Survey on factuality in large language models: Knowledge, retrieval and domain-specificity

C Wang, X Liu, Y Yue, X Tang, T Zhang… - arxiv preprint arxiv …, 2023‏ - arxiv.org
This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As
LLMs find applications across diverse domains, the reliability and accuracy of their outputs …

Recommender systems in the era of large language models (llms)

Z Zhao, W Fan, J Li, Y Liu, X Mei… - … on Knowledge and …, 2024‏ - ieeexplore.ieee.org
With the prosperity of e-commerce and web applications, Recommender Systems (RecSys)
have become an indispensable and important component, providing personalized …

[PDF][PDF] A survey of large language models

WX Zhao, K Zhou, J Li, T Tang… - arxiv preprint arxiv …, 2023‏ - paper-notes.zhjwpku.com
Ever since the Turing Test was proposed in the 1950s, humans have explored the mastering
of language intelligence by machine. Language is essentially a complex, intricate system of …

Datasets for large language models: A comprehensive survey

Y Liu, J Cao, C Liu, K Ding, L ** - arxiv preprint arxiv:2402.18041, 2024‏ - arxiv.org
This paper embarks on an exploration into the Large Language Model (LLM) datasets,
which play a crucial role in the remarkable advancements of LLMs. The datasets serve as …

[HTML][HTML] Large language models in law: A survey

J Lai, W Gan, J Wu, Z Qi, SY Philip - AI Open, 2024‏ - Elsevier
The advent of artificial intelligence (AI) has significantly impacted the traditional judicial
industry. Moreover, recently, with the development of the concept of AI-generated content …

A survey on knowledge distillation of large language models

X Xu, M Li, C Tao, T Shen, R Cheng, J Li, C Xu… - arxiv preprint arxiv …, 2024‏ - arxiv.org
In the era of Large Language Models (LLMs), Knowledge Distillation (KD) emerges as a
pivotal methodology for transferring advanced capabilities from leading proprietary LLMs …

Adapting Large Language Models to Domains via Reading Comprehension

D Cheng, S Huang, F Wei - arxiv preprint arxiv:2309.09530, 2023‏ - arxiv.org
We explore how continued pre-training on domain-specific corpora influences large
language models, revealing that training on the raw corpora endows the model with domain …

Lawbench: Benchmarking legal knowledge of large language models

Z Fei, X Shen, D Zhu, F Zhou, Z Han, S Zhang… - arxiv preprint arxiv …, 2023‏ - arxiv.org
Large language models (LLMs) have demonstrated strong capabilities in various aspects.
However, when applying them to the highly specialized, safe-critical legal domain, it is …

Sniffer: Multimodal large language model for explainable out-of-context misinformation detection

P Qi, Z Yan, W Hsu, ML Lee - Proceedings of the IEEE/CVF …, 2024‏ - openaccess.thecvf.com
Misinformation is a prevalent societal issue due to its potential high risks. Out-Of-Context
(OOC) misinformation where authentic images are repurposed with false text is one of the …

Disc-lawllm: Fine-tuning large language models for intelligent legal services

S Yue, W Chen, S Wang, B Li, C Shen, S Liu… - arxiv preprint arxiv …, 2023‏ - arxiv.org
We propose DISC-LawLLM, an intelligent legal system utilizing large language models
(LLMs) to provide a wide range of legal services. We adopt legal syllogism prompting …