Challenges and applications of large language models
Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …
A survey of text watermarking in the era of large language models
Text watermarking algorithms are crucial for protecting the copyright of textual content.
Historically, their capabilities and application scenarios were limited. However, recent …
Historically, their capabilities and application scenarios were limited. However, recent …
A survey of large language models
Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …
Harnessing the power of llms in practice: A survey on chatgpt and beyond
This article presents a comprehensive and practical guide for practitioners and end-users
working with Large Language Models (LLMs) in their downstream Natural Language …
working with Large Language Models (LLMs) in their downstream Natural Language …
G-eval: Nlg evaluation using gpt-4 with better human alignment
The quality of texts generated by natural language generation (NLG) systems is hard to
measure automatically. Conventional reference-based metrics, such as BLEU and ROUGE …
measure automatically. Conventional reference-based metrics, such as BLEU and ROUGE …
H2o: Heavy-hitter oracle for efficient generative inference of large language models
Abstract Large Language Models (LLMs), despite their recent impressive accomplishments,
are notably cost-prohibitive to deploy, particularly for applications involving long-content …
are notably cost-prohibitive to deploy, particularly for applications involving long-content …
A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions
The emergence of large language models (LLMs) has marked a significant breakthrough in
natural language processing (NLP), fueling a paradigm shift in information acquisition …
natural language processing (NLP), fueling a paradigm shift in information acquisition …
Exploring the limits of chatgpt for query or aspect-based text summarization
Text summarization has been a crucial problem in natural language processing (NLP) for
several decades. It aims to condense lengthy documents into shorter versions while …
several decades. It aims to condense lengthy documents into shorter versions while …
Sources of hallucination by large language models on inference tasks
Large Language Models (LLMs) are claimed to be capable of Natural Language Inference
(NLI), necessary for applied tasks like question answering and summarization. We present a …
(NLI), necessary for applied tasks like question answering and summarization. We present a …
Guiding large language models via directional stimulus prompting
Abstract We introduce Directional Stimulus Prompting, a novel framework for guiding black-
box large language models (LLMs) towards specific desired outputs. Instead of directly …
box large language models (LLMs) towards specific desired outputs. Instead of directly …