Большие языковые модели

Ngram language models tend to increase in size with inflating the corpus size, and con- sume considerable resources. In this pa- per, we propose an efficient method for im- plementing …...

AbstractHow do adjectives project from a noun to its parts? If a motorcycle is red, are its wheels red? Is a nuclear submarine’s captain nuclear? These questions are easy for …...

AbstractThere has been considerable progress made towards conversational models that generate coherent and fluent responses; however, this often involves training large language models on large dialogue datasets, such as Reddit. …...

AbstractLarge language models have recently achieved state of the art performance across a wide variety of natural language tasks. Meanwhile, the size of these models and their latency have significantly …...

AbstractLarge language models have shown promising results in zero-shot settings. For example, they can perform multiple choice tasks simply by conditioning on a question and selecting the answer with the …...

AbstractSocratic questioning is an educational method that allows students to discover answers to complex problems by asking them a series of thoughtful questions. Generation of didactically sound questions is challenging, …...

AbstractRecent work on applying large language models (LMs) achieves impressive performance in many NLP applications. Adapting or posttraining an LM using an unlabeled domain corpus can produce even better performance …...

AbstractThe remarkable success of large language models has been driven by dense models trained on massive unlabeled, unstructured corpora. These corpora typically contain text from diverse, heterogeneous sources, but information …...

AbstractThis paper investigates models of event implications. Specifically, how well models predict entity state-changes, by targeting their understanding of physical attributes. Nominally, Large Language models (LLM) have been exposed to …...

AbstractRecent work on large language models relies on the intuition that most natural language processing tasks can be described via natural language instructions and that models trained on these instructions …...

AbstractThe recent success of large language models for text generation poses a severe threat to academic integrity, as plagiarists can generate realistic paraphrases indistinguishable from original work. However, the role …...

AbstractLarge Language Models (LMs) have achieved state-of-the-art performance on many Natural Language Processing (NLP) benchmarks. With the growing number of new benchmarks, we build bigger and more complex LMs. However, …...

AbstractPrevious work has demonstrated that pre-trained large language models (LLM) acquire knowledge during pre-training which enables reasoning over relationships between words (e.g, hyponymy) and more complex inferences over larger units …...

AbstractSentence Simplification is a valuable technique that can benefit language learners and children a lot. However, current research focuses more on English sentence simplification. The development of Chinese sentence simplification …...

AbstractThe remarkable advancements in large language models (LLMs) have brought about significant improvements in Natural Language Processing(NLP) tasks. This paper presents a comprehensive review of in-context learning techniques, focusing on …...

AbstractIn this paper we evaluate the utility of large language models (LLMs) for translation of text with markup in which the most important and challenging aspect is to correctly transfer …...

AbstractLarge language models (LLMs) have demonstrated powerful capabilities in both text understanding and generation. Companies have begun to offer Embedding as a Service (EaaS) based on these LLMs, which can …...

AbstractThis paper describes our zero-shot approachesfor the Visual Word Sense Disambiguation(VWSD) Task in English. Our preliminarystudy shows that the simple approach of match-ing candidate images with the phrase usingCLIP suffers …...

AbstractLarge language models (LLMs) pre-trained on massive corpora have demonstrated impressive few-shot learning ability on many NLP tasks. A common practice is to recast the task into a text-to-text format …...

AbstractWhile the fluency and coherence of Large Language Models (LLMs) in text generation have seen significant improvements, their competency in generating appropriate expressions of uncertainty remains limited.Using a multilingual closed-book …...

AbstractIn everyday life, humans often plan their actions by following step-by-step instructions in the form of goal-oriented scripts. Previous work has exploited language models (LMs) to plan for abstract goals …...

AbstractLarge language models (LLMs) have been widely employed for graph-to-text generation tasks. However, the process of finetuning LLMs requires significant training resources and annotation work. In this paper, we explore …...

AbstractRelationship extraction from unstructured data remains one of the most challenging tasks in the field of Natural Language Processing (NLP). The complexity of relationship extraction arises from the need to …...

AbstractLarge language models (LLMs) and generative AI have emerged as the most important areas in the field of natural language processing (NLP). LLMs are considered to be a key component …...

AbstractLarge language models (LLMs) can be used to generate text data for training and evaluating other models. However, creating high-quality datasets with LLMs can be challenging. In this work, we …...

AbstractRemarkable progress has been made on automated reasoning with natural text, by using Large Language Models (LLMs) and methods such as Chain-of-Thought prompting and Selection-Inference. These techniques search for proofs …...

AbstractRecent advances in the capacity of large language models to generate human-like text have resulted in their increased adoption in user-facing settings. In parallel, these improvements have prompted a heated …...

AbstractRecent literature has shown that large language models (LLMs) are generally excellent few-shot reasoners to solve text reasoning tasks. However, the capability of LLMs on table reasoning tasks is yet …...

AbstractWith the advent of large language models (LLMs), the trend in NLP has been to train LLMs on vast amounts of data to solve diverse language understanding and generation tasks. …...

Medical Named Entity Recognition (NER) stands as a pivotal technique within the realm of medicine, cate sequence labeling.encompassing intri- Profound medi- cal knowledge acumen and accurate de- marcation of entity …...

AbstractLarge language models (LLMs) have been shown to perform well in answering questions and in producing long-form texts, both in few-shot closed-book settings. While the former can be validated using …...

AbstractLarge language models (LMs) beyond a certain scale, demonstrate the emergent capability of generating free-text rationales for their predictions via chain-of-thought (CoT) prompting. While CoT can yield dramatically improved performance, …...

AbstractExisting bias mitigation methods require social-group-specific word pairs (e.g., “man” – “woman”) for each social attribute (e.g., gender), restricting the bias mitigation to only one specified social attribute. Further, this …...

In this study, we delve into the efficacy of the Tree-of-Thought Prompting technique as a mechanism to address linguistic challenges and augment the reasoning capabilities of expansive language models. Specifically, …...

AbstractAs large language models (LLMs) have become the norm in NLP, demonstrating good performance in generation and reasoning tasks, one of its most fatal disadvantages is the lack of factual …...

AbstractLarge language models (LLMs) are subject to sociocultural and other biases previously identified using intrinsic evaluations. However, when and how these intrinsic biases in pre-trained LM representations propagate to downstream, …...

AbstractDespite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the difficulty of encoding a wealth of world knowledge in …...