NICE: To Optimize In-Context Examples or Not?
Recent works have shown that large language models (LLMs) work remarkably well on a wide range of tasks through in-context learning and optimization of in-context examples (ICE). However, most of these...
View ArticleEntGPT: Linking Generative Large Language Models with Knowledge Bases
The ability of Large Language Models (LLMs) to generate factually correct output remains relatively unexplored due to the lack of fact-checking and knowledge grounding during training and inference. In...
View ArticleEvaluation Metrics for Text Data Augmentation in NLP
Recent surveys on data augmentation for natural language processing have reported different techniques and advancements in the field. Several frameworks, tools, and repositories promote the...
View ArticleHistory, Development, and Principles of Large Language Models-An Introductory...
Language models serve as a cornerstone in natural language processing (NLP), utilizing mathematical methods to generalize language laws and knowledge for prediction and generation. Over extensive...
View ArticleGenTranslate: Large Language Models are Generative Multilingual Speech and...
Recent advances in large language models (LLMs) have stepped forward the development of multilingual speech and machine translation by its reduced representation errors and incorporated external...
View ArticleCan LLMs Recognize Toxicity? Structured Toxicity Investigation Framework and...
In the pursuit of developing Large Language Models (LLMs) that adhere to societal standards, it is imperative to discern the existence of toxicity in the generated text. The majority of existing...
View ArticleInvestigating Consistency in Query-Based Meeting Summarization: A Comparative...
With more and more advanced data analysis techniques emerging, people will expect these techniques to be applied in more complex tasks and solve problems in our daily lives. Text Summarization is one...
View ArticleTL;DR Progress: Multi-faceted Literature Exploration in Text Summarization
This paper presents TL;DR Progress, a new tool for exploring the literature on neural text summarization. It organizes 514~papers based on a comprehensive annotation scheme for text summarization...
View ArticleA Thorough Examination of Decoding Methods in the Era of LLMs
Decoding methods play an indispensable role in converting language models from next-token predictors into practical task solvers. Prior research on decoding methods, primarily focusing on task-specific...
View ArticleLiFi: Lightweight Controlled Text Generation with Fine-Grained Control Codes
In the rapidly evolving field of text generation, the demand for more precise control mechanisms has become increasingly apparent. To address this need, we present a novel methodology, LIFI, which...
View ArticleShould I try multiple optimizers when fine-tuning pre-trained Transformers...
NLP research has explored different neural model architectures and sizes, datasets, training objectives, and transfer learning techniques. However, the choice of optimizer during training has not been...
View ArticleSpeechCLIP+: Self-supervised multi-task representation learning for speech...
The recently proposed visually grounded speech model SpeechCLIP is an innovative framework that bridges speech and text through images via CLIP without relying on text transcription. On this basis,...
View ArticleNLP for Knowledge Discovery and Information Extraction from Energetics Corpora
We present a demonstration of the utility of NLP for aiding research into energetic materials and associated systems. The NLP method enables machine understanding of textual data, offering an automated...
View ArticleInstruct Once, Chat Consistently in Multiple Rounds: An Efficient Tuning...
Tuning pretrained language models for dialogue generation has been a prevalent paradigm for building capable dialogue agents. Yet, traditional tuning narrowly views dialogue generation as resembling...
View ArticleEvent-Keyed Summarization
We introduce event-keyed summarization (EKS), a novel task that marries traditional summarization and document-level event extraction, with the goal of generating a contextualized summary for a...
View ArticleGemini Goes to Med School: Exploring the Capabilities of Multimodal Large...
Large language models have the potential to be valuable in the healthcare industry, but it's crucial to verify their safety and effectiveness through rigorous evaluation. For this purpose, we...
View ArticleSemi-Supervised Learning for Bilingual Lexicon Induction
We consider the problem of aligning two sets of continuous word representations, corresponding to languages, to a common space in order to infer a bilingual lexicon. It was recently shown that it is...
View ArticleUsing Large Language Models for Student-Code Guided Test Case Generation in...
In computer science education, test cases are an integral part of programming assignments since they can be used as assessment items to test students' programming knowledge and provide personalized...
View ArticleGeneralizing Conversational Dense Retrieval via LLM-Cognition Data Augmentation
Conversational search utilizes muli-turn natural language contexts to retrieve relevant passages. Existing conversational dense retrieval models mostly view a conversation as a fixed sequence of...
View ArticleNatural Language Reinforcement Learning
Reinforcement Learning (RL) has shown remarkable abilities in learning policies for decision-making tasks. However, RL is often hindered by issues such as low sample efficiency, lack of...
View Article![BLACKPINK – DEADLINE – EP [iTunes Plus M4A]](http://is1-ssl.mzstatic.com/image/thumb/Video211/v4/de/b3/b1/deb3b1d4-ba28-22e8-2428-ef97617b2a4b/90d54d743002a6d66308f217cd867947_Preview_Image_Intermediate_nonvideo_sdr_426725135_2574858051.png/316x316bb.webp)
