708 citations · 2.5k across the 18 of their papers we have counts for
9 papers · 1 filter
The Bias is in the Details: An Assessment of Cognitive Bias in LLMs
R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3
As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…
Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai
Parinthapat Pengpun, Can Udomcharoenchaikit, Weerayut Buaphet +1
We present a synthetic data approach for instruction-tuning large language models (LLMs) for low-resource languages in a data-efficient manner, specifically focusing on Thai. We id…
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Gemini Team, Petko Georgiev, Ving Ian Lei +1132
In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over…
Assessing biomedical knowledge robustness in large language models by query-efficient sampling attacks
R. Patrick Xian, Alex J. Lee, Satvik Lolla +4
The increasing depth of parametric domain knowledge in large language models (LLMs) is fueling their rapid deployment in real-world applications. Understanding model vulnerabilitie…
Learning to Skip for Language Modeling
Dewen Zeng, Nan Du, Tao Wang +4
Overparameterized large-scale language models have impressive generalization performance of in-context few-shot learning. However, most language models allocate the same amount of…
PaLM 2 Technical Report
Rohan Anil, Andrew M. Dai, Orhan Firat +125
We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 i…
LaMDA: Language Models for Dialog Applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall +57
We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters an…
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
Nan Du, Yanping Huang, Andrew M. Dai +24
Scaling language models with more data, compute and parameters has driven significant progress in natural language processing. For example, thanks to scaling, GPT-3 was able to ach…
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu +6
Neural network scaling has been critical for improving the model quality in many real-world machine learning applications with vast amounts of training data and compute. Although t…