Inside Cicero: Enhancing Language Models with Strategic Reasoning and Planning

Table of Contents

1. Introduction
2. The Language Architecture
3. The Transformer Architecture
4. The Bart Model
5. Encoder-Decoder Architecture
6. Challenges with the Naive Approach
7. Exploitable Behavior in Language Models
8. Using No-Press Models for Guided Conversation
9. Outputting Plans for Strategic Conversation
10. Combining Language Model and Planning Model
11. Teaching the Language Model to Condition on Plans
12. Complications in Training Data
13. The Modular Architecture of Cicero
14. The Key Sub-Modules
15. Filters for Cleaning Up Outputs

The Language Architecture and its Transformer Architecture

In the field of natural language processing, transformer architectures have gained a lot of Attention due to their ability to handle sequential data more effectively. One such architecture is the Bart model, which is Based on the Transformer framework. The Bart model plays a crucial role in a language architecture developed by a research team. This language architecture aims to enhance the performance and capabilities of language models by incorporating strategic reasoning and planning.

The language architecture consists of several components, with the Bart model serving as the foundation. The model follows an encoder-decoder structure, allowing it to encode a given Context and generate an appropriate response. However, using the naive approach of only feeding conversation history and game board state to the model for fine-tuning results in inaccurate and exploitable behavior.

To address these challenges, the researchers incorporated no-press models in the architecture. These models generate plans for the language model to condition on, enabling more guided conversations. By creating per-player plans, the model can provide strategic suggestions and Prompts for each player's conversation. This not only enriches the conversations but also reduces the need for encoding detailed information into the language model itself.

The inclusion of plans in the training data of the language model posed another challenge. Since the moves made by players may not always Align with their original plans, the researchers implemented an inference process to determine the likely intentions behind each move. By inserting this inferred plan information into the training data, the language model can be effectively conditioned on plans during both training and gameplay.

The overall architecture of this language model, named Cicero, is modular in nature. It brings together different sub-modules, including the Bart model, strategic reasoning model, reinforcement learning components, planning model, and various filters for cleaning up the model's outputs. These components Interact to Create a powerful language model that exhibits top performance in press diplomacy tasks.

While Cicero strives to produce realistic and contextually appropriate responses, it is not infallible. Some outputs may be irrelevant or nonsensical, showcasing the limitations of the filtering process. However, efforts have been made to prevent the model from generating offensive or meta-referential content to maintain the authenticity of its conversations.

In conclusion, the language architecture based on transformer frameworks, specifically using the Bart model, shows promise in enhancing the capabilities of language models. By incorporating strategic reasoning, guided conversation planning, and effective conditioning on plans, the model achieves improved language generation and minimizes exploitable behavior.

Pros:

• Enhanced language generation capabilities
• Guided conversation planning improves strategic reasoning
• Effective conditioning on plans improves model performance

Cons:

• Filtering process may still result in some irrelevant or nonsensical outputs
• Language model's outputs may not always align perfectly with the inferred plans