performance of vision-language pre-trained models (VLPs) on conventional VQA task, they still suffer from two problems: First, VLPs tend to rely on language biases in datasets and fail to generalize to out-of-distribution (OOD) data. Second, they are inefficient in terms of memory footprint and co... | 2023.emnlp-main.34 | 10.18653/v1/2023.emnlp-main.34 | null | 2210.14558 | title_snapshot | [
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Selectively Answering Ambiguous Questions | https://aclanthology.org/2023.emnlp-main.35/ | [
"Jeremy Cole",
"Michael Zhang",
"Daniel Gillick",
"Julian Eisenschlos",
"Bhuwan Dhingra",
"Jacob Eisenstein"
] | Trustworthy language models should abstain from answering questions when they do not know the answer. However, the answer to a question can be unknown for a variety of reasons. Prior research has focused on the case in which the question is clear and the answer is unambiguous but possibly unknown. However, the answer t... | 2023.emnlp-main.35 | 10.18653/v1/2023.emnlp-main.35 | null | 2305.14613 | title_snapshot | [
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Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning | https://aclanthology.org/2023.emnlp-main.36/ | [
"Dong-Ho Lee",
"Kian Ahrabian",
"Woojeong Jin",
"Fred Morstatter",
"Jay Pujara"
] | Temporal knowledge graph (TKG) forecasting benchmarks challenge models to predict future facts using knowledge of past facts. In this paper, we develop an approach to use in-context learning (ICL) with large language models (LLMs) for TKG forecasting. Our extensive evaluation compares diverse baselines, including both ... | 2023.emnlp-main.36 | 10.18653/v1/2023.emnlp-main.36 | null | 2305.10613 | title_snapshot | [
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Knowledge Graph Compression Enhances Diverse Commonsense Generation | https://aclanthology.org/2023.emnlp-main.37/ | [
"EunJeong Hwang",
"Veronika Thost",
"Vered Shwartz",
"Tengfei Ma"
] | Generating commonsense explanations requires reasoning about commonsense knowledge beyond what is explicitly mentioned in the context. Existing models use commonsense knowledge graphs such as ConceptNet to extract a subgraph of relevant knowledge pertaining to concepts in the input. However, due to the large coverage a... | 2023.emnlp-main.37 | 10.18653/v1/2023.emnlp-main.37 | null | null | null | [
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Pragmatic Reasoning Unlocks Quantifier Semantics for Foundation Models | https://aclanthology.org/2023.emnlp-main.38/ | [
"Yiyuan Li",
"Rakesh Menon",
"Sayan Ghosh",
"Shashank Srivastava"
] | Generalized quantifiers (e.g., \textit{few}, \textit{most}) are used to indicate the proportions predicates satisfy (for example, \textit{some} apples are red). One way to interpret quantifier semantics is to explicitly bind these satisfactions with percentage scopes (e.g., 30%-40% of apples are red). This approach can... | 2023.emnlp-main.38 | 10.18653/v1/2023.emnlp-main.38 | null | 2311.04659 | title_snapshot | [
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LLM-FP4: 4-Bit Floating-Point Quantized Transformers | https://aclanthology.org/2023.emnlp-main.39/ | [
"Shih-yang Liu",
"Zechun Liu",
"Xijie Huang",
"Pingcheng Dong",
"Kwang-Ting Cheng"
] | We propose LLM-FP4 for quantizing both weights and activations in large language models (LLMs) down to 4-bit floating-point values, in a post-training manner. Existing post-training quantization (PTQ) solutions are primarily integer-based and struggle with bit widths below 8 bits. Compared to integer quantization, floa... | 2023.emnlp-main.39 | 10.18653/v1/2023.emnlp-main.39 | null | 2310.16836 | title_snapshot | [
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Improving Biomedical Abstractive Summarisation with Knowledge Aggregation from Citation Papers | https://aclanthology.org/2023.emnlp-main.40/ | [
"Chen Tang",
"Shun Wang",
"Tomas Goldsack",
"Chenghua Lin"
] | Abstracts derived from biomedical literature possess distinct domain-specific characteristics, including specialised writing styles and biomedical terminologies, which necessitate a deep understanding of the related literature. As a result, existing language models struggle to generate technical summaries that are on p... | 2023.emnlp-main.40 | 10.18653/v1/2023.emnlp-main.40 | null | 2310.15684 | title_snapshot | [
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Explanation Selection Using Unlabeled Data for Chain-of-Thought Prompting | https://aclanthology.org/2023.emnlp-main.41/ | [
"Xi Ye",
"Greg Durrett"
] | Recent work has shown how to prompt large language models with explanations to obtain strong performance on textual reasoning tasks, i.e., the chain-of-thought paradigm. However, subtly different explanations can yield widely varying downstream task accuracy. Explanations that have not been “tuned” for a task, such as ... | 2023.emnlp-main.41 | 10.18653/v1/2023.emnlp-main.41 | null | 2302.04813 | title_snapshot | [
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HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine Translation | https://aclanthology.org/2023.emnlp-main.42/ | [
"David Dale",
"Elena Voita",
"Janice Lam",
"Prangthip Hansanti",
"Christophe Ropers",
"Elahe Kalbassi",
"Cynthia Gao",
"Loïc Barrault",
"Marta R. Costa-jussà"
] | Hallucinations in machine translation are translations that contain information completely unrelated to the input. Omissions are translations that do not include some of the input information. While both cases tend to be catastrophic errors undermining user trust, annotated data with these types of pathologies is extre... | 2023.emnlp-main.42 | 10.18653/v1/2023.emnlp-main.42 | null | 2305.11746 | title_snapshot | [
