297 citations · 314 across the 8 of their papers we have counts for
8 papers
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…
Beyond Performance: Quantifying and Mitigating Label Bias in LLMs
Yuval Reif, Roy Schwartz
Large language models (LLMs) have shown remarkable adaptability to diverse tasks, by leveraging context prompts containing instructions, or minimal input-output examples. However,…
Holistic Safety and Responsibility Evaluations of Advanced AI Models
Laura Weidinger, Joslyn Barnhart, Jenny Brennan +16
Safety and responsibility evaluations of advanced AI models are a critical but developing field of research and practice. In the development of Google DeepMind's advanced AI models…
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…
Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering
Han Zhou, Xingchen Wan, Lev Proleev +4
Prompting and in-context learning (ICL) have become efficient learning paradigms for large language models (LLMs). However, LLMs suffer from prompt brittleness and various bias fac…
Benchmarking Continuous Time Models for Predicting Multiple Sclerosis Progression
Alexander Norcliffe, Lev Proleev, Diana Mincu +3
Multiple sclerosis is a disease that affects the brain and spinal cord, it can lead to severe disability and has no known cure. The majority of prior work in machine learning for m…
Disability prediction in multiple sclerosis using performance outcome measures and demographic data
Subhrajit Roy, Diana Mincu, Lev Proleev +8
Literature on machine learning for multiple sclerosis has primarily focused on the use of neuroimaging data such as magnetic resonance imaging and clinical laboratory tests for dis…