DECIDER: Rule-Controllable Decoding Strategy for Language Generation

핵심 개념

DECIDER is a rule-controllable decoding strategy inspired by dual-system cognitive theory, aiming to guide language generation towards specific targets in a more human-like manner.

초록

DECIDER introduces a rule-controllable decoding strategy for language generation inspired by dual-system cognitive theory. It focuses on guiding the generation process towards specific targets in a more human-like manner, balancing text quality and target satisfaction. The method aims to follow high-level rules to approach the target rather than focusing solely on the target itself.

Existing approaches often lack high-level reasoning, leading to quick task satisfaction but at the cost of overall planning and commonsense reasoning. DECIDER addresses this issue by incorporating logical reasoners and explicit modeling of decision-making processes based on dual-system cognitive theory. By introducing rules that guide pre-trained generative models, DECIDER demonstrates improved performance in balancing text quality and target satisfaction during inference.

The framework consists of three modules: a base pre-trained language model (PLM) for text generation, a logical reasoner for rule interpretation, and a decision function to combine results from both systems. Through extensive experimental results, DECIDER showcases its effectiveness in following given rules to generate higher-quality and target-meeting text across various tasks.

Key points include:

• Introduction of DECIDER as a rule-controllable decoding strategy for language generation.
• Utilization of dual-system cognitive theory to guide the generation process towards specific targets.
• Incorporation of logical reasoners and explicit modeling of decision-making processes.
• Improved performance in balancing text quality and target satisfaction during inference.

통계

Existing methods often need fine-tuning or re-training on large data due to domain adaptation.
Pre-trained language models are popular for developing decoding methods that guide models toward target keywords.
Dual Processing Theory explains existing constrained decoding works.
Proposed method makes improvements with logic reasoners implementing high-level rules programmable for different tasks.
Extensive experimental results demonstrate DECIDER's effectiveness in guiding generation direction towards targets.

인용구

"DECIDER allows rule signals to flow into the PLM at each decoding step."
"DECIDER is a general framework that allows users to program any rules according to different tasks."

핵심 통찰 요약

DECIDER

by Chen Xu,Tian... 게시일 arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01954.pdf

더 깊은 질문

How does DECIDER compare with traditional unconstrained decoding methods

DECIDER outperforms traditional unconstrained decoding methods in both tasks of constrained language generation. In the CommonGen task, DECIDER achieved higher scores across various metrics compared to methods like Top-k Sampling and Beam Search. Similarly, in the PersonaChat task, DECIDER surpassed canonical decoding methods like Beam Search in terms of BLEU, NIST, ROUGE, F1 score, and BERT-score. The key difference lies in how DECIDER incorporates high-level rules to guide the generation process towards specific targets or personas while maintaining text quality.

What are the implications of using dual-system cognitive theory in language generation

The implications of using dual-system cognitive theory in language generation are significant. By imitating the interplay between System 1 (intuitive) and System 2 (logical) decision-making processes observed in humans, DECIDER introduces a more human-like reasoning approach to generating text. This allows for a balance between following explicit rules or constraints (System 2) and leveraging intuitive word predictions from pre-trained models (System 1). By incorporating this dual-system framework into language generation models like PLMs, it enables more controlled and contextually relevant output.

How can DECIDER be applied beyond the specific tasks mentioned in the content

DECIDER can be applied beyond the specific tasks mentioned in the content by adapting its rule-controllable decoding strategy to various other scenarios requiring controllable text generation. Some potential applications include sentiment-controlled text generation where emotions or tones need to be reflected accurately based on given cues; topic-guided responses where generated content aligns with specified themes or subjects; and style imitation tasks where writing styles or genres can be emulated through predefined rules. Additionally, DECIDER could be utilized for personalized recommendation systems that tailor responses based on user profiles or preferences by integrating persona-based constraints similar to those used in PersonaChat experiments.