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With Greater Text Comes Greater Necessity: Inference-Time Training Helps Long Text Generation

Y. Wang, D. Ma, D. Cai

TL;DR

Temp-Lora introduces an inference-time training paradigm where a temporary LoRA module captures and updates long-context information during generation, discarding the module afterward to avoid altering base model parameters. This approach yields substantial perplexity reductions on long-text benchmarks (PG19 and GuoFeng) and notable translation quality gains, while remaining complementary to existing long-context techniques and reducing computational costs by shortening the effective context window. Empirical results across multiple models demonstrate robust improvements that scale with text length, supporting its practicality for ultra-long generation tasks. The work provides deployment strategies (cascaded vs parallelized) and parameter-sensitivity insights to guide real-world adoption, highlighting Temp-Lora as a versatile, efficient solution for long-context language tasks.

Abstract

Long text generation, such as novel writing and discourse-level translation with extremely long contexts, presents significant challenges to current language models. Existing methods mainly focus on extending the model's context window through strategies like length extrapolation. However, these approaches demand substantial hardware resources during the training and/or inference phases. Our proposed method, Temp-Lora, introduces an alternative concept. Instead of relying on the KV cache to store all context information, we embeds this information directly into a temporary Lora module. In the process of long text generation, this module is progressively trained with text generated previously. This approach not only efficiently preserves contextual knowledge but also prevents any permanent alteration to the model's parameters given that the module is discarded post-generation. Extensive experiments on the PG19 language modeling benchmark and the GuoFeng discourse-level translation benchmark validate the effectiveness of Temp-Lora. Our results show that: 1) Temp-Lora substantially enhances generation quality for long text, as indicated by a 13.2% decrease in perplexity (PPL) on a subset of PG19, and a 29.3% decrease in PPL along with a 113.2% increase in BLEU score on a subset of GuoFeng, 2) Temp-Lora is compatible with and enhances most existing long text generation methods, and 3) Temp-Lora can greatly reduce computational costs by shortening the context window. For example, we can ensure a moderate improvement in generation quality (a decrease of 3.8% in PPL) while enabling a 51.5% memory usage reduction and a 60.0% decrease in latency for inference.

With Greater Text Comes Greater Necessity: Inference-Time Training Helps Long Text Generation

TL;DR

Temp-Lora introduces an inference-time training paradigm where a temporary LoRA module captures and updates long-context information during generation, discarding the module afterward to avoid altering base model parameters. This approach yields substantial perplexity reductions on long-text benchmarks (PG19 and GuoFeng) and notable translation quality gains, while remaining complementary to existing long-context techniques and reducing computational costs by shortening the effective context window. Empirical results across multiple models demonstrate robust improvements that scale with text length, supporting its practicality for ultra-long generation tasks. The work provides deployment strategies (cascaded vs parallelized) and parameter-sensitivity insights to guide real-world adoption, highlighting Temp-Lora as a versatile, efficient solution for long-context language tasks.

Abstract

Long text generation, such as novel writing and discourse-level translation with extremely long contexts, presents significant challenges to current language models. Existing methods mainly focus on extending the model's context window through strategies like length extrapolation. However, these approaches demand substantial hardware resources during the training and/or inference phases. Our proposed method, Temp-Lora, introduces an alternative concept. Instead of relying on the KV cache to store all context information, we embeds this information directly into a temporary Lora module. In the process of long text generation, this module is progressively trained with text generated previously. This approach not only efficiently preserves contextual knowledge but also prevents any permanent alteration to the model's parameters given that the module is discarded post-generation. Extensive experiments on the PG19 language modeling benchmark and the GuoFeng discourse-level translation benchmark validate the effectiveness of Temp-Lora. Our results show that: 1) Temp-Lora substantially enhances generation quality for long text, as indicated by a 13.2% decrease in perplexity (PPL) on a subset of PG19, and a 29.3% decrease in PPL along with a 113.2% increase in BLEU score on a subset of GuoFeng, 2) Temp-Lora is compatible with and enhances most existing long text generation methods, and 3) Temp-Lora can greatly reduce computational costs by shortening the context window. For example, we can ensure a moderate improvement in generation quality (a decrease of 3.8% in PPL) while enabling a 51.5% memory usage reduction and a 60.0% decrease in latency for inference.
Paper Structure (20 sections, 2 figures, 5 tables, 1 algorithm)

This paper contains 20 sections, 2 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Temp-Lora
  3. Cache Reuse
  4. Attention Sinks
  5. Experiments
  6. Dataset:
  7. Baselines:
  8. Evaluation Metric:