LLM as Effective Streaming Processor: Bridging Streaming-Batch Mismatches with Group Position Encoding (2505.16983v2)

Abstract: LLMs are primarily designed for batch processing. Existing methods for adapting LLMs to streaming rely either on expensive re-encoding or specialized architectures with limited scalability. This work identifies three key mismatches in adapting batch-oriented LLMs to streaming: (1) input-attention, (2) output-attention, and (3) position-ID mismatches. While it is commonly assumed that the latter two mismatches require frequent re-encoding, our analysis reveals that only the input-attention mismatch significantly impacts performance, indicating re-encoding outputs is largely unnecessary. To better understand this discrepancy with the common assumption, we provide the first comprehensive analysis of the impact of position encoding on LLMs in streaming, showing that preserving relative positions within source and target contexts is more critical than maintaining absolute order. Motivated by the above analysis, we introduce a group position encoding paradigm built on batch architectures to enhance consistency between streaming and batch modes. Extensive experiments on cross-lingual and cross-modal tasks demonstrate that our method outperforms existing approaches. Our method requires no architectural modifications, exhibits strong generalization in both streaming and batch modes. The code is available at repository https://github.com/EIT-NLP/StreamingLLM.

An Analytical Perspective on Effective Streaming Processing for LLMs

The paper "LLM as Effective Streaming Processor: Bridging Streaming-Batch Mismatches with Group Position Encoding" explores the adaptation of LLMs, traditionally designed for batch processing, to streaming scenarios. Streaming applications like real-time translation or speech recognition demand a paradigm shift in how LLMs process information, yet many existing methods introduce complexity and computational inefficiencies. This research identifies and addresses three pivotal mismatches between batch-oriented and streaming applications: input-attention, output-attention, and position-ID mismatches.

Key Insights and Contributions

The primary insight from the paper is the impact analysis of these mismatches on streaming performance. In streaming tasks, models often re-encode inputs and outputs to mitigate any architectural misalignments. Contrary to existing assumptions, this paper finds that input-attention mismatch substantially affects performance, whereas output-attention and position-ID mismatches exhibit minimal effects. Re-encoding outputs—a common strategy for resolving perceived discrepancies—proved largely unnecessary.

From the above findings, the paper proposes a group position encoding paradigm—an approach that aligns the encoding of tokens in streaming tasks more closely with batch processing methods without architectural modification. By grouping position IDs separately for inputs and outputs, the model achieves strong generalization across varying streaming scenarios and avoids the computational costs associated with frequent re-encoding.

Experimental Results

Extensive experiments across cross-lingual translation and cross-modal tasks demonstrate the superiority of the group position encoding method. The paper benchmarks performance using BLEU scores for translation tasks and WER for speech recognition. Results show significant improvements over conventional strategies, presenting higher efficiency and accuracy without re-encoding.

Practical and Theoretical Implications

The findings hold substantial implications in both practical and theoretical domains. Practically, the proposed group position encoding method facilitates the seamless adaptation of LLMs to streaming tasks, enhancing their utility in real-time applications without the overhead of re-encoding. Theoretically, this research challenges the prevailing assumptions about positional encoding in dynamic processing scenarios, suggesting a reevaluation of the role and implementation of positional encoding in LLM architectures.

Future Developments

The paper opens avenues for further exploration into refining streaming model architectures to optimize efficiency and processing power. As the demand for real-time applications continues to grow, models like this—capable of handling streaming data efficiently—are likely to become increasingly prevalent. Future research may explore optimizing position encoding further or explore integration with more diverse modalities beyond text and speech.

In conclusion, this paper provides critical insights into making LLMs more adept at streaming processing by addressing mismatches at the architectural level. The proposed method stands out for its simplicity, efficiency, and adaptability, paving the way for robust applications in dynamic real-world scenarios.