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Gradient-based Gradual Pruning for Language-Specific Multilingual Neural Machine Translation | https://aclanthology.org/2023.emnlp-main.43/ | [
"Dan He",
"Minh-Quang Pham",
"Thanh-Le Ha",
"Marco Turchi"
] | Multilingual neural machine translation (MNMT) offers the convenience of translating between multiple languages with a single model. However, MNMT often suffers from performance degradation in high-resource languages compared to bilingual counterparts. This degradation is commonly attributed to parameter interference, ... | 2023.emnlp-main.43 | 10.18653/v1/2023.emnlp-main.43 | null | null | null | [
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LLM-powered Data Augmentation for Enhanced Cross-lingual Performance | https://aclanthology.org/2023.emnlp-main.44/ | [
"Chenxi Whitehouse",
"Monojit Choudhury",
"Alham Fikri Aji"
] | This paper explores the potential of leveraging Large Language Models (LLMs) for data augmentation in multilingual commonsense reasoning datasets where the available training data is extremely limited. To achieve this, we utilise several LLMs, namely Dolly-v2, StableVicuna, ChatGPT, and GPT-4, to augment three datasets... | 2023.emnlp-main.44 | 10.18653/v1/2023.emnlp-main.44 | null | 2305.14288 | title_snapshot | [
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Prompt-based Logical Semantics Enhancement for Implicit Discourse Relation Recognition | https://aclanthology.org/2023.emnlp-main.45/ | [
"Chenxu Wang",
"Ping Jian",
"Mu Huang"
] | Implicit Discourse Relation Recognition (IDRR), which infers discourse relations without the help of explicit connectives, is still a crucial and challenging task for discourse parsing. Recent works tend to exploit the hierarchical structure information from the annotated senses, which demonstrate enhanced discourse re... | 2023.emnlp-main.45 | 10.18653/v1/2023.emnlp-main.45 | null | 2311.00367 | title_snapshot | [
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VLIS: Unimodal Language Models Guide Multimodal Language Generation | https://aclanthology.org/2023.emnlp-main.46/ | [
"Jiwan Chung",
"Youngjae Yu"
] | Multimodal language generation, which leverages the synergy of language and vision, is a rapidly expanding field. However, existing vision-language models face challenges in tasks that require complex linguistic understanding. To address this issue, we introduce Visual-Language models as Importance Sampling weights (VL... | 2023.emnlp-main.46 | 10.18653/v1/2023.emnlp-main.46 | null | 2310.09767 | title_snapshot | [
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Conceptual structure coheres in human cognition but not in large language models | https://aclanthology.org/2023.emnlp-main.47/ | [
"Siddharth Suresh",
"Kushin Mukherjee",
"Xizheng Yu",
"Wei-Chun Huang",
"Lisa Padua",
"Timothy Rogers"
] | 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 ... | 2023.emnlp-main.47 | 10.18653/v1/2023.emnlp-main.47 | null | 2304.02754 | title_snapshot | [
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Towards LLM-driven Dialogue State Tracking | https://aclanthology.org/2023.emnlp-main.48/ | [
"Yujie Feng",
"Zexin Lu",
"Bo Liu",
"Liming Zhan",
"Xiao-Ming Wu"
] | Dialogue State Tracking (DST) is of paramount importance in ensuring accurate tracking of user goals and system actions within task-oriented dialogue systems. The emergence of large language models (LLMs) such as GPT3 and ChatGPT has sparked considerable interest in assessing their efficacy across diverse applications.... | 2023.emnlp-main.48 | 10.18653/v1/2023.emnlp-main.48 | null | 2310.14970 | title_snapshot | [
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Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis | https://aclanthology.org/2023.emnlp-main.49/ | [
"Haoyu Zhang",
"Yu Wang",
"Guanghao Yin",
"Kejun Liu",
"Yuanyuan Liu",
"Tianshu Yu"
] | Though Multimodal Sentiment Analysis (MSA) proves effective by utilizing rich information from multiple sources (*e.g.,* language, video, and audio), the potential sentiment-irrelevant and conflicting information across modalities may hinder the performance from being further improved. To alleviate this, we present Ada... | 2023.emnlp-main.49 | 10.18653/v1/2023.emnlp-main.49 | null | 2310.05804 | title_snapshot | [
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Multitask Multimodal Prompted Training for Interactive Embodied Task Completion | https://aclanthology.org/2023.emnlp-main.50/ | [
"Georgios Pantazopoulos",
"Malvina Nikandrou",
"Amit Parekh",
"Bhathiya Hemanthage",
"Arash Eshghi",
"Ioannis Konstas",
"Verena Rieser",
"Oliver Lemon",
"Alessandro Suglia"
] | Interactive and embodied tasks pose at least two fundamental challenges to existing Vision & Language (VL) models, including 1) grounding language in trajectories of actions and observations, and 2) referential disambiguation. To tackle these challenges, we propose an Embodied MultiModal Agent (EMMA): a unified encoder... | 2023.emnlp-main.50 | 10.18653/v1/2023.emnlp-main.50 | null | 2311.04067 | title_snapshot | [
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We’re Afraid Language Models Aren’t Modeling Ambiguity | https://aclanthology.org/2023.emnlp-main.51/ | [
"Alisa Liu",
"Zhaofeng Wu",
"Julian Michael",
"Alane Suhr",
"Peter West",
"Alexander Koller",
"Swabha Swayamdipta",
