核心概念
The core message of this paper is to introduce a novel causal estimand, Incremental Stylistic Effect (ISE), to evaluate the impact of various text editing strategies in dynamic human-language model collaborations, and to propose the CausalCollab algorithm to effectively estimate ISE from observational data.
摘要
This paper examines the collaborative dynamics between humans and language models (LMs), where the interactions typically involve LMs proposing text segments and humans editing or responding to these proposals. The authors frame this as a causal inference problem, driven by the counterfactual question: how would the outcome of collaboration change if humans employed a different text editing/refinement strategy?
The key challenges addressed in this work are:
- Formulating an appropriate causal estimand: The conventional average treatment effect (ATE) estimand is inapplicable due to the high dimensionality of text-based treatments.
- Proposing a novel causal estimand - Incremental Stylistic Effect (ISE): ISE characterizes the average impact of infinitesimally shifting a text towards a specific style, such as increasing formality. This addresses the limitations of ATE.
- Developing CausalCollab, an algorithm to estimate the ISE of various interaction strategies in dynamic human-LM collaborations.
The authors establish the theoretical conditions for non-parametric identification of ISE and demonstrate the effectiveness of CausalCollab through empirical studies across three distinct human-LM collaboration scenarios. The results show that CausalCollab significantly improves counterfactual estimation over competitive baselines by mitigating confounding factors.
The key insights from the qualitative analysis reveal that the CVAE model in CausalCollab is able to learn explainable human strategies according to the outcomes of the task, such as identifying words that contribute to increasing formality in the CoAuthor dataset.
統計資料
The paper does not contain any explicit numerical data or statistics. The focus is on developing a causal inference framework for human-language model collaborations.
引述
"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."
"Applying editing strategies from past successful collaborations may not always be effective, since the success of these strategies could be confounded by specific prompt setups."
"Numerous word sequences either fail to form coherent sentences or are implausible as human edits, resulting in some configurations having a zero probability of occurring."