Embedding-Aligned Language Models (2406.00024v2)

Abstract: We propose a novel approach for training LLMs to adhere to objectives defined within a latent embedding space. Our method leverages reinforcement learning (RL), treating a pre-trained LLM as an environment. Our embedding-aligned guided language (EAGLE) agent is trained to iteratively steer the LLM's generation towards optimal regions of the latent embedding space, w.r.t. some predefined criterion. We demonstrate the effectiveness of the EAGLE agent using the MovieLens 25M and Amazon Review datasets to surface content gaps that satisfy latent user demand. We also demonstrate the benefit of using an optimal design of a state-dependent action set to improve EAGLE's efficiency. Our work paves the way for controlled and grounded text generation using LLMs, ensuring consistency with domain-specific knowledge and data representations.

• The paper presents the EAGLE framework, a reinforcement learning-based method that leverages latent embedding spaces for improved LLM controllability.
• The methodology employs a Markov decision process and G-optimal design to guide the exploration of the text generation action space.
• Experimental results on the MovieLens 25M dataset demonstrate enhanced contextual alignment and scalability across different LLM environments.

Embedding-Aligned LLMs: Enhancing LLM Controllability with Latent Embedding Spaces

The paper "Embedding-Aligned LLMs" by Guy Tennenholtz et al. presents an innovative framework aimed at leveraging latent embedding spaces for improved control over the generation of LLMs. The proposed method, termed Embedding-Aligned Guided LanguagE (EAGLE) agent, employs reinforcement learning (RL) techniques to guide LLM generation in alignment with predefined criteria rooted in latent embeddings. This approach addresses the need for generating text that is congruent with domain-specific knowledge and data representations.

Framework and Methods

The authors propose that latent embeddings, which are extensively utilized in areas such as recommender systems and image classification, provide a compact representation of domain-specific entities. EAGLE introduces a process where these embeddings define the objective function within an RL-driven framework to guide LLM outputs.

A critical aspect of the research is the formulation of a Markov decision process (MDP) where the state space consists of possible text sequences (LLM outputs), and the action space is defined by textual prompts that modify these sequences. The transition probabilities are governed by the outputs of a pre-trained environment LLM. This RL approach contrasts with the traditional method of embedding LLMs (ELMs), which rely on a trained decoder to map latent embeddings back to the ambient text space.

A notable contribution of the paper is the discussion of action space design using LLMs. Specifically, it utilizes G-optimal design criteria to ensure that the action space is explorative and efficient. This strategy mitigates the bias and coverage limitations of naively generated action spaces, especially those arising from LLM generation.

Experimental Validation

The effectiveness of the EAGLE framework is empirically demonstrated using the MovieLens 25M dataset. This dataset provides a robust testbed due to its detailed user and movie interaction data. Behavioral embeddings for both users and movies are computed using collaborative filtering techniques, and Gemini Ultra LLM is employed to generate textual descriptions and action prompts.

Key experiments involve:

1. Comparison with ELMs: EAGLE is shown to outperform traditional ELMs in generating consistent and contextually valuable content. This is partly due to the realizability constraints of ELMs when decoding embeddings into coherent text.
2. Reference Policy Variants: Different reference policies, including uniform sampling, myopic best next-step action, and G-optimal design, are evaluated for their efficiency in training the RL agent.
3. Environment Transfer: The robustness of the EAGLE framework is tested by transferring the trained agent across different LLM environments (e.g., from Gemini Pro to GPT-4), demonstrating minimal performance degradation.

Results and Implications

The results underscore several compelling benefits of the EAGLE framework:

• Enhanced Controllability: By iteratively guiding the LLM based on feedback from the latent embedding space, EAGLE ensures that the generated content aligns closely with the desired criteria.
• Efficiency Gains: The use of G-optimal action set design improves the exploration efficiency and effectiveness of the RL process.
• Scalability and Adaptability: The framework's ability to adapt to various LLM environments without significant retraining underscores its practical utility.

The authors also highlight the potential for broader applications of EAGLE, extending beyond text generation to other modalities such as images and audio. The incorporation of domain-specific knowledge through latent embeddings could significantly enhance the controllability and relevance of generative models in these fields.

Future Directions

The research opens several pathways for further exploration:

• Action Space Design: Investigating more sophisticated methods for action space generation, potentially incorporating human expert input or advanced generative models, could further refine EAGLE's capabilities.
• Multimodal Applications: Extending the framework to handle multimodal inputs and outputs, integrating embeddings from diverse data sources, may broaden its applicability.
• Customization and Personalization: Deepening the personalization strategies within EAGLE to dynamically adjust to individual user preferences in real-time applications.

In conclusion, the paper presents a thoughtful and structured approach to leveraging latent embedding spaces for controlling LLM outputs. The EAGLE framework represents a significant step forward in the development of guided language generation, rooted in robust RL methods and optimized through advanced design of action spaces. This work lays a solid foundation for future enhancements and applications across a spectrum of generative AI tasks.