洞察 - Language Models - # Intractable Inference in Language Models

Amortizing Intractable Inference in Large Language Models: A Bayesian Approach

Q: How can GFlowNet fine-tuning be applied to other types of language models beyond autoregressive ones

GFlowNet fine-tuning can be applied to various types of language models beyond autoregressive ones by adapting the training process to suit the specific model architecture. For instance, for transformer-based models like BERT or RoBERTa, which are bidirectional and do not rely on autoregressive sampling, GFlowNet fine-tuning can still be implemented by modifying the policy generation process. Instead of conditioning on previous tokens in an autoregressive manner, the policy could consider contextual embeddings from both directions and generate samples accordingly. This adaptation would involve redefining how rewards are calculated and updating the policy parameters based on these rewards.

Q: What are the potential implications of using GFlowNet objectives for probabilistic inference in other domains

The potential implications of using GFlowNet objectives for probabilistic inference in other domains extend beyond natural language processing. In fields such as biology, chemistry, physics, and finance where complex data structures need to be sampled or generated according to certain criteria or constraints, GFlowNet fine-tuning can offer a principled approach to learning policies that sample diverse high-reward objects efficiently. By applying this framework to tasks like molecular design in drug discovery or simulation-based optimization in engineering, researchers can benefit from improved sample diversity while maintaining high likelihood under given constraints.

Q: How might the concept of amortized inference impact the development of future language models

The concept of amortized inference introduced through methods like GFlowNet fine-tuning has significant implications for the development of future language models. It enables more efficient querying of large language models by training them to approximate posterior distributions over latent variables rather than relying solely on maximum-likelihood estimation or reward-maximizing strategies. This shift towards amortized inference allows for better generalization across tasks with limited data and promotes a more principled approach to sampling from complex distributions within language models. As future language models evolve towards handling more nuanced reasoning tasks and structured data manipulation, incorporating amortized inference techniques will likely play a crucial role in enhancing their capabilities while ensuring robust performance across various applications.

核心概念

Amortized Bayesian inference using GFlowNets enables efficient sampling from intractable posteriors in large language models.

摘要

Large language models face challenges with tasks like infilling and constrained generation due to intractable posterior distributions. Amortized Bayesian inference with GFlowNets offers a solution by fine-tuning models to sample from these distributions efficiently. This approach improves diversity, data efficiency, and generalization compared to traditional training methods. Empirical results demonstrate the effectiveness of this method across various tasks, including sequence continuation, reasoning, arithmetic problem-solving, and story infilling.

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"An absolute improvement of 10.9% over supervised fine-tuning on subjectivity classification with only 10 labeled examples."
"Outperforms supervised fine-tuning and PPO by 63% on integer arithmetic with 50 demonstrations."

引用

"A deeply moving storyline."
"The cat was hungry."
"Now the cat is sleepy, not hungry."

从中提取的关键见解

Amortizing intractable inference in large language models

by Edward J. Hu... 在 arxiv.org 03-15-2024

https://arxiv.org/pdf/2310.04363.pdf

Amortizing intractable inference in large language models

更深入的查询

How can GFlowNet fine-tuning be applied to other types of language models beyond autoregressive ones

GFlowNet fine-tuning can be applied to various types of language models beyond autoregressive ones by adapting the training process to suit the specific model architecture. For instance, for transformer-based models like BERT or RoBERTa, which are bidirectional and do not rely on autoregressive sampling, GFlowNet fine-tuning can still be implemented by modifying the policy generation process. Instead of conditioning on previous tokens in an autoregressive manner, the policy could consider contextual embeddings from both directions and generate samples accordingly. This adaptation would involve redefining how rewards are calculated and updating the policy parameters based on these rewards.

What are the potential implications of using GFlowNet objectives for probabilistic inference in other domains

The potential implications of using GFlowNet objectives for probabilistic inference in other domains extend beyond natural language processing. In fields such as biology, chemistry, physics, and finance where complex data structures need to be sampled or generated according to certain criteria or constraints, GFlowNet fine-tuning can offer a principled approach to learning policies that sample diverse high-reward objects efficiently. By applying this framework to tasks like molecular design in drug discovery or simulation-based optimization in engineering, researchers can benefit from improved sample diversity while maintaining high likelihood under given constraints.

How might the concept of amortized inference impact the development of future language models

The concept of amortized inference introduced through methods like GFlowNet fine-tuning has significant implications for the development of future language models. It enables more efficient querying of large language models by training them to approximate posterior distributions over latent variables rather than relying solely on maximum-likelihood estimation or reward-maximizing strategies. This shift towards amortized inference allows for better generalization across tasks with limited data and promotes a more principled approach to sampling from complex distributions within language models. As future language models evolve towards handling more nuanced reasoning tasks and structured data manipulation, incorporating amortized inference techniques will likely play a crucial role in enhancing their capabilities while ensuring robust performance across various applications.