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Jerry Huang

jerry.huang@mila.quebec
PhD - Université de Montréal
Supervisor
Sarath Chandar
Research Topics
Deep Learning
Natural Language Processing
Optimization
Recurrent Neural Networks
Representation Learning

Publications

Towards Practical Tool Usage for Continually Learning LLMs
Jerry Huang
Prasanna Parthasarathi
Mehdi Rezagholizadeh
A. Chandar
Large language models (LLMs) show an innate skill for solving language based tasks. But insights have suggested an inability to adjust for i… (see more)nformation or task-solving skills becoming outdated, as their knowledge, stored directly within their parameters, remains static in time. Tool use helps by offloading work to systems that the LLM can access through an interface, but LLMs that use them still must adapt to nonstationary environments for prolonged use, as new tools can emerge and existing tools can change. Nevertheless, tools require less specialized knowledge, therefore we hypothesize they are better suited for continual learning (CL) as they rely less on parametric memory for solving tasks and instead focus on learning when to apply pre-defined tools. To verify this, we develop a synthetic benchmark and follow this by aggregating existing NLP tasks to form a more realistic testing scenario. While we demonstrate scaling model size is not a solution, regardless of tool usage, continual learning techniques can enable tool LLMs to both adapt faster while forgetting less, highlighting their potential as continual learners.
2024-04-13
ArXiv (preprint)
doi.org
arxiv.org
Context-Aware Assistant Selection for Improved Inference Acceleration with Large Language Models
Jerry Huang
Prasanna Parthasarathi
Mehdi Rezagholizadeh
A. Chandar
2023-12-31
EMNLP (published)
doi.org
arxiv.org
EpiK-Eval: Evaluation for Language Models as Epistemic Models
Gabriele Prato
Jerry Huang
Prasanna Parthasarathi
Shagun Sodhani
A. Chandar
In the age of artificial intelligence, the role of large language models (LLMs) is becoming increasingly central. Despite their growing prev… (see more)alence, their capacity to consolidate knowledge from different training documents—a crucial ability in numerous applications—remains unexplored. This paper presents the first study examining the capability of LLMs to effectively combine such information within their parameter space. We introduce EpiK-Eval, a novel question-answering benchmark tailored to evaluate LLMs' proficiency in formulating a coherent and consistent knowledge representation from segmented narratives. Evaluations across various LLMs reveal significant weaknesses in this domain. We contend that these shortcomings stem from the intrinsic nature of prevailing training objectives. Consequently, we advocate for refining the approach towards knowledge consolidation, as it harbors the potential to dramatically improve their overall effectiveness and performance. The findings from this study offer insights for developing more robust and reliable LLMs. Our code and benchmark are available at https://github.com/chandar-lab/EpiK-Eval
2023-11-30
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (published)
doi.org
openreview.net