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From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification (2403.06326v1)

Published 10 Mar 2024 in cs.CL, cs.AI, and cs.LG

Abstract: User alignment is crucial for adapting general-purpose LMs to downstream tasks, but human annotations are often not available for all types of instructions, especially those with customized constraints. We observe that user instructions typically contain constraints. While assessing response quality in terms of the whole instruction is often costly, efficiently evaluating the satisfaction rate of constraints is feasible. We investigate common constraints in NLP tasks, categorize them into three classes based on the types of their arguments, and propose a unified framework, ACT (Aligning to ConsTraints), to automatically produce supervision signals for user alignment with constraints. Specifically, ACT uses constraint verifiers, which are typically easy to implement in practice, to compute constraint satisfaction rate (CSR) of each response. It samples multiple responses for each prompt and collect preference labels based on their CSR automatically. Subsequently, ACT adapts the LM to the target task through a ranking-based learning process. Experiments on fine-grained entity typing, abstractive summarization, and temporal question answering show that ACT is able to enhance LMs' capability to adhere to different classes of constraints, thereby improving task performance. Further experiments show that the constraint-following capabilities are transferable.

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Citations (6)
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Summary

  • The paper presents a novel framework using automatic constraint verification to align language model outputs with specific user-defined requirements.
  • It categorizes constraints into three types and utilizes verifiers to generate high-quality supervision signals that reduce the dependency on extensive human annotations.
  • Empirical results demonstrate competitive performance across diverse NLP tasks, confirming the method’s effective constraint transferability and adaptability.

Enhancing LLM Alignment with Automatic Constraint Verification

Introduction to Automatic Constraint Verification for LMs

LLMs (LMs) are pivotal in understanding and generating human-like responses. A central challenge in leveraging LMs across varied tasks involves aligning them closely with user-defined instructions, which often encompass explicit or implicit constraints. However, the scarcity of human annotations for every nuanced instruction makes this alignment task particularly challenging. Addressing this, a novel paper introduces a comprehensive approach through automatic verification of constraints embedded within user instructions, termed as \MODELFULL (\MODEL). This technique promises an efficient and scalable method to adapt LMs to customized constraints with minimal human supervision.

Constraint-Based LM Alignment Framework

The paper proposes a unified framework focusing on three types of constraints based on their argument characteristics: constraints defined on the response, constraints involving both the prompt and response, and those concerning multiple prompt-response pairs. \MODEL utilizes automatic constraint verifiers to assess the compliance of LM responses to these constraints and leverages this feedback to align LMs accordingly.

Automatic Constraint Verifiers

A core component of \MODEL is the automatic constraint verifiers, which evaluate the adherence of LM responses to the specified constraints. These verifiers are typically straightforward to implement and can be rule-based, involving simple comparisons or model-based metrics. They provide a Constraint Satisfaction Rate (CSR) that quantifies the degree to which responses fulfill the constraints, serving as a proxy for alignment supervision.

Efficient Data Annotation and Alignment

\MODEL advocates for a shift from treating instructions as indivisible wholes to focusing on their constituent constraints for efficient data annotation. By automatically evaluating constraint satisfaction, the framework generates high-quality supervision signals for LM alignment, significantly reducing the need for exhaustive human annotations. This approach not only streamlines the alignment process but also captures vital user intent nuances through constraints, offering a targeted adaptation of LMs to task-specific requirements.

Empirical Validation across Diverse NLP Tasks

The effectiveness of \MODEL is empirically demonstrated across several NLP tasks, each associated with distinct types of constraints. The tasks include fine-grained entity typing, abstractive summarization, and temporal question answering, revealing how \MODEL successfully adapts LMs to comply with task-specific constraints. Notably, the experiments showcase that even with minimal or no labeled data, \MODEL achieves comparable performance to traditional fine-tuning methods, substantiating the efficacy of automatic constraint verification in enhancing LMs' task alignment.

Exploring Constraint Transferability

A notable aspect of this paper is its investigation into the transferability of constraint-following capabilities. The researchers conduct a pilot paper on tasks sharing an extractiveness constraint to examine whether LMs can generalize learned constraints across different tasks. The findings indicate a positive transfer effect, highlighting the potential of developing general constraint-following models for a more efficient and versatile adaptation of LMs across a broad spectrum of applications.

Implications and Future Directions

The introduction of \MODEL opens up new avenues in the domain of LLM alignment, underscoring the significance of constraints in refining LMs to meet specific user requirements. This framework not only provides a cost-efficient alternative to labor-intensive human annotation but also enhances the adaptability of LMs across varied tasks. Looking forward, this research paves the way for further exploration into the field of constraint-based LM adaptation, particularly in understanding the boundaries of constraint transferability and developing universally applicable models capable of navigating across multiple constraint-defined tasks.

Overall, this paper marks a significant step towards realizing the potential of LMs in catering to the ever-evolving landscape of user-defined tasks, driven by the efficient and effective alignment mechanism of \MODELFULL.

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