"Noah Smith",
"Yejin Choi"
] | Ambiguity is an intrinsic feature of natural language. Managing ambiguity is a key part of human language understanding, allowing us to anticipate misunderstanding as communicators and revise our interpretations as listeners. As language models are increasingly employed as dialogue interfaces and writing aids, handling... | 2023.emnlp-main.51 | 10.18653/v1/2023.emnlp-main.51 | null | 2304.14399 | title_snapshot | [
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Linear-Time Modeling of Linguistic Structure: An Order-Theoretic Perspective | https://aclanthology.org/2023.emnlp-main.52/ | [
"Tianyu Liu",
"Afra Amini",
"Mrinmaya Sachan",
"Ryan Cotterell"
] | Tasks that model the relation between pairs of tokens in a string are a vital part of understanding natural language. Such tasks, in general, require exhaustive pair-wise comparisons of tokens, thus having a quadratic runtime complexity in the length of the string. We show that these exhaustive comparisons can be avoid... | 2023.emnlp-main.52 | 10.18653/v1/2023.emnlp-main.52 | null | 2305.15057 | title_snapshot | [
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GEMINI: Controlling The Sentence-Level Summary Style in Abstractive Text Summarization | https://aclanthology.org/2023.emnlp-main.53/ | [
"Guangsheng Bao",
"Zebin Ou",
"Yue Zhang"
] | Human experts write summaries using different techniques, including extracting a sentence from the document and rewriting it, or fusing various information from the document to abstract it. These techniques are flexible and thus difficult to be imitated by any single method. To address this issue, we propose an adaptiv... | 2023.emnlp-main.53 | 10.18653/v1/2023.emnlp-main.53 | null | 2304.03548 | title_judge | [
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Fidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text Generation | https://aclanthology.org/2023.emnlp-main.54/ | [
"Wei-Lin Chen",
"Cheng-Kuang Wu",
"Hsin-Hsi Chen",
"Chung-Chi Chen"
] | In this paper, we address the hallucination problem commonly found in natural language generation tasks. Language models often generate fluent and convincing content but can lack consistency with the provided source, resulting in potential inaccuracies. We propose a new decoding method called Fidelity-Enriched Contrast... | 2023.emnlp-main.54 | 10.18653/v1/2023.emnlp-main.54 | null | 2310.14981 | title_snapshot | [
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Analyzing Norm Violations in Live-Stream Chat | https://aclanthology.org/2023.emnlp-main.55/ | [
"Jihyung Moon",
"Dong-Ho Lee",
"Hyundong Cho",
"Woojeong Jin",
"Chan Park",
"Minwoo Kim",
"Jonathan May",
"Jay Pujara",
"Sungjoon Park"
] | Toxic language, such as hate speech, can deter users from participating in online communities and enjoying popular platforms. Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and Twitter. These approa... | 2023.emnlp-main.55 | 10.18653/v1/2023.emnlp-main.55 | null | 2305.10731 | title_snapshot | [
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Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality | https://aclanthology.org/2023.emnlp-main.56/ | [
"Harman Singh",
"Pengchuan Zhang",
"Qifan Wang",
"Mengjiao Wang",
"Wenhan Xiong",
"Jingfei Du",
"Yu Chen"
] | Contrastively trained vision-language models have achieved remarkable progress in vision and language representation learning. However, recent research has highlighted severe limitations of these models in their ability to perform compositional reasoning over objects, attributes, and relations. Scene graphs have emerge... | 2023.emnlp-main.56 | 10.18653/v1/2023.emnlp-main.56 | null | 2305.13812 | title_snapshot | [
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Reading Books is Great, But Not if You Are Driving! Visually Grounded Reasoning about Defeasible Commonsense Norms | https://aclanthology.org/2023.emnlp-main.57/ | [
"Seungju Han",
"Junhyeok Kim",
"Jack Hessel",
"Liwei Jiang",
"Jiwan Chung",
"Yejin Son",
"Yejin Choi",
"Youngjae Yu"
] | Commonsense norms are defeasible by context: reading books is usually great, but not when driving a car. While contexts can be explicitly described in language, in embodied scenarios, contexts are often provided visually. This type of visually grounded reasoning about defeasible commonsense norms is generally easy for ... | 2023.emnlp-main.57 | 10.18653/v1/2023.emnlp-main.57 | null | 2310.10418 | title_snapshot | [
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Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus | https://aclanthology.org/2023.emnlp-main.58/ | [
"Tianhang Zhang",
"Lin Qiu",
"Qipeng Guo",
"Cheng Deng",
"Yue Zhang",
"Zheng Zhang",
"Chenghu Zhou",
"Xinbing Wang",
"Luoyi Fu"
] | Large Language Models (LLMs) have gained significant popularity for their impressive performance across diverse fields. However, LLMs are prone to hallucinate untruthful or nonsensical outputs that fail to meet user expectations in many real-world applications. Existing works for detecting hallucinations in LLMs either... | 2023.emnlp-main.58 | 10.18653/v1/2023.emnlp-main.58 | null | 2311.13230 | title_snapshot | [
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FactKB: Generalizable Factuality Evaluation using Language Models Enhanced with Factual Knowledge | https://aclanthology.org/2023.emnlp-main.59/ | [
"Shangbin Feng",
"Vidhisha Balachandran",
"Yuyang Bai",
"Yulia Tsvetkov"
