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An Invariant Learning Characterization of Controlled Text Generation
Claudia Shi · Carolina Zheng · Keyon Vafa · Amir Feder · David Blei
Event URL: https://openreview.net/forum?id=zInaytkuzX »

Controlled generation refers to the problem of creating text that contains stylistic or semantic attributes of interest. Many approaches reduce this problem to building a predictor of the desired attribute.For example, researchers hoping to deploy a large language model to produce non-toxic content may use a toxicity classifier to filter generated text. In this paper, we show that the performance of controlled generation may be poor if the target distribution of text differs from the distribution the predictor was trained on. Instead, we take inspiration from causal representation learning and cast controlled generation under distribution shift as an invariant learning problem: the most effective predictor should be invariant across multiple text environments. Experiments demonstrate the promise and difficulty of adapting invariant learning methods, which have been primarily developed for vision, to text.

Author Information

Claudia Shi (Columbia University)
Carolina Zheng (Columbia University)
Keyon Vafa (Columbia University)
Amir Feder (Columbia University)
Amir Feder

Amir Feder is a Postdoctoral Research Scientist in the Data Science Institute, working with Professor David Blei on causal inference and natural language processing. His research seeks to develop methods that integrate causality into natural language processing, and use them to build linguistically-informed algorithms for predicting and understanding human behavior. Through the paradigm of causal machine learning, Amir aims to build bridges between machine learning and the social sciences. Before joining Columbia, Amir received his PhD from the Technion, where he was advised by Roi Reichart and worked closely with Uri Shalit. In a previous (academic) life, Amir was an economics, statistics and history student at Tel Aviv University, the Hebrew University of Jerusalem and Northwestern University. Amir was the organizer of the First Workshop on Causal Inference and NLP (CI+NLP) at EMNLP 2021.

David Blei (Columbia University)

David Blei is a Professor of Statistics and Computer Science at Columbia University, and a member of the Columbia Data Science Institute. His research is in statistical machine learning, involving probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference algorithms for massive data. He works on a variety of applications, including text, images, music, social networks, user behavior, and scientific data. David has received several awards for his research, including a Sloan Fellowship (2010), Office of Naval Research Young Investigator Award (2011), Presidential Early Career Award for Scientists and Engineers (2011), Blavatnik Faculty Award (2013), and ACM-Infosys Foundation Award (2013). He is a fellow of the ACM.

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