IMPOSSIBLE DISTILLATION: Paraphrasing and Summarization Framework

核心概念

IMPOSSIBLE DISTILLATION distills high-quality paraphrase datasets and models from low-quality LMs using paraphrastic proximity and critic-guided filtering.

摘要

IMPOSSIBLE DISTILLATION introduces a novel framework for paraphrasing and sentence summarization.
The framework leverages paraphrastic proximity intrinsic to pre-trained LMs like GPT2 to distill high-quality datasets and models.
By identifying generations from proximal subspaces in LM distributions, the method outperforms strong baselines in multiple benchmarks.
The distilled dataset exhibits higher diversity and fidelity compared to larger datasets like ParaBank or ChatGPT-Para.
The process involves pair generation, filtering with critics, student model distillation, self-distillation, controllability enhancement, domain-specific testing, and generalization to sentence summarization.

統計資料

IMPOSSIBLE DISTILLATION produces a high-quality dataset even from GPT2-scale LMs.
Our model with 770M parameters consistently outperforms strong baselines in multiple benchmarks.
The distilled dataset from 1.5B LMs shows better metrics than state-of-the-art datasets like ParaBank or ChatGPT-Para.

引述

從以下內容提煉的關鍵洞見

Impossible Distillation

by Jaehun Jung,... 於 arxiv.org 03-20-2024

https://arxiv.org/pdf/2305.16635.pdf

深入探究

How does the concept of "paraphrastic proximity" impact the effectiveness of IMPOSSIBLE DISTILLATION

"Paraphrastic proximity" plays a crucial role in the effectiveness of IMPOSSIBLE DISTILLATION by leveraging the tendency of language models (LMs) to encode paraphrases in close proximity within their distribution. This concept allows the framework to identify and distill generations from these proximal subspaces, enabling the generation of high-quality datasets and models even from small, low-quality LMs like GPT2. By constraining the LM search space towards these paraphrastic subspaces through informative context and filtering with critics, IMPOSSIBLE DISTILLATION can encourage the model to produce multiple sequences that paraphrase each other effectively.

What are the implications of using off-the-shelf LMs for distilling high-quality datasets compared to extreme-scale teacher models

Using off-the-shelf LMs for distilling high-quality datasets offers several implications compared to extreme-scale teacher models. Firstly, it reduces reliance on large-scale models like GPT3, making it more cost-effective and accessible for researchers who may not have access to such resources. Additionally, by utilizing pre-trained LMs like GPT2 as teachers, IMPOSSIBLE DISTILLATION demonstrates that effective data distillation can be achieved without requiring extreme-scale models or human-authored references. This approach showcases the potential for smaller LMs to perform complex tasks when guided appropriately through techniques like paraphrastic proximity and critic-guided filtering.

How can the framework of IMPOSSIBLE DISTILLATION be applied to other NLP tasks beyond paraphrasing and summarization

The framework of IMPOSSIBLE DISTILLATION can be applied beyond paraphrasing and summarization tasks to various other NLP tasks by adapting its methodology based on task requirements. For instance:

Text Generation: The framework could be modified for generating diverse text outputs by adjusting filters based on criteria specific to text generation.
Machine Translation: By incorporating bilingual constraints into pair generation and filtering processes, IMPOSSIBLE DISTILLATION could be tailored for unsupervised machine translation.
Sentiment Analysis: Adapting critics in the framework could focus on sentiment preservation between input-output pairs for sentiment analysis tasks.
By customizing contextual constraints, filters, and training procedures according to different NLP tasks' characteristics, IMPOSSIBLE DISTILLATION has the flexibility to address a wide range of natural language processing challenges effectively.