From Similarity to Structure: Training-free LLM Context Compression with Hybrid Graph Priors
In the rapidly evolving field of natural language processing, large language models (LLMs) have become indispensable for a variety of applications. However, one of the persistent challenges in utilizing these models is their computational expense, especially when dealing with long-context inputs. Recent advancements have highlighted the necessity for effective context compression mechanisms to enhance the usability of LLMs without compromising performance.
Traditional methods for context compression often rely on trained compressors, which can be resource-intensive and may not generalize well across different tasks. Moreover, approaches such as dense retrieval-style selection and heuristic trimming frequently struggle to maintain a balance between task relevance, topic coverage, and cross-sentence coherence when working within a strict token budget. This necessitates a fresh perspective on how to achieve efficient context compression.
A Novel Approach to Context Compression
To tackle these issues, a new framework has been proposed that is both training-free and model-agnostic. This innovative method selects a compact set of sentences guided by structural graph priors, thereby enhancing the performance of LLMs in processing long inputs. The proposed framework constructs a sparse hybrid sentence graph that effectively integrates two types of edges:
- Mutual k-NN Semantic Edges: These edges capture semantic relationships between sentences, enabling the model to identify and prioritize related content.
- Short-range Sequential Edges: These edges maintain the sequential order of sentences, preserving the narrative flow and coherence of the text.
By combining these two edge types, the framework extracts a ‘topic skeleton’ through clustering techniques, which serves as a foundation for further sentence ranking. This ranking is achieved using an interpretable score that encompasses several critical factors:
- Task Relevance: Ensures that the selected sentences are pertinent to the specific task at hand.
- Cluster Representativeness: Evaluates how well a sentence represents its respective topic cluster.
- Bridge Centrality: Identifies sentences that act as bridges between different topics, enhancing coherence.
- Cycle Coverage Cue: Focuses on maintaining a comprehensive coverage of the overarching topics.
Once the sentences are scored, a budgeted greedy selection process is employed, which includes redundancy suppression to ensure clarity and readability in the compressed context. This innovative strategy aims to maintain the original order of sentences, thereby preserving the logical flow of information.
Experimental Results and Implications
The framework has been rigorously tested across four diverse datasets, demonstrating its competitive edge against both strong extractive and abstractive baselines. Notably, the proposed method exhibits larger gains on long-document benchmarks, signifying its potential to significantly enhance LLM performance in real-world applications.
In conclusion, the introduction of a training-free and model-agnostic context compression framework based on hybrid graph priors marks a significant advancement in the field of natural language processing. By addressing the limitations of existing approaches, this innovative method not only enhances the efficiency of long-context LLMs but also opens new avenues for research in context management and information retrieval.
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