Paramveer S. Dhillon

Also published as: Paramveer Dhillon


2024

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Causal Inference for Human-Language Model Collaboration
Bohan Zhang | Yixin Wang | Paramveer Dhillon
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

In this paper, we examine the collaborative dynamics between humansand language models (LMs), where the interactions typically involveLMs proposing text segments and humans editing or responding to theseproposals. Productive engagement with LMs in such scenarios necessitates that humans discern effective text-based interaction strategies, such as editing and response styles, from historical human-LM interactions. This objective is inherently causal, driven by the counterfactual ‘what-if’ question: how would the outcome of collaboration change if humans employed a different text editing/refinement strategy? A key challenge in answering this causal inference question is formulating an appropriate causal estimand: the conventional average treatment effect (ATE) estimand is inapplicable to text-based treatments due to their high dimensionality. To address this concern, we introduce a new causal estimand– *Incremental Stylistic Effect (ISE)*, which characterizes the average impact of infinitesimally shifting a text towards a specific style, such as increasing formality. We establish the conditions for the non-parametric identification of ISE. Building on this, we develop *CausalCollab*, an algorithm designed to estimate the ISE of various interaction strategies in dynamic human-LM collaborations. Our empirical investigations across three distinct human-LM collaboration scenarios reveal that *CausalCollab* effectively reduces confounding and significantly improves counterfactual estimation over a set of competitive baselines.

2015

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Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing
Phil Blunsom | Shay Cohen | Paramveer Dhillon | Percy Liang
Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing

2012

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Spectral Dependency Parsing with Latent Variables
Paramveer Dhillon | Jordan Rodu | Michael Collins | Dean Foster | Lyle Ungar
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Metric Learning for Graph-Based Domain Adaptation
Paramveer Dhillon | Partha Talukdar | Koby Crammer
Proceedings of COLING 2012: Posters

2010

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Learning Better Data Representation Using Inference-Driven Metric Learning
Paramveer S. Dhillon | Partha Pratim Talukdar | Koby Crammer
Proceedings of the ACL 2010 Conference Short Papers

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A New Approach to Lexical Disambiguation of Arabic Text
Rushin Shah | Paramveer S. Dhillon | Mark Liberman | Dean Foster | Mohamed Maamouri | Lyle Ungar
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

2009

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Transfer Learning, Feature Selection and Word Sense Disambiguation
Paramveer S. Dhillon | Lyle H. Ungar
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers