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

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

Recent advancements in large language models (LLMs) have revolutionized natural language processing tasks, enabling chatbots to provide plausible answers to common questions. However, these models still struggle with long-horizon planning problems. Classical planners, on the other hand, excel at solving such problems efficiently. To bridge this gap, a new framework called LLM+P has been introduced, which combines the strengths of LLMs and classical planners.

LLM+P takes a natural language description of a planning problem and converts it into a planning domain definition language (PDDL) file. It then leverages classical planners to quickly find a solution, which is then translated back into natural language. Through a comprehensive set of experiments on benchmark problems, LLM+P has shown the ability to provide optimal solutions for most problems, whereas LLMs fail to provide feasible plans for the majority of problems.

Scaling neural networks has been a prominent trend in recent years. Increasing the depth of neural networks has allowed for exponential expressivity and the creation of powerful deep networks. Sparse mixture of experts (MoE) models and model parallelism approaches have efficiently enlarged the hidden dimensions of these networks. However, sequence length, the last atomic dimension of neural networks, has remained a limitation.

Breaking the limitation of sequence length offers significant advantages. Firstly, it provides large memory and receptive fields for models, enabling them to interact with humans and the world more effectively. Secondly, longer contexts contain more complex causality and reasoning paths that models can exploit in training data. In contrast, shorter dependencies have more spurious correlations, which harm generalization. Lastly, longer contexts open up possibilities for exploring in-context learning, which can be a paradigm shift for many-shot learning as it helps models alleviate catastrophic forgetting.

Scaling sequence length faces the challenge of striking the right balance between 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 shown promise in sequence modeling as they can operate as a CNN during training and transform into an efficient RNN at test time. However, their performance on regular lengths is not as good as Transformers due to limited model expressivity.

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 is a straightforward way to achieve nearly linear complexity. However, this sacrifices the ability to recall early tokens, forgetting prompts at the beginning of the sequence. Sparse attention, on the other hand, reduces computation by sparsifying the attention matrix while preserving the ability to recall long-distant information.

Efficient Transformer-based variants, such as low-rank attention, kernel-based methods, downsampling approaches, recurrent models, and retrieval-based methods, have been explored. However, none of these variants have been successfully scaled to 1 billion tokens. To address this, LONGNET, a solution that replaces the attention mechanism of vanilla Transformers with dilated attention, has been introduced. Dilated attention allocates attention resources exponentially as the distance between tokens increases, achieving linear computation complexity and logarithmic dependency between tokens. LONGNET can be transformed into a dense Transformer, supporting off-the-shelf optimization techniques and allowing for efficient scaling to 1 billion tokens.

In conclusion, bridging the gap between LLMs and classical planners through the LLM+P framework empowers LLMs with optimal planning proficiency, enabling them to provide correct or optimal plans for complex problems. Additionally, scaling the sequence length to 1 billion tokens with LONGNET opens up new possibilities for natural language processing tasks. Three actionable pieces of advice that can be derived from these advancements are:

1. Incorporate classical planning techniques into language models: By combining the strengths of classical planners and LLMs, we can enhance the planning abilities of language models, enabling them to solve complex problems efficiently.

2. Explore alternative approaches to scaling sequence length: Traditional methods like RNN-style models have limitations in parallelization, while state space models face challenges in model expressivity. Exploring novel approaches like dilated attention can offer efficient scaling of sequence length without sacrificing performance.

3. Continuously push the boundaries of scalability in neural networks: The advancements in scaling neural networks have led to transformative breakthroughs in natural language processing. By continuously exploring new techniques and pushing the boundaries of scalability, we can unlock further potential in language models and sequence modeling.

In summary, the combination of LLM+P and LONGNET represents significant progress in empowering language models and scaling sequence length, respectively. These advancements have the potential to revolutionize natural language processing tasks and open up new avenues for research and development in the field.