Summary of From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification, by Fei Wang et al.
From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification
by Fei Wang, Chao Shang, Sarthak Jain, Shuai Wang, Qiang Ning, Bonan Min, Vittorio Castelli, Yassine Benajiba, Dan Roth
First submitted to arxiv on: 10 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel framework called ACT (Aligning to ConsTraints) for adapting general-purpose language models to downstream tasks with customized constraints. The authors observe that user instructions often contain constraints, which can be challenging to evaluate due to the high cost of assessing response quality. Instead, they propose efficiently evaluating the satisfaction rate of constraints using constraint verifiers. ACT samples multiple responses and collects preference labels based on their constraint satisfaction rate (CSR). The framework adapts language models through a ranking-based learning process, improving task performance in fine-grained entity typing, abstractive summarization, and temporal question answering. The authors demonstrate that the constraint-following capabilities are transferable across tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to get a computer to do something specific, like give you information about a certain topic or answer a particular type of question. This paper is about making computers better at following instructions and doing what you want them to do. The problem is that instructions often have rules or constraints that need to be followed, but it’s hard to tell if the computer is doing what you want it to do. So, researchers came up with a new way to teach computers how to follow these rules. It involves giving the computer lots of examples and feedback about what it did right or wrong. This helps the computer learn how to follow the rules better over time. The paper shows that this approach works well for different tasks like summarizing texts and answering questions. |
Keywords
* Artificial intelligence * Question answering * Summarization
