User alignment is crucial for adapting general-purpose language models (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.

使用者对齐是将通用语言模型（LMs）调整为下游任务所必不可少的，但人工注释往往无法涵盖所有类型的指令，尤其是具有定制约束的指令。本研究提出了一种统一框架 ACT（Aligning to ConsTraints），通过约束验证器自动产生用户对齐的监督信号，从而有效评估不同类别约束的满足率，并通过基于排序的学习过程来适应目标任务，实验证明 ACT 能够提高 LMs 对各种约束的遵循能力，从而改善任务性能，并且该约束遵循能力是可迁移的。

从指令到约束：与自动约束验证的语言模型对齐

From Instructions to Constraints: Language Model Alignment with  Automatic Constraint Verification

An important aspect of developing LLMs that interact with humans is to align
models' behavior to their users. It is possible to prompt an LLM into behaving
as a certain persona, especially a user group or ideological persona the model
captured during its pertaining stage. But, how to best align an LLM with a
specific user and not a demographic or ideological group remains an open
question. Mining public opinion surveys (by Pew Research), we find that the
opinions of a user and their demographics and ideologies are not mutual
predictors. We use this insight to align LLMs by modeling both user opinions as
well as user demographics and ideology, achieving up to 7 points accuracy gains
in predicting public opinions from survey questions across a broad set of
topics. In addition to the typical approach of prompting LLMs with demographics
and ideology, we discover that utilizing the most relevant past opinions from
individual users enables the model to predict user opinions more accurately.

通过挖掘公众意见调查的数据，结合用户观点、人口统计学和意识形态信息来对 LLMs 进行定位，从而实现更好地预测各种话题下的公众观点，同时发现利用个体用户的相关过往意见可以提高预测准确性。