Videos
See AI2's full collection of videos on our YouTube channel.Viewing 11-20 of 251 videos
Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing
November 2, 2023 | Tom SherborneAbstract: Cross-lingual semantic parsing transfers parsing capability from a high-resource language (e.g., English) to low-resource languages with scarce training data. Previous work has primarily considered silver-standard data augmentation or zero-shot methods, however, exploiting few-shot gold data is…
On Parameter Efficiency of Neural Language Models
November 2, 2023 | Chen LiangAbstract: Pre-trained neural language models have demonstrated remarkable generalizability in various downstream tasks, such as natural language understanding and question answering. However, these models have grown to contain hundreds of billions of parameters, making them difficult to be deployed in…
Benchmarking Compositionality with Formal Languages
November 1, 2023 | Josef ValvodaAbstract: Recombining known primitive concepts into larger novel combinations is a quintessentially human cognitive capability. Whether large neural models in NLP can acquire this ability while learning from data is an open question. In this paper, we investigate this problem from the perspective of formal…
Studying Large Language Model Generalization with Influence Functions
October 31, 2023 | Roger GrosseAbstract: When trying to gain better visibility into a machine learning model in order to understand and mitigate the associated risks, a potentially valuable source of evidence is: which training examples most contribute to a given behavior? Influence functions aim to answer a counterfactual: how would the…
Modular Language Models
October 16, 2023 | Suchin GururanganConventional language models (LMs) are trained densely: all parameters are updated with respect to all data. We argue that dense training leads to a variety of well-documented issues with LMs, including their prohibitive training cost and unreliable downstream behavior. We then introduce a new class of LMs that…
Towards Cost-Efficient Use of Pre-trained Models
October 10, 2023 | Alan RitterAbstract: Large language models are leading to many exciting breakthroughs, but this comes at a significant cost in terms of both computational and data labeling expenses. Training state-of-the-art models requires access to high-end GPUs for pre-training and inference, in addition to labeled data for fine-tuning…
Reliability and interactive debugging for language models
October 6, 2023 | Bhargavi ParanjapeAbstract: Large language models have permeated our everyday lives and are used in critical decision making scenarios that can affect millions of people. Despite their impressive progress, model deficiencies may result in exacerbating harmful biases or lead to catastrophic failures. In this talk, I discuss several…
The University of Washington eScience Institute: a Home for Data-Intensive Discovery
September 27, 2023 | Sarah Stone, Executive Director, eScience Institute, University of WashingtonAbstract: The University of Washington eScience Institute, one of the nation's first university data science institutes, grew out of the Moore-Sloan Data Science Environment effort which was focused on identifying and tackling impediments to the broad and sustainable adoption of data-intensive discovery. With a…
Reliable Evaluation and High-Quality Data: Building Blocks for Helpful Question Answering Systems
September 26, 2023 | Ehsan KamallooAbstract: As models continue to rapidly evolve in complexity and scale, the status quo of how they are being evaluated and the quality of benchmarks has not significantly changed. This inertia leaves challenges in evaluation and data quality unaddressed, which results in the potential for erroneous conclusions…
Vision Without Labels
September 13, 2023 | Bharath Hariharan/Cornell UniversityBio: Bharath Hariharan is an assistant professor at Cornell University. He works on problems in computer vision and machine learning that defy the big data label. He did his PhD at University of California, Berkeley with Jitendra Malik. His work has been recognized with an NSF CAREER and a PAMI Young Researcher…