arXiv:2502.18915v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks. However, they are often distracted by irrelevant or noisy context in input sequences that degrades output quality. This problem affects both long- and short-context scenarios, such as retrieval-augmented generation, table question-answering, and in-context learning. We reveal that LLMs can implicitly identify whether input sequences contain useful information at early layers, prior to token generation. Leveragi
The proliferation of Large Language Models (LLMs) and their deployment across various applications necessitates constant research into improving their efficiency and reliability, especially as context windows grow.
Improving the ability of LLMs to filter noise and focus on relevant information directly enhances their performance, reduces computational overhead, and makes them more effective in complex real-world scenarios like retrieval-augmented generation.
This research suggests a more efficient method for context denoising in LLMs by identifying and dropping irrelevant information at early processing stages, potentially leading to more robust and less 'distracted' models.
- · AI developers and researchers
- · Companies utilizing LLMs for RAG, QA, and in-context learning
- · Users of LLM-powered applications
- · Less efficient LLM architectures
- · Applications highly sensitive to noisy inputs without robust denoising
- · Computational resources consumed by handling irrelevant information
LLMs can process longer and noisier contexts more effectively, improving their practical utility and reducing error rates.
This efficiency gain could lower the computational cost of deploying large-context LLMs, making advanced AI capabilities more accessible.
More reliable context understanding could accelerate the development of sophisticated AI agents capable of handling ambiguous or information-dense environments.
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Read at arXiv cs.AI