Language Models can Self-Lengthen to Generate Long Texts
Recent advancements in Large Language Models (LLMs) have significantly
enhanced their ability to process long contexts, yet a notable gap remains in
generating long, aligned outputs. This limitation stems from a training gap
where pre-training lacks effective instructions for long-text generation, and
post-training data primarily consists of short query-response pairs. Current
approaches, such as instruction backtranslation and behavior imitation, face
challenges including data quality, copyright issues, and constraints on
proprietary model usage. In this paper, we introduce an innovative iterative
training framework called Self-Lengthen that leverages only the intrinsic
knowledge and skills of LLMs without the need for auxiliary data or proprietary
models. The framework consists of two roles: the Generator and the Extender.
The Generator produces the initial response, which is then split and expanded
by the Extender. This process results in a new, longer response, which is used
to train both the Generator and the Extender iteratively. Through this process,
the models are progressively trained to handle increasingly longer responses.
Experiments on benchmarks and human evaluations show that Self-Lengthen
outperforms existing methods in long-text generation, when applied to top
open-source LLMs such as Qwen2 and LLaMA3. Our code is publicly available at
https://github.com/QwenLM/Self-Lengthen.
67242c98d26ffc2cbd545d21
Shanghaoran Quan
claimed_verified
2024-11-03T14:22:34.065Z
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Shanghaoran Quan
user
quanshr
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Tianyi Tang
67242c98d26ffc2cbd545d23
Bowen Yu
67242c98d26ffc2cbd545d24
An Yang
claimed_verified
2025-05-19T06:43:14.449Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1644733028938-62088594a5943c8a8fc94560.png
An Yang
user
yangapku
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Dayiheng Liu
claimed_verified
2024-12-20T08:34:13.388Z
6434d4989bd5a84b5dd0b0f5
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Dayiheng Liu
user
Losin94
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Bofei Gao
claimed_verified
2024-11-15T09:38:53.850Z
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Bofei Gao
user
KbsdJames
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Jianhong Tu
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Yichang Zhang
67242c98d26ffc2cbd545d29
Jingren Zhou
67242c98d26ffc2cbd545d2a
Junyang Lin
Language Models can Self-Lengthen to Generate Long Texts
Recent advancements in Large Language Models (LLMs) have significantly
enhanced their ability to process long contexts, yet a notable gap remains in
generating long, aligned outputs. This limitation stems from a training gap
where pre-training lacks effective instructions for long-text generation, and
post-training data primarily consists of short query-response pairs. Current
approaches, such as instruction backtranslation and behavior imitation, face
challenges including data quality, copyright issues, and constraints on
proprietary model usage. In this paper, we introduce an innovative iterative
training framework called Self-Lengthen that leverages only the intrinsic
knowledge and skills of LLMs without the need for auxiliary data or proprietary
models. The framework consists of two roles: the Generator and the Extender.
The Generator produces the initial response, which is then split and expanded
by the Extender. This process results in a new, longer response, which is used
to train both the Generator and the Extender iteratively. Through this process,
the models are progressively trained to handle increasingly longer responses.
Experiments on benchmarks and human evaluations show that Self-Lengthen
outperforms existing methods in long-text generation, when applied to top
open-source LLMs such as Qwen2 and LLaMA3. Our code is publicly available at
https://github.com/QwenLM/Self-Lengthen.
The Self-Lengthen framework iteratively trains LLMs to generate longer, coherent outputs using only intrinsic model capabilities without requiring additional data or proprietary models.
Large Language Models LLMs long contexts long-text generation pre-training instruction backtranslation behavior imitation Self-Lengthen iterative training Generator Extender