163 citations · 355 across the 9 of their papers we have counts for
9 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.…
A Cognitive Paradigm Approach to Probe the Perception-Reasoning Interface in VLMs
Mohit Vaishnav, Tanel Tammet
A fundamental challenge in artificial intelligence involves understanding the cognitive mechanisms underlying visual reasoning in sophisticated models like Vision-Language Models (…
FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data
Haoran Sun, Renren Jin, Shaoyang Xu +10
Large language models (LLMs) have demonstrated prowess in a wide range of tasks. However, many LLMs exhibit significant performance discrepancies between high- and low-resource lan…
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Marah Abdin, Jyoti Aneja, Hany Awadalla +126
We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal test…
Textbooks Are All You Need II: phi-1.5 technical report
Yuanzhi Li, Sébastien Bubeck, Ronen Eldan +3
We continue the investigation into the power of smaller Transformer-based language models as initiated by \textbf{TinyStories} -- a 10 million parameter model that can produce cohe…
SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference
Luciano Del Corro, Allie Del Giorno, Sahaj Agarwal +3
Autoregressive large language models (LLMs) have made remarkable progress in various natural language generation tasks. However, they incur high computation cost and latency result…
Textbooks Are All You Need
Suriya Gunasekar, Yi Zhang, Jyoti Aneja +16
We introduce phi-1, a new large language model for code, with significantly smaller size than competing models: phi-1 is a Transformer-based model with 1.3B parameters, trained for…
Log-DenseNet: How to Sparsify a DenseNet
Hanzhang Hu, Debadeepta Dey, Allison Del Giorno +2
Skip connections are increasingly utilized by deep neural networks to improve accuracy and cost-efficiency. In particular, the recent DenseNet is efficient in computation and param…
Ignoring Distractors in the Absence of Labels: Optimal Linear Projection to Remove False Positives During Anomaly Detection
Allison Del Giorno, J. Andrew Bagnell, Martial Hebert
In the anomaly detection setting, the native feature embedding can be a crucial source of bias. We present a technique, Feature Omission using Context in Unsupervised Settings (FOC…