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2023 Mar April 2024 May 2025

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14	15 Matthew Wipperman (Regeneron) 12:00 pm Matthew Wipperman (Regeneron) @ Hackerman Hall B17 Apr 15 @ 12:00 pm – 1:15 pm Tickets Abstract Biomarkers can be used for a variety of purposes in clinical research and drug development, and while molecular and imaging biomarkers have utility in such research, digital biomarkers are increasingly being used to assess[...]	16	17	18	19	20
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2023 Mar April 2024 May 2025

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Abstract
The rapidity with which children acquire language is one of the mysteries of human cognition. A widely held view is that children master language by means of a language-specific learning device. An earlier proposal, generating renewed interest, is that children make use of domain-general, associative learning mechanisms in acquiring language. However, we know little about the actual learning mechanisms involved, making it difficult to determine the relative contributions of innate and acquired knowledge. A recent approach to studying this problem exposes infants to artificial languages and assesses the resulting learning. Studies using this paradigm have led to a number of exciting discoveries regarding the learning mechanisms available during infancy. This approach is useful for achieving finer-grained characterizations of infant learning mechanisms than previously imagined. Such characterizations should result in a better understanding of the relative contributions of innate and learned factors to language acquisition, as well as the dynamic between the two. I will present several studies using this methodology and will discuss the implications for the study of language acquisition.

Artificial Language Learning and Language Acquisition – Rebecca Gomez (JHU) - 2000

ICLR 2024
https://arxiv.org/abs/2310.00840

Abstract:
Text generation models are notoriously vulnerable to errors in the training data. With the wide-spread availability of massive amounts of web-crawled data becoming more commonplace, how can we enhance the robustness of models trained on a massive amount of noisy web-crawled text? In our work, we propose Error Norm Truncation (ENT), a robust enhancement method to the standard training objective that truncates noisy data. Compared to methods that only uses the negative log-likelihood loss to estimate data quality, our method provides a more accurate estimation by considering the distribution of non-target tokens, which is often overlooked by previous work. Through comprehensive experiments across language modeling, machine translation, and text summarization, we show that equipping text generation models with ENT improves generation quality over standard training and previous soft and hard truncation methods. Furthermore, we show that our method improves the robustness of models against two of the most detrimental types of noise in machine translation, resulting in an increase of more than 2 BLEU points over the MLE baseline when up to 50% of noise is added to the data.

Error Norm Truncation: Robust Training in the Presence of Data Noise for Text Generation Models

Eric Fosler-Lussier: Speech Recognition - 2000

Lecture given as part of CS 601.471/671 NLP: Self-supervised Models:
https://self-supervised.cs.jhu.edu/sp2024/

Speaker: Tianjian Li:
https://tianjianl.github.io/

Tutorial: Efficient language models -- Tianjian Li (JHU)

Abstract: High-quality datasets are crucial for improving the capabilities and training efficiency of large language models. However, current datasets are typically prepared in an ad hoc, heuristic way. In this talk, Sang Michael Xie will present principled approaches to improving and understanding language models centered on the pre-training data distribution. First, he will describe how to improve the efficiency of training multipurpose language models by optimizing the mixture of data sources with robust optimization. Second, he will discuss an efficient importance resampling method for selecting relevant data from trillion-token-scale web datasets for training a specialized model. Finally, he will introduce a first theoretical analysis of in-context learning, a key capability of language models to learn from examples in a textual prompt, that traces the capability back to modeling coherence structure in the pre-training data.

Speaker Biography: Sang Michael Xie is a computer science PhD student at Stanford University advised by Percy Liang and Tengyu Ma. His research focuses on data-centric machine learning for language models, understanding pre-training and adaptation, and pre-training and self-training methods for robust machine learning. Xie was awarded a NDSEG Fellowship and was previously a student researcher at Google Brain. His work has been recognized as one of Scientific American‘s World-Changing Ideas, published in flagship venues such as Science, and covered by media outlets including The New York Times, The Washington Post, Reuters, BBC News, IEEE Spectrum, and The Verge.

Data-distributional Approaches for Generalizable Language Models -- Sang Michael Xie (Stanford)

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