BeLLM: Backward Dependency Enhanced Large Language Model for Sentence Embeddings

Core Concepts

Backward dependencies enhance sentence embeddings in large language models.

Abstract

Sentence embeddings are crucial for semantic similarity measurement. BeLLM introduces backward dependency modeling to improve LLMs. Experimental results show BeLLM outperforms previous SOTA models. Ablation study highlights the importance of balancing uni- and bi-directional layers. Case study demonstrates BeLLM's superior performance in semantic retrieval tasks. Discussion on enhanced dependency shows the effectiveness of incorporating backward dependencies in LLMs.

Stats

Existing LLMs mainly adopt autoregressive architecture without explicit backward dependency modeling. BeLLM achieves state-of-the-art performance in various semantic textual similarity tasks. BeLLM significantly outperforms previous SOTA models, such as SimCSE and RoBERTa, across different benchmarks.

Quotes

"Most advanced NLP models adopted autoregressive architectures with forward dependency modeling only." "BeLLM achieves a notable 2.5% improvement compared to the previous SOTA model." "BeLLM performs the best in all S-STS datasets."

Key Insights Distilled From

BeLLM

by Xianming Li,... at arxiv.org 03-15-2024

https://arxiv.org/pdf/2311.05296.pdf

Deeper Inquiries

How can the efficiency of large-scale parameter models like BeLLM be optimized for real-world applications

BeLLM, being a large-scale parameter model, can optimize its efficiency for real-world applications through several strategies. One approach is to implement model compression techniques like quantization and pruning to reduce the number of parameters without compromising performance significantly. Another method is to leverage hardware accelerators like GPUs or TPUs to speed up computations and improve overall efficiency. Additionally, optimizing the training process by fine-tuning hyperparameters, adjusting learning rates, and employing regularization techniques can enhance the model's efficiency. Moreover, utilizing distributed computing frameworks like Apache Spark or TensorFlow Distributed can help parallelize tasks and scale up processing power for handling larger datasets efficiently.

What are the implications of neglecting backward dependencies in autoregressive LLMs for sentence embeddings

Neglecting backward dependencies in autoregressive LLMs for sentence embeddings can have significant implications on their effectiveness in capturing context comprehensively. Backward dependencies play a crucial role in understanding relationships between words within sentences that occur before the current word being processed. Without considering these dependencies, models may struggle to grasp nuanced semantic connections that rely on information presented earlier in the text. This limitation could lead to suboptimal sentence embeddings that lack depth and fail to capture intricate contextual nuances accurately.

How does incorporating backward dependencies impact the overall context understanding capabilities of large language models

Incorporating backward dependencies into large language models enhances their overall context understanding capabilities by allowing them to capture bidirectional relationships within text more effectively. By modeling both forward and backward dependencies, these models gain a more comprehensive view of the input sequence's semantics and are better equipped to understand complex linguistic structures across sentences. This improved context understanding leads to more accurate representations of textual data, enabling enhanced performance in various natural language processing tasks such as semantic similarity measurement, sentiment analysis, and text generation.