Jason Eisner

About

Jason Eisner is the John C. Malone Professor of Computer Science at Johns Hopkins University. The page does not provide specific details about his research focus, background, or key contributions. The content primarily consists of a humorous song and personal notes related to the department's move to Malone Hall, with no explicit biographical or research information included.

Research topics

• Computer Science
• Artificial Intelligence
• Natural Language Processing
• Speech recognition
• Chemistry
• Physics
• Philosophy
• Linguistics

Selected publications

• Learning How to Ask: Querying LMs with Mixtures of Soft Prompts

  Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · 2021 · 316 citations

  Senior authorCorresponding

  Natural-language prompts have recently been used to coax pretrained language models into performing other AI tasks, using a fill-in-theblank paradigm For example, language models retain factual knowledge from their training corpora that can be extracted by asking them to "fill in the blank" in a sentential prompt. However, where does this prompt come from? We explore the idea of learning prompts by gradient descent-either fine-tuning prompts taken from previous work, or starting from random init…

  DOI

• LLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts

  2024-01-01 · 28 citations

  preprintOpen access

  This paper introduces a framework for the automated evaluation of natural language texts.A manually constructed rubric describes how to assess multiple dimensions of interest.To evaluate a text, a large language model (LLM) is prompted with each rubric question and produces a distribution over potential responses.The LLM predictions often fail to agree well with human judges-indeed, the humans do not fully agree with one another.However, the multiple LLM distributions can be combined to predict…

  PublisherOA PDFDOI

• LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error

  2024-01-01 · 10 citations

  articleOpen access

  Tools are essential for large language models (LLMs) to acquire up-to-date information and take consequential actions in external environments.Existing work on tool-augmented LLMs primarily focuses on the broad coverage of tools and the flexibility of adding new tools.However, a critical aspect that has surprisingly been understudied is simply how accurately an LLM uses tools for which it has been trained.We find that existing LLMs, including GPT-4 and open-source LLMs specifically fine-tuned fo…

  PublisherOA PDFDOI

• Learning to Retrieve Iteratively for In-Context Learning

  2024-01-01 · 2 citations

  articleOpen access
  PublisherOA PDFDOI

• Fast Controlled Generation from Language Models with Adaptive Weighted Rejection Sampling

  ArXiv.org · 2025-04-07 · 1 citations

  preprintOpen access

  The dominant approach to generating from language models subject to some constraint is locally constrained decoding (LCD), incrementally sampling tokens at each time step such that the constraint is never violated. Typically, this is achieved through token masking: looping over the vocabulary and excluding non-conforming tokens. There are two important problems with this approach. (i) Evaluating the constraint on every token can be prohibitively expensive -- LM vocabularies often exceed $100,000…

  PublisherOA PDFDOI

Recent grants

• CAREER: Finite-State Machine Learning on Strings and Sequences

  NSF · $500k · 2004–2010

• RI: Medium: Learned Dynamic Prioritization

  NSF · $900k · 2010–2014

• ITR: Weighted Dynamic Programming for Statistical Natural Language Processing

  NSF · $425k · 2003–2007

Frequent coauthors

• Ryan Cotterell

  73 shared

• Tim Vieira

  23 shared

• Benjamin Van Durme

  19 shared

• Matthew R. Gormley

  Broad Center

  18 shared

• Luc De Raedt

  KU Leuven

  18 shared

• Christo Kirov

  17 shared

• Mans Hulden

  15 shared

• Chu‐Cheng Lin

  15 shared

Labs

• Jason Eisner LabPI

Awards & honors

• Fellow of the Association for Computational Linguistics

Similar researchers at Johns Hopkins University

• Colin Wilsonh-71
• Brice Ménardh-55