] | Evaluating the factual consistency of automatically generated summaries is essential for the progress and adoption of reliable summarization systems. Despite recent advances, existing factuality evaluation models are not robust, being especially prone to entity and relation errors in new domains. We propose FactKB—a si... | 2023.emnlp-main.59 | 10.18653/v1/2023.emnlp-main.59 | null | 2305.08281 | title_snapshot | [
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Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation | https://aclanthology.org/2023.emnlp-main.60/ | [
"Xuanli He",
"Qiongkai Xu",
"Jun Wang",
"Benjamin Rubinstein",
"Trevor Cohn"
] | Modern NLP models are often trained over large untrusted datasets, raising the potential for a malicious adversary to compromise model behaviour. For instance, backdoors can be implanted through crafting training instances with a specific textual trigger and a target label. This paper posits that backdoor poisoning att... | 2023.emnlp-main.60 | 10.18653/v1/2023.emnlp-main.60 | null | 2305.11596 | title_snapshot | [
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Symbol tuning improves in-context learning in language models | https://aclanthology.org/2023.emnlp-main.61/ | [
"Jerry Wei",
"Le Hou",
"Andrew Lampinen",
"Xiangning Chen",
"Da Huang",
"Yi Tay",
"Xinyun Chen",
"Yifeng Lu",
"Denny Zhou",
"Tengyu Ma",
"Quoc Le"
] | We present symbol tuning - finetuning language models on in-context input-label pairs where natural language labels (e.g., “positive/negative sentiment”) are replaced with arbitrary symbols (e.g., “foo/bar”). Symbol tuning leverages the intuition that when a model cannot use instructions or natural language labels to f... | 2023.emnlp-main.61 | 10.18653/v1/2023.emnlp-main.61 | null | 2305.08298 | title_snapshot | [
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The neural dynamics of word recognition and integration | https://aclanthology.org/2023.emnlp-main.62/ | [
"Jon Gauthier",
"Roger Levy"
] | Listeners recognize and integrate words in rapid and noisy everyday speech by combining expectations about upcoming content with incremental sensory evidence. We present a computational model of word recognition which formalizes this perceptual process in Bayesian decision theory. We fit this model to explain scalp EEG... | 2023.emnlp-main.62 | 10.18653/v1/2023.emnlp-main.62 | null | null | null | [
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Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models | https://aclanthology.org/2023.emnlp-main.63/ | [
"Gangwoo Kim",
"Sungdong Kim",
"Byeongguk Jeon",
"Joonsuk Park",
"Jaewoo Kang"
] | Questions in open-domain question answering are often ambiguous, allowing multiple interpretations. One approach to handling them is to identify all possible interpretations of the ambiguous question (AQ) and to generate a long-form answer addressing them all, as suggested by Stelmakh et al., (2022). While it provides ... | 2023.emnlp-main.63 | 10.18653/v1/2023.emnlp-main.63 | null | 2310.14696 | title_snapshot | [
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Incorporating Worker Perspectives into MTurk Annotation Practices for NLP | https://aclanthology.org/2023.emnlp-main.64/ | [
"Olivia Huang",
"Eve Fleisig",
"Dan Klein"
] | Current practices regarding data collection for natural language processing on Amazon Mechanical Turk (MTurk) often rely on a combination of studies on data quality and heuristics shared among NLP researchers. However, without considering the perspectives of MTurk workers, these approaches are susceptible to issues reg... | 2023.emnlp-main.64 | 10.18653/v1/2023.emnlp-main.64 | null | 2311.02802 | title_snapshot | [
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Predict the Future from the Past? On the Temporal Data Distribution Shift in Financial Sentiment Classifications | https://aclanthology.org/2023.emnlp-main.65/ | [
"Yue Guo",
"Chenxi Hu",
"Yi Yang"
] | Temporal data distribution shift is prevalent in the financial text. How can a financial sentiment analysis system be trained in a volatile market environment that can accurately infer sentiment and be robust to temporal data distribution shifts? In this paper, we conduct an empirical study on the financial sentiment a... | 2023.emnlp-main.65 | 10.18653/v1/2023.emnlp-main.65 | null | 2310.12620 | title_snapshot | [
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Look-back Decoding for Open-Ended Text Generation | https://aclanthology.org/2023.emnlp-main.66/ | [
"Nan Xu",
"Chunting Zhou",
"Asli Celikyilmaz",
"Xuezhe Ma"
] | Given a prefix (context), open-ended generation aims to decode texts that are coherent, which do not abruptly drift from previous topics, and informative, which do not suffer from undesired repetitions. In this paper, we propose Look-back, an improved decoding algorithm that leverages the Kullback–Leibler divergence to... | 2023.emnlp-main.66 | 10.18653/v1/2023.emnlp-main.66 | null | 2305.13477 | title_snapshot | [
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Large Language Models Can Self-Improve | https://aclanthology.org/2023.emnlp-main.67/ | [
"Jiaxin Huang",
"Shixiang Gu",
"Le Hou",
"Yuexin Wu",
"Xuezhi Wang",
"Hongkun Yu",
"Jiawei Han"
] | Large Language Models (LLMs) have achieved excellent performances in various tasks. However, fine-tuning an LLM requires extensive supervision. Human, on the other hand, may improve their reasoning abilities by self-thinking without external inputs. In this work, we demonstrate that an LLM is also capable of self-impro... | 2023.emnlp-main.67 | 10.18653/v1/2023.emnlp-main.67 | null | 2210.11610 | title_snapshot | [
