LLM+P: Empowering Large Language Models with Optimal Planning Proficiency

LLM+P: Empowering Large Language Models with Optimal Planning Proficiency

Recent years have seen significant advancements in large language models (LLMs). These models have demonstrated impressive zero-shot generalization abilities, with state-of-the-art chatbots being able to provide plausible answers to many everyday questions. However, LLMs still struggle when it comes to solving long-horizon planning problems. On the other hand, classical planners excel at efficiently finding correct or even optimal plans for formatted problems. In an attempt to combine the strengths of both LLMs and classical planners, the LLM+P framework has been introduced.

LLM+P takes a natural language description of a planning problem as input and returns a correct or optimal plan in natural language as output. The framework achieves this by first converting the language description into a file written in the planning domain definition language (PDDL). Classical planners are then leveraged to quickly find a solution, which is then translated back into natural language. Alongside LLM+P, a diverse set of benchmark problems from common planning scenarios has been defined.

Through a comprehensive set of experiments on these benchmark problems, LLM+P has proven its ability to provide optimal solutions for most problems. In contrast, LLMs often fail to provide feasible plans for the same problems. This integration of classical planners into LLMs bridges the gap between the two approaches and opens up new possibilities for solving complex planning problems.

Scaling Neural Networks and Sequence Length

In recent years, there has been a trend towards scaling neural networks to achieve greater expressive power. Scaling up the depth of neural networks has been a popular approach, resulting in the creation of powerful deep networks. Additionally, techniques such as sparse MoE models and model parallelism have efficiently increased the hidden dimension of neural networks. However, the limitation of sequence length has been a challenge in scaling neural networks.

Breaking the limitation of sequence length offers several advantages. Firstly, it provides larger memory and receptive fields for models, enabling them to interact more effectively with humans and the world. Secondly, longer contexts contain more complex causality and reasoning paths that models can exploit during training. In contrast, shorter dependencies may lead to more spurious correlations, which can be detrimental to generalization. Finally, longer contexts have the potential to explore the limits of in-context learning, which could revolutionize many-shot learning by helping models mitigate catastrophic forgetting.

Scaling up the sequence length presents challenges in balancing computational complexity and model expressivity. RNN-style models have primarily been used to increase sequence length, but their sequential nature limits parallelization during training, which is crucial for long-sequence modeling. State space models have emerged as an appealing alternative for sequence modeling, operating as CNNs during training and transforming into efficient RNNs at test time. While they perform well on long-range benchmarks, their performance on regular lengths is not as good as Transformers.

Another approach to scaling sequence length is to decrease the complexity of Transformers, particularly the quadratic complexity of self-attention. Implementing sliding windows or convolution modules over attention can reduce the complexity to nearly linear. However, this sacrifices the ability to recall early tokens, which may contain important prompts. Sparse attention, on the other hand, reduces computation by sparsifying the attention matrix while preserving the possibility of recalling long-distant information.

Efficient Transformer-based variants, including low-rank attention, kernel-based methods, downsampling approaches, recurrent models, and retrieval-based methods, have also been explored. However, none of these variants have been successfully scaled to 1 billion tokens.

Introducing LONGNET: Scaling Sequence Length to 1 Billion Tokens

In this work, the researchers successfully scaled the sequence length to 1 billion tokens with the introduction of LONGNET. LONGNET replaces the attention mechanism of vanilla Transformers with a novel component called dilated attention. The design principle of LONGNET is that attention allocation decreases exponentially as the distance between tokens increases. This approach achieves linear computation complexity and logarithmic dependency between tokens.

The implementation of LONGNET allows for seamless transformation into a dense Transformer, supporting off-the-shelf optimization techniques such as kernel fusion, quantization, and distributed training. The linear complexity of LONGNET enables parallelization across nodes, breaking the computational and memory constraints associated with vanilla Transformers. This efficient scaling of sequence length to 1 billion tokens is achieved with nearly constant runtime, whereas vanilla Transformers suffer from quadratic complexity.

Actionable Advice:

1. Incorporate classical planners into large language models: As seen in the LLM+P framework, combining the strengths of classical planners and large language models can lead to more robust and reliable solutions for complex planning problems. Consider integrating classical planning techniques into existing language models to enhance their planning capabilities.

2. Explore efficient scaling techniques for sequence length: Scaling up the sequence length of neural networks can offer significant advantages in terms of memory, reasoning, and learning capabilities. Investigate approaches such as dilated attention, sparse attention, and other efficient Transformer-based variants to achieve longer sequence lengths without sacrificing computational efficiency.

3. Leverage parallelization and distributed algorithms: When scaling sequence length or any other aspect of neural networks, consider leveraging parallelization and distributed algorithms to overcome computational and memory constraints. This can help achieve efficient training and inference on large-scale models, enabling breakthroughs in various domains.

In conclusion, the integration of classical planners into large language models through frameworks like LLM+P opens up new possibilities for solving complex planning problems. Additionally, scaling the sequence length of neural networks to overcome limitations and achieve greater expressive power is a promising avenue for research. By incorporating these advancements, we can enhance the capabilities of language models and enable them to tackle increasingly complex tasks.