- The paper demonstrates that combining routing weights and hidden state embeddings in MoE LLMs enhances embedding quality across diverse tasks.
- The methodology employs a weighted sum of RW and HS, outperforming concatenation on semantic similarity and classification over 20 MTEB datasets.
- Empirical results imply that pre-trained MoE LLMs can serve as robust embedding models without additional fine-tuning, reducing computational costs.
Mixture-of-Experts LLMs as Embedding Models: An Analysis
The paper under consideration investigates the potential of Mixture-of-Experts (MoE) LLMs to function as effective embedding models without requiring additional representation fine-tuning. This exploration is particularly pertinent given the widespread application of LLMs primarily in generative tasks, which are often limited by their decoder-only architectures.
Analysis of MoE LLMs for Embedding
MoE architectures have garnered attention for their ability to enhance model generalization and reduce inference costs by dynamically assigning tasks to specialized experts. This paper leverages the unique properties of MoE's expert routers, proposing that these routers can act inherently as competent embedding models across diverse embedding-focused tasks.
Methodology and Findings
A key observation in the paper is the complementarity between the MoE routing weights (RW) and the hidden state (HS) embeddings typically extracted from LLMs. The paper argues that while HS embeddings are often biased towards predicting the next token, RW captures high-level semantics, adding robustness and context.
The authors introduce a novel approach of combining RW and HS embeddings to enhance performance in embedding tasks. Their experiments indicate that a weighted sum of RW and HS similarities outperforms the concatentation method, optimizing performance across tasks such as semantic textual similarity and classification.
Experimental Validation
The empirical evaluations span 20 datasets from the Massive Text Embedding Benchmark (MTEB), covering six types of tasks. The results consistently demonstrate that combining RW with HS significantly improves embedding quality, achieving better performance than HS or RW used independently. Notably, this combination outperforms various existing methods without the necessity for additional training.
Implications and Future Work
The findings suggest that pre-trained MoE LLMs can indeed claim the title of generalists, provided their routing weights are effectively utilized. This opens up new avenues for deploying MoE architectures in representation learning tasks, reducing the dependency on task-specific fine-tuning.
The paper implies practical implications for the deployment of LLMs in scenarios demanding rich and robust embeddings without computationally expensive training processes. Theoretically, it adds a dimension to understanding dynamic routing in MoE and its impact on model comprehensiveness.
Future research could delve into refining combination strategies of RW and HS embeddings, exploring adaptive mechanisms that further exploit the complementarity for specific task requirements. Additionally, investigations into how these insights can influence the development of other dynamic routing architectures may prove valuable.
Conclusion
This paper provides a significant contribution to the discourse surrounding LLMs and embedding models. By examining the seldom-explored potential of MoE LLMs, it challenges traditional views and offers a pathway to exploit inherent model properties. The insights from this research could inform the design of future architectures and advance the application of LLMs in diverse computational linguistics problems.