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CodeT5+: Open Code Large Language Models for Code Understanding and Generation | https://aclanthology.org/2023.emnlp-main.68/ | [
"Yue Wang",
"Hung Le",
"Akhilesh Gotmare",
"Nghi Bui",
"Junnan Li",
"Steven Hoi"
] | Large language models (LLMs) pretrained on vast source code have achieved prominent progress in code intelligence. However, existing code LLMs have two main limitations. First, they often adopt a specific architecture (encoder-only or decoder-only) or rely on a unified encoder-decoder network for different downstream t... | 2023.emnlp-main.68 | 10.18653/v1/2023.emnlp-main.68 | null | 2305.07922 | title_snapshot | [
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Structural generalization in COGS: Supertagging is (almost) all you need | https://aclanthology.org/2023.emnlp-main.69/ | [
"Alban Petit",
"Caio Corro",
"François Yvon"
] | In many Natural Language Processing applications, neural networks have been found to fail to generalize on out-of-distribution examples. In particular, several recent semantic parsing datasets have put forward important limitations of neural networks in cases where compositional generalization is required. In this work... | 2023.emnlp-main.69 | 10.18653/v1/2023.emnlp-main.69 | null | 2310.14124 | title_snapshot | [
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BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations | https://aclanthology.org/2023.emnlp-main.70/ | [
"Qizhi Pei",
"Wei Zhang",
"Jinhua Zhu",
"Kehan Wu",
"Kaiyuan Gao",
"Lijun Wu",
"Yingce Xia",
"Rui Yan"
] | Recent advancements in biological research leverage the integration of molecules, proteins, and natural language to enhance drug discovery. However, current models exhibit several limitations, such as the generation of invalid molecular SMILES, underutilization of contextual information, and equal treatment of structur... | 2023.emnlp-main.70 | 10.18653/v1/2023.emnlp-main.70 | null | 2310.07276 | title_snapshot | [
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Hyperpolyglot LLMs: Cross-Lingual Interpretability in Token Embeddings | https://aclanthology.org/2023.emnlp-main.71/ | [
"Andrea W Wen-Yi",
"David Mimno"
] | Cross-lingual transfer learning is an important property of multilingual large language models (LLMs). But how do LLMs represent relationships between languages? Every language model has an input layer that maps tokens to vectors. This ubiquitous layer of language models is often overlooked. We find that similarities b... | 2023.emnlp-main.71 | 10.18653/v1/2023.emnlp-main.71 | null | 2311.18034 | title_snapshot | [
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Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation | https://aclanthology.org/2023.emnlp-main.72/ | [
"Jian Wang",
"Yi Cheng",
"Dongding Lin",
"Chak Leong",
"Wenjie Li"
] | Target-oriented dialogue systems, designed to proactively steer conversations toward predefined targets or accomplish specific system-side goals, are an exciting area in conversational AI. In this work, by formulating a <dialogue act, topic> pair as the conversation target, we explore a novel problem of personalized ta... | 2023.emnlp-main.72 | 10.18653/v1/2023.emnlp-main.72 | null | 2310.07397 | title_snapshot | [
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SeqXGPT: Sentence-Level AI-Generated Text Detection | https://aclanthology.org/2023.emnlp-main.73/ | [
"Pengyu Wang",
"Linyang Li",
"Ke Ren",
"Botian Jiang",
"Dong Zhang",
"Xipeng Qiu"
] | Widely applied large language models (LLMs) can generate human-like content, raising concerns about the abuse of LLMs. Therefore, it is important to build strong AI-generated text (AIGT) detectors. Current works only consider document-level AIGT detection, therefore, in this paper, we first introduce a sentence-level d... | 2023.emnlp-main.73 | null | null | 2310.08903 | title_snapshot | [
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QTSumm: Query-Focused Summarization over Tabular Data | https://aclanthology.org/2023.emnlp-main.74/ | [
"Yilun Zhao",
"Zhenting Qi",
"Linyong Nan",
"Boyu Mi",
"Yixin Liu",
"Weijin Zou",
"Simeng Han",
"Ruizhe Chen",
"Xiangru Tang",
"Yumo Xu",
"Dragomir Radev",
"Arman Cohan"
] | People primarily consult tables to conduct data analysis or answer specific questions. Text generation systems that can provide accurate table summaries tailored to users’ information needs can facilitate more efficient access to relevant data insights. Motivated by this, we define a new query-focused table summarizati... | 2023.emnlp-main.74 | 10.18653/v1/2023.emnlp-main.74 | null | 2305.14303 | title_snapshot | [
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From Wrong To Right: A Recursive Approach Towards Vision-Language Explanation | https://aclanthology.org/2023.emnlp-main.75/ | [
"Jiaxin Ge",
"Sanjay Subramanian",
"Trevor Darrell",
"Boyi Li"
] | Addressing the challenge of adapting pre-trained vision-language models for generating insightful explanations for visual reasoning tasks with limited annotations, we present ReVisE: a Recursive Visual Explanation algorithm. Our method iteratively computes visual features (conditioned on the text input), an answer, and... | 2023.emnlp-main.75 | 10.18653/v1/2023.emnlp-main.75 | null | 2311.12391 | title_snapshot | [
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‘Don’t Get Too Technical with Me’: A Discourse Structure-Based Framework for Automatic Science Journalism | https://aclanthology.org/2023.emnlp-main.76/ | [
"Ronald Cardenas",
"Bingsheng Yao",
