UltraIF: Advancing Instruction Following from the Wild
Instruction-following made modern large language models (LLMs) helpful
assistants. However, the key to taming LLMs on complex instructions remains
mysterious, for that there are huge gaps between models trained by open-source
community and those trained by leading companies. To bridge the gap, we propose
a simple and scalable approach UltraIF for building LLMs that can follow
complex instructions with open-source data. UltraIF first decomposes real-world
user prompts into simpler queries, constraints, and corresponding evaluation
questions for the constraints. Then, we train an UltraComposer to compose
constraint-associated prompts with evaluation questions. This prompt composer
allows us to synthesize complicated instructions as well as filter responses
with evaluation questions. In our experiment, for the first time, we
successfully align LLaMA-3.1-8B-Base to catch up with its instruct version on 5
instruction-following benchmarks without any benchmark information, using only
8B model as response generator and evaluator. The aligned model also achieved
competitive scores on other benchmarks. Moreover, we also show that UltraIF
could further improve LLaMA-3.1-8B-Instruct through self-alignment, motivating
broader use cases for the method. Our code will be available at
https://github.com/kkk-an/UltraIF.
67a57b1fdea89ffe80d9fe56
Kaikai An
claimed_verified
2025-02-07T09:58:18.320Z
66c89152d33e34fbc29497d7
/avatars/bbddabf6532393951c4759e5915a065b.svg
KaikaiAn
user
kkk-an
67a57b1fdea89ffe80d9fe57
Li Sheng
claimed_verified
2025-02-14T11:10:55.558Z
65c874daa3ea4f6d8df75dd1
https://cdn-avatars.huggingface.co/v1/production/uploads/65c874daa3ea4f6d8df75dd1/k-2Ng8pZvEwu6IcY9omYo.jpeg
li sheng
user
bambisheng
67a57b1fdea89ffe80d9fe58
Ganqu Cui
admin_assigned
2025-02-07T10:31:05.333Z
650eba9555dc1e841746f132
/avatars/af6f5ee78f161d25ec0afc45d2def8eb.svg
Ganqu Cui
user
ganqu
67a57b1fdea89ffe80d9fe59
Shuzheng Si
claimed_verified
2025-02-07T09:58:16.229Z
637c99bbfe115289cfedfb44
https://cdn-avatars.huggingface.co/v1/production/uploads/637c99bbfe115289cfedfb44/344NN9KKF_XXTlVYaGaMW.png
ssz
user
ssz1111
67a57b1fdea89ffe80d9fe5a
Ning Ding
67a57b1fdea89ffe80d9fe5b
Yu Cheng
claimed_verified
2025-06-02T07:53:56.027Z
67017abfe4d49b157ac534d9
/avatars/997e1b9f54b27a7728a9d4abfee4ba91.svg
Yu Cheng
user
ych133
67a57b1fdea89ffe80d9fe5c
Baobao Chang
UltraIF: Advancing Instruction Following from the Wild
Instruction-following made modern large language models (LLMs) helpful
assistants. However, the key to taming LLMs on complex instructions remains
mysterious, for that there are huge gaps between models trained by open-source
community and those trained by leading companies. To bridge the gap, we propose
a simple and scalable approach UltraIF for building LLMs that can follow
complex instructions with open-source data. UltraIF first decomposes real-world
user prompts into simpler queries, constraints, and corresponding evaluation
questions for the constraints. Then, we train an UltraComposer to compose
constraint-associated prompts with evaluation questions. This prompt composer
allows us to synthesize complicated instructions as well as filter responses
with evaluation questions. In our experiment, for the first time, we
successfully align LLaMA-3.1-8B-Base to catch up with its instruct version on 5
instruction-following benchmarks without any benchmark information, using only
8B model as response generator and evaluator. The aligned model also achieved
competitive scores on other benchmarks. Moreover, we also show that UltraIF
could further improve LLaMA-3.1-8B-Instruct through self-alignment, motivating
broader use cases for the method. Our code will be available at
https://github.com/kkk-an/UltraIF.
UltraIF enhances open-source LLMs' instruction-following capabilities by decomposing complex prompts and using evaluation questions to filter responses, achieving competitive scores on benchmarks.
UltraIF UltraComposer instruction-following prompts constraints evaluation questions LLaMA-3.1-8B-Base LLaMA-3.1-8B-Instruct self-alignment