Ajay Sharma

Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri et al. · allen-ai, berkeley

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

Anubhav Reddy Nallabasannagari, Madhu Reddiboina, Ryan Seltzer et al.

Intensive care clinicians need reliable clinical practice tools to preempt unexpected critical events that might harm their patients in intensive care units (ICU), to pre-plan timely interventions, and to keep the patient's family well informed. The conventional statistical models are built by curating only a limited number of key variables, which means a vast unknown amount of potentially precious data remains unused. Deep learning models (DLMs) can be leveraged to learn from large complex datasets and construct predictive clinical tools. This retrospective study was performed using 42,818 hospital admissions involving 35,348 patients, which is a subset of the MIMIC-III dataset. Natural language processing (NLP) techniques were applied to build DLMs to predict in-hospital mortality (IHM) and length of stay >=7 days (LOS). Over 75 million events across multiple data sources were processed, resulting in over 355 million tokens. DLMs for predicting IHM using data from all sources (AS) and chart data (CS) achieved an AUC-ROC of 0.9178 and 0.9029, respectively, and PR-AUC of 0.6251 and 0.5701, respectively. DLMs for predicting LOS using AS and CS achieved an AUC-ROC of 0.8806 and 0.8642, respectively, and PR-AUC of 0.6821 and 0.6575, respectively. The observed AUC-ROC difference between models was found to be significant for both IHM and LOS at p=0.05. The observed PR-AUC difference between the models was found to be significant for IHM and statistically insignificant for LOS at p=0.05. In this study, deep learning models were constructed using data combined from a variety of sources in Electronic Health Records (EHRs) such as chart data, input and output events, laboratory values, microbiology events, procedures, notes, and prescriptions. It is possible to predict in-hospital mortality with much better confidence and higher reliability from models built using all sources of data.

Sonam Chauhan, Ajay Sharma

The conventional commitment scheme requires both commitment string and a valid key for the sender to verify his commitment. Differ from the conventional commitment scheme; fuzzy commitment scheme accepts the key that is similar to the original key. The new opening key, not identical to the original key, differs from the initial key in some suitable metrics. The fuzziness in the fuzzy commitment scheme tolerate small amount of corruptions. The fuzzy commitment scheme based on the cryptographic hash functions suffers security imperfections. Thus, this paper combines the fuzzy commitment scheme with the Reed Solomon error correction codes, which are capable of correcting certain number of errors. As a result, Reed Solomon code proves better alternative for fuzzy commitment scheme than hash functions, as the Reed Solomon codes are more secure than the hashing techniques. Moreover, the Fuzzy Commitment Scheme based on Reed Solomon codes provides security at two levels that making it suitable for securing data. This paper explore the efficiency of executing fuzzy commitment scheme in conjunction with Reed Solomon code as a novel better alternative to the conventional commitment scheme.