"Dakuo Wang",
"Yufang Hou"
] | Science journalism refers to the task of reporting technical findings of a scientific paper as a less technical news article to the general public audience. We aim to design an automated system to support this real-world task (i.e., automatic science journalism ) by 1) introducing a newly-constructed and real-world dat... | 2023.emnlp-main.76 | 10.18653/v1/2023.emnlp-main.76 | null | 2310.15077 | title_judge | [
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LACMA: Language-Aligning Contrastive Learning with Meta-Actions for Embodied Instruction Following | https://aclanthology.org/2023.emnlp-main.77/ | [
"Cheng-Fu Yang",
"Yen-Chun Chen",
"Jianwei Yang",
"Xiyang Dai",
"Lu Yuan",
"Yu-Chiang Wang",
"Kai-Wei Chang"
] | End-to-end Transformers have demonstrated an impressive success rate for Embodied Instruction Following when the environment has been seen in training. However, they tend to struggle when deployed in an unseen environment. This lack of generalizability is due to the agent’s insensitivity to subtle changes in natural la... | 2023.emnlp-main.77 | 10.18653/v1/2023.emnlp-main.77 | null | 2310.12344 | title_snapshot | [
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Penalty Decoding: Well Suppress the Self-Reinforcement Effect in Open-Ended Text Generation | https://aclanthology.org/2023.emnlp-main.78/ | [
"Wenhong Zhu",
"Hongkun Hao",
"Rui Wang"
] | The decoding algorithm is critical for open-ended text generation, transforming latent representations into coherent and meaningful outputs. This paper investigates the self-reinforcement effect in text generation and the effectiveness of a repetition penalty to mitigate it. However, determining the optimal repetition ... | 2023.emnlp-main.78 | 10.18653/v1/2023.emnlp-main.78 | null | 2310.14971 | title_snapshot | [
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Towards Robust Pruning: An Adaptive Knowledge-Retention Pruning Strategy for Language Models | https://aclanthology.org/2023.emnlp-main.79/ | [
"Jianwei Li",
"Qi Lei",
"Wei Cheng",
"Dongkuan Xu"
] | The pruning objective has recently extended beyond accuracy and sparsity to robustness in language models. Despite this, existing methods struggle to enhance robustness against adversarial attacks when continually increasing model sparsity and require a retraining process. As humans step into the era of large language ... | 2023.emnlp-main.79 | 10.18653/v1/2023.emnlp-main.79 | null | 2310.13191 | title_snapshot | [
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Clinical Contradiction Detection | https://aclanthology.org/2023.emnlp-main.80/ | [
"Dave Makhervaks",
"Plia Gillis",
"Kira Radinsky"
] | Detecting contradictions in text is essential in determining the validity of the literature and sources that we consume. Medical corpora are riddled with conflicting statements. This is due to the large throughput of new studies and the difficulty in replicating experiments, such as clinical trials. Detecting contradic... | 2023.emnlp-main.80 | 10.18653/v1/2023.emnlp-main.80 | null | null | null | [
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Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements | https://aclanthology.org/2023.emnlp-main.81/ | [
"Jiacheng Liu",
"Wenya Wang",
"Dianzhuo Wang",
"Noah Smith",
"Yejin Choi",
"Hannaneh Hajishirzi"
] | Today’s language models can be remarkably intelligent yet still produce text that contains trivial commonsense errors. Therefore, we seek a retrospective verification approach that can reflect on the commonsense plausibility of the machine text, and introduce Vera, a general-purpose model that learns to estimate the co... | 2023.emnlp-main.81 | 10.18653/v1/2023.emnlp-main.81 | null | 2305.03695 | title_snapshot | [
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Text-Transport: Toward Learning Causal Effects of Natural Language | https://aclanthology.org/2023.emnlp-main.82/ | [
"Victoria Lin",
"Louis-Philippe Morency",
"Eli Ben-Michael"
] | As language technologies gain prominence in real-world settings, it is important to understand *how* changes to language affect reader perceptions. This can be formalized as the *causal effect* of varying a linguistic attribute (e.g., sentiment) on a reader’s response to the text. In this paper, we introduce Text-Trans... | 2023.emnlp-main.82 | 10.18653/v1/2023.emnlp-main.82 | null | 2310.20697 | title_snapshot | [
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How Does Generative Retrieval Scale to Millions of Passages? | https://aclanthology.org/2023.emnlp-main.83/ | [
"Ronak Pradeep",
"Kai Hui",
"Jai Gupta",
"Adam Lelkes",
"Honglei Zhuang",
"Jimmy Lin",
"Donald Metzler",
"Vinh Tran"
] | The emerging paradigm of generative retrieval re-frames the classic information retrieval problem into a sequence-to-sequence modeling task, forgoing external indices and encoding an entire document corpus within a single Transformer. Although many different approaches have been proposed to improve the effectiveness of... | 2023.emnlp-main.83 | 10.18653/v1/2023.emnlp-main.83 | null | 2305.11841 | title_snapshot | [
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Unveiling the Implicit Toxicity in Large Language Models | https://aclanthology.org/2023.emnlp-main.84/ | [
"Jiaxin Wen",
"Pei Ke",
"Hao Sun",
"Zhexin Zhang",
"Chengfei Li",
"Jinfeng Bai",
"Minlie Huang"
