A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions [PDF]
ACM Trans. Inf. Syst., 2023 The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP), fueling a paradigm shift in information acquisition.
Lei Huang +10 more
semanticscholar +1 more source
Large language models propagate race-based medicine
npj Digital Medicine, 2023 Large language models (LLMs) are being integrated into healthcare systems; but these models may recapitulate harmful, race-based medicine. The objective of this study is to assess whether four commercially available large language models (LLMs) propagate
Jesutofunmi A. Omiye +4 more
doaj +1 more source
Evaluating ChatGPT, Gemini and other Large Language Models (LLMs) in orthopaedic diagnostics: A prospective clinical study. [PDF]
Comput Struct Biotechnol JPagano S +8 more
europepmc +2 more sources
Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling [PDF]
International Conference on Machine Learning, 2023 How do large language models (LLMs) develop and evolve over the course of training? How do these patterns change as models scale? To answer these questions, we introduce \textit{Pythia}, a suite of 16 LLMs all trained on public data seen in the exact ...
Stella Biderman +12 more
semanticscholar +1 more source
ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs [PDF]
International Conference on Learning Representations, 2023 Despite the advancements of open-source large language models (LLMs), e.g., LLaMA, they remain significantly limited in tool-use capabilities, i.e., using external tools (APIs) to fulfill human instructions.
Yujia Qin +17 more
semanticscholar +1 more source
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models [PDF]
International Conference on Machine Learning, 2022 Large language models (LLMs) show excellent performance but are compute- and memory-intensive. Quantization can reduce memory and accelerate inference. However, existing methods cannot maintain accuracy and hardware efficiency at the same time.
Guangxuan Xiao +4 more
semanticscholar +1 more source
Unifying Large Language Models and Knowledge Graphs: A Roadmap [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2023 Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the field of natural language processing and artificial intelligence, due to their emergent ability and generalizability. However, LLMs are black-box models, which often fall
Shirui Pan +5 more
semanticscholar +1 more source
Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment [PDF]
arXiv.org, 2023 Ensuring alignment, which refers to making models behave in accordance with human intentions [1,2], has become a critical task before deploying large language models (LLMs) in real-world applications.
Yang Liu +8 more
semanticscholar +1 more source
The promises of large language models for protein design and modeling
Frontiers in Bioinformatics, 2023 The recent breakthroughs of Large Language Models (LLMs) in the context of natural language processing have opened the way to significant advances in protein research. Indeed, the relationships between human natural language and the “language of proteins”
Giorgio Valentini +12 more
doaj +1 more source
Efficient Memory Management for Large Language Model Serving with PagedAttention [PDF]
Symposium on Operating Systems Principles, 2023 High throughput serving of large language models (LLMs) requires batching sufficiently many requests at a time. However, existing systems struggle because the key-value cache (KV cache) memory for each request is huge and grows and shrinks dynamically ...
Woosuk Kwon +8 more
semanticscholar +1 more source