Idée - Language model adaptation - # Proxy-tuning for efficient instruction-following, domain adaptation, and task-specific finetuning

Efficient Customization of Large Language Models through Proxy-Tuning

Q: How might proxy-tuning be extended to enable more fine-grained control over the customization, beyond the single hyperparameter explored in the paper?

Proxy-tuning can be extended to provide more fine-grained control over customization by introducing additional hyperparameters that allow for nuanced adjustments in the steering process. One approach could involve incorporating multiple hyperparameters that govern different aspects of the tuning process. For example, one hyperparameter could control the strength of the contrast between the expert and anti-expert models, while another could regulate the influence of specific types of tokens or token categories in the output distribution. By introducing a suite of hyperparameters, users could have more control over the tuning process, enabling them to tailor the customization to specific requirements or preferences. Additionally, the hyperparameters could be designed to target different aspects of the output distribution, such as promoting certain types of tokens, enhancing stylistic elements, or emphasizing factual accuracy. By allowing users to adjust these hyperparameters independently, proxy-tuning could offer a more granular level of control over the customization process, leading to more precise and targeted modifications in the model's behavior.

Q: What are the potential downsides or limitations of proxy-tuning compared to directly finetuning large language models, and how could these be addressed?

While proxy-tuning offers a lightweight and resource-efficient alternative to direct finetuning of large language models, it also comes with certain limitations. One potential downside is that proxy-tuning may not capture the full complexity of the model's internal representations and may not be as effective in capturing intricate patterns or nuances that direct finetuning can achieve. Additionally, proxy-tuning relies on the predictions of smaller expert models, which may not always capture the full range of behaviors exhibited by the larger base model. To address these limitations, researchers could explore ways to enhance the capabilities of the expert models used in proxy-tuning. This could involve training more sophisticated expert models that better capture the nuances of the base model, incorporating additional data sources or training techniques to improve the performance of the expert models. Furthermore, researchers could investigate ensemble methods that combine multiple expert models to provide a more comprehensive view of the base model's behavior, potentially mitigating the limitations of individual expert models.

Q: Given the finding that proxy-tuning can sometimes outperform direct finetuning on knowledge-intensive tasks, what are the implications for understanding the mechanisms by which language models acquire and retain factual knowledge?

The finding that proxy-tuning can outperform direct finetuning on knowledge-intensive tasks has significant implications for understanding how language models acquire and retain factual knowledge. It suggests that decoding-time guidance, as employed in proxy-tuning, may play a crucial role in preserving and enhancing the model's factual knowledge. By leveraging smaller expert models to steer the larger base model towards more accurate and informative outputs, proxy-tuning demonstrates the importance of real-time adjustments in maintaining the model's knowledge base. This finding underscores the potential of decoding-time algorithms in reinforcing the model's factual accuracy and knowledge retention. It implies that the mechanisms by which language models acquire and retain factual knowledge are not solely dependent on pretraining or direct finetuning but can be further enhanced through dynamic steering during the generation process. Understanding and optimizing these mechanisms could lead to more robust and reliable language models that excel in knowledge-intensive tasks while maintaining factual accuracy and consistency.

Concepts de base

Proxy-tuning is a lightweight decoding-time algorithm that can efficiently customize large pretrained language models without accessing their internal weights, by leveraging small tuned models as "experts" to guide the predictions of the larger base model.

Résumé

The paper introduces proxy-tuning, a lightweight decoding-time algorithm that can efficiently customize large pretrained language models without accessing their internal weights. The key idea is to leverage small tuned models as "experts" to guide the predictions of the larger base model.

The authors first evaluate proxy-tuning for instruction-following tasks, where they use a small 7B-parameter CHAT model as the expert to steer larger 13B and 70B LLAMA2 base models. Proxy-tuning is able to close 91.1% and 88.1% of the performance gap between the base models and their directly tuned CHAT counterparts, respectively, across a range of benchmarks testing knowledge, reasoning, and safety.

The authors then demonstrate the generality of proxy-tuning by applying it to domain adaptation on code, and task-specific finetuning on question-answering and math problems. For code adaptation, proxy-tuning the 13B and 70B base models leads to 17-32% and 6-8% absolute improvements, respectively, over the untuned base models. For task finetuning, proxy-tuning the 70B model achieves 31% absolute improvement on average over the untuned 70B, and 9% over the tuned 7B task model.

The paper also analyzes how proxy-tuning influences the token-level probability distribution, finding that it has the largest impact on promoting reasoning and stylistic tokens. Additionally, the authors show how a hyperparameter can be introduced to the proxy-tuning formula to provide more granular control over the strength of the tuning.

Finally, the authors present a case study of applying proxy-tuning to the truly black-box GPT-3.5 model, in an extremely limited-information setting where only the top 5 log probabilities are available. Using LLAMA2-7B as the expert, they are able to proxy-tune GPT-3.5 for temporal adaptation, improving its accuracy on questions about recent events.

Overall, the paper demonstrates the promise of proxy-tuning as an efficient and effective approach for customizing large language models to diverse user needs and applications, without requiring access to the model's internal weights.

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arxiv.org

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Citations

"Proxy-tuning demonstrates the promise of tuning small LMs for efficient, effective customization of large pretrained LMs through decoding-time guidance."
"Remarkably, we find that proxy-tuning closes 91% of the performance gap between LLAMA2-13B and its directly tuned CHAT version, and 88% of the gap for the 70B model, when evaluated across knowledge, reasoning, and safety benchmarks."
"Proxy-tuning a larger model also consistently outperforms the small tuned expert, indicating that our method combines the benefits of tuning with larger pretraining scale."

Idées clés tirées de

Tuning Language Models by Proxy

by Alisa Liu,Xi... à arxiv.org 04-02-2024

https://arxiv.org/pdf/2401.08565.pdf

Questions plus approfondies

How might proxy-tuning be extended to enable more fine-grained control over the customization, beyond the single hyperparameter explored in the paper?

Proxy-tuning can be extended to provide more fine-grained control over customization by introducing additional hyperparameters that allow for nuanced adjustments in the steering process. One approach could involve incorporating multiple hyperparameters that govern different aspects of the tuning process. For example, one hyperparameter could control the strength of the contrast between the expert and anti-expert models, while another could regulate the influence of specific types of tokens or token categories in the output distribution. By introducing a suite of hyperparameters, users could have more control over the tuning process, enabling them to tailor the customization to specific requirements or preferences.
Additionally, the hyperparameters could be designed to target different aspects of the output distribution, such as promoting certain types of tokens, enhancing stylistic elements, or emphasizing factual accuracy. By allowing users to adjust these hyperparameters independently, proxy-tuning could offer a more granular level of control over the customization process, leading to more precise and targeted modifications in the model's behavior.

What are the potential downsides or limitations of proxy-tuning compared to directly finetuning large language models, and how could these be addressed?

While proxy-tuning offers a lightweight and resource-efficient alternative to direct finetuning of large language models, it also comes with certain limitations. One potential downside is that proxy-tuning may not capture the full complexity of the model's internal representations and may not be as effective in capturing intricate patterns or nuances that direct finetuning can achieve. Additionally, proxy-tuning relies on the predictions of smaller expert models, which may not always capture the full range of behaviors exhibited by the larger base model.
To address these limitations, researchers could explore ways to enhance the capabilities of the expert models used in proxy-tuning. This could involve training more sophisticated expert models that better capture the nuances of the base model, incorporating additional data sources or training techniques to improve the performance of the expert models. Furthermore, researchers could investigate ensemble methods that combine multiple expert models to provide a more comprehensive view of the base model's behavior, potentially mitigating the limitations of individual expert models.

Given the finding that proxy-tuning can sometimes outperform direct finetuning on knowledge-intensive tasks, what are the implications for understanding the mechanisms by which language models acquire and retain factual knowledge?

The finding that proxy-tuning can outperform direct finetuning on knowledge-intensive tasks has significant implications for understanding how language models acquire and retain factual knowledge. It suggests that decoding-time guidance, as employed in proxy-tuning, may play a crucial role in preserving and enhancing the model's factual knowledge. By leveraging smaller expert models to steer the larger base model towards more accurate and informative outputs, proxy-tuning demonstrates the importance of real-time adjustments in maintaining the model's knowledge base.
This finding underscores the potential of decoding-time algorithms in reinforcing the model's factual accuracy and knowledge retention. It implies that the mechanisms by which language models acquire and retain factual knowledge are not solely dependent on pretraining or direct finetuning but can be further enhanced through dynamic steering during the generation process. Understanding and optimizing these mechanisms could lead to more robust and reliable language models that excel in knowledge-intensive tasks while maintaining factual accuracy and consistency.