] | The open-endedness of large language models (LLMs) combined with their impressive capabilities may lead to new safety issues when being exploited for malicious use. While recent studies primarily focus on probing toxic outputs that can be easily detected with existing toxicity classifiers, we show that LLMs can generat... | 2023.emnlp-main.84 | 10.18653/v1/2023.emnlp-main.84 | null | 2311.17391 | title_snapshot | [
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Is ChatGPT a General-Purpose Natural Language Processing Task Solver? | https://aclanthology.org/2023.emnlp-main.85/ | [
"Chengwei Qin",
"Aston Zhang",
"Zhuosheng Zhang",
"Jiaao Chen",
"Michihiro Yasunaga",
"Diyi Yang"
] | Spurred by advancements in scale, large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot—i.e., without adaptation on downstream data. Recently, the debut of ChatGPT has drawn a great deal of attention from the natural language processing (NLP... | 2023.emnlp-main.85 | 10.18653/v1/2023.emnlp-main.85 | null | 2302.06476 | title_snapshot | [
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Length is a Curse and a Blessing for Document-level Semantics | https://aclanthology.org/2023.emnlp-main.86/ | [
"Chenghao Xiao",
"Yizhi Li",
"G Hudson",
"Chenghua Lin",
"Noura Al Moubayed"
] | In recent years, contrastive learning (CL) has been extensively utilized to recover sentence and document-level encoding capability from pre-trained language models. In this work, we question the length generalizability of CL-based models, i.e., their vulnerability towards length-induced semantic shift. We verify not o... | 2023.emnlp-main.86 | 10.18653/v1/2023.emnlp-main.86 | null | 2310.16193 | title_snapshot | [
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ALCUNA: Large Language Models Meet New Knowledge | https://aclanthology.org/2023.emnlp-main.87/ | [
"Xunjian Yin",
"Baizhou Huang",
"Xiaojun Wan"
] | With the rapid development of NLP, large-scale language models (LLMs) excel in various tasks across multiple domains now. However, existing benchmarks may not adequately measure these models’ capabilities, especially when faced with new knowledge. In this paper, we address the lack of benchmarks to evaluate LLMs’ abili... | 2023.emnlp-main.87 | 10.18653/v1/2023.emnlp-main.87 | null | 2310.14820 | title_snapshot | [
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Location-Aware Visual Question Generation with Lightweight Models | https://aclanthology.org/2023.emnlp-main.88/ | [
"Nicholas Suwono",
"Justin Chen",
"Tun Hung",
"Ting-Hao Huang",
"I-Bin Liao",
"Yung-Hui Li",
"Lun-Wei Ku",
"Shao-Hua Sun"
] | This work introduces a novel task, location-aware visual question generation (LocaVQG), which aims to generate engaging questions from data relevant to a particular geographical location. Specifically, we represent such location-aware information with surrounding images and a GPS coordinate. To tackle this task, we pre... | 2023.emnlp-main.88 | 10.18653/v1/2023.emnlp-main.88 | null | 2310.15129 | title_snapshot | [
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MemeCap: A Dataset for Captioning and Interpreting Memes | https://aclanthology.org/2023.emnlp-main.89/ | [
"EunJeong Hwang",
"Vered Shwartz"
] | Memes are a widely popular tool for web users to express their thoughts using visual metaphors. Understanding memes requires recognizing and interpreting visual metaphors with respect to the text inside or around the meme, often while employing background knowledge and reasoning abilities. We present the task of meme c... | 2023.emnlp-main.89 | 10.18653/v1/2023.emnlp-main.89 | null | 2305.13703 | title_snapshot | [
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Where to start? Analyzing the potential value of intermediate models | https://aclanthology.org/2023.emnlp-main.90/ | [
"Leshem Choshen",
"Elad Venezian",
"Shachar Don-Yehiya",
"Noam Slonim",
"Yoav Katz"
] | Previous studies observed that finetuned models may be better base models than the vanilla pretrained model. Such a model, finetuned on some source dataset, may provide a better starting point for a new finetuning process on a desired target dataset. Here, we perform a systematic analysis of this intertraining scheme, ... | 2023.emnlp-main.90 | 10.18653/v1/2023.emnlp-main.90 | null | 2211.00107 | title_snapshot | [
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Transcending Scaling Laws with 0.1% Extra Compute | https://aclanthology.org/2023.emnlp-main.91/ | [
"Yi Tay",
"Jason Wei",
"Hyung Chung",
"Vinh Tran",
"David So",
"Siamak Shakeri",
"Xavier Garcia",
"Steven Zheng",
"Jinfeng Rao",
"Aakanksha Chowdhery",
"Denny Zhou",
"Donald Metzler",
"Slav Petrov",
"Neil Houlsby",
"Quoc Le",
"Mostafa Dehghani"
] | Scaling language models improves performance but comes with significant computational costs. This paper proposes UL2R, a method that substantially improves existing language models and their scaling curves with a relatively tiny amount of extra compute. The key idea is to continue training a state-of-the-art large lang... | 2023.emnlp-main.91 | 10.18653/v1/2023.emnlp-main.91 | null | 2210.11399 | title_snapshot | [
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CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation | https://aclanthology.org/2023.emnlp-main.92/ | [
"Minzhi Li",
"Taiwei Shi",
"Caleb Ziems",
"Min-Yen Kan",
"Nancy Chen",
"Zhengyuan Liu",
"Diyi Yang"
] | Annotated data plays a critical role in Natural Language Processing (NLP) in training models and evaluating their performance. Given recent developments in Large Language Models (LLMs), models such as ChatGPT demonstrate zero-shot capability on many text-annotation tasks, comparable with or even exceeding human annotat... | 2023.emnlp-main.92 | 10.18653/v1/2023.emnlp-main.92 | null | 2310.15638 | title_snapshot | [
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Optimizing Retrieval-augmented Reader Models via Token Elimination | https://aclanthology.org/2023.emnlp-main.93/ | [
"Moshe Berchansky",
"Peter Izsak",
"Avi Caciularu",
"Ido Dagan",
"Moshe Wasserblat"
] | Fusion-in-Decoder (FiD) is an effective retrieval-augmented language model applied across a variety of open-domain tasks, such as question answering, fact checking, etc. In FiD, supporting passages are first retrieved and then processed using a generative model (Reader), which can cause a significant bottleneck in deco... | 2023.emnlp-main.93 | 10.18653/v1/2023.emnlp-main.93 | null | 2310.13682 | title_snapshot | [
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WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom | https://aclanthology.org/2023.emnlp-main.94/ | [
"Ruichao Yang",
"Wei Gao",
"Jing Ma",
"Hongzhan Lin",
"Zhiwei Yang"
] | Fake news debunking primarily focuses on determining the truthfulness of news articles, which oversimplifies the issue as fake news often combines elements of both truth and falsehood. Thus, it becomes crucial to identify specific instances of misinformation within the articles. In this research, we investigate a novel... | 2023.emnlp-main.94 | 10.18653/v1/2023.emnlp-main.94 | null | 2310.16579 | title_snapshot | [
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Robust Prompt Optimization for Large Language Models Against Distribution Shifts | https://aclanthology.org/2023.emnlp-main.95/ | [
"Moxin Li",
"Wenjie Wang",
"Fuli Feng",
"Yixin Cao",
"Jizhi Zhang",
"Tat-Seng Chua"
] | Large Language Model (LLM) has demonstrated significant ability in various Natural Language Processing tasks. However, their effectiveness is highly dependent on the phrasing of the task prompt, leading to research on automatic prompt optimization using labeled task data. We reveal that these prompt optimization techni... | 2023.emnlp-main.95 | 10.18653/v1/2023.emnlp-main.95 | null | 2305.13954 | title_snapshot | [
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Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction | https://aclanthology.org/2023.emnlp-main.96/ | [
"Martin Josifoski",
"Marija Sakota",
"Maxime Peyrard",
"Robert West"
] | Large language models (LLMs) have great potential for synthetic data generation. This work shows that useful data can be synthetically generated even for tasks that cannot be solved directly by LLMs: for problems with structured outputs, it is possible to prompt an LLM to perform the task in the reverse direction, by g... | 2023.emnlp-main.96 | 10.18653/v1/2023.emnlp-main.96 | null | 2303.04132 | title_snapshot | [
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Condensing Multilingual Knowledge with Lightweight Language-Specific Modules | https://aclanthology.org/2023.emnlp-main.97/ | [
"Haoran Xu",
"Weiting Tan",
"Shuyue Li",
"Yunmo Chen",
"Benjamin Van Durme",
"Philipp Koehn",
"Kenton Murray"
] | Incorporating language-specific (LS) modules or Mixture-of-Experts (MoE) are proven methods to boost performance in multilingual model performance, but the scalability of these approaches to hundreds of languages or experts tends to be hard to manage. We present Language-specific Matrix Synthesis (LMS), a novel method ... | 2023.emnlp-main.97 | 10.18653/v1/2023.emnlp-main.97 | null | 2305.13993 | title_snapshot | [
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The Framework Tax: Disparities Between Inference Efficiency in NLP Research and Deployment | https://aclanthology.org/2023.emnlp-main.98/ | [
"Jared Fernandez",
"Jacob Kahn",
"Clara Na",
"Yonatan Bisk",
"Emma Strubell"
] | Increased focus on the computational efficiency of systems in natural language processing has motivated the design of efficient model architectures and improvements to underlying hardware accelerators. However, the resulting increases in computational throughput and reductions in floating point operations have not dire... | 2023.emnlp-main.98 | 10.18653/v1/2023.emnlp-main.98 | null | 2302.06117 | title_snapshot | [
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Evaluating Cross-Domain Text-to-SQL Models and Benchmarks | https://aclanthology.org/2023.emnlp-main.99/ | [
"Mohammadreza Pourreza",
"Davood Rafiei"
] | Text-to-SQL benchmarks play a crucial role in evaluating the progress made in the field and the ranking of different models. However, accurately matching a model-generated SQL query to a reference SQL query in a benchmark fails for various reasons, such as underspecified natural language queries, inherent assumptions i... | 2023.emnlp-main.99 | 10.18653/v1/2023.emnlp-main.99 | null | 2310.18538 | title_snapshot | [
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Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs | https://aclanthology.org/2023.emnlp-main.100/ | [
"Simone Conia",
"Min Li",
"Daniel Lee",
"Umar Minhas",
"Ihab Ilyas",
"Yunyao Li"
] | Recent work in Natural Language Processing and Computer Vision has been using textual information – e.g., entity names and descriptions – available in knowledge graphs to ground neural models to high-quality structured data. However, when it comes to non-English languages, the quantity and quality of textual informatio... | 2023.emnlp-main.100 | 10.18653/v1/2023.emnlp-main.100 | null | 2311.15781 | title_snapshot | [
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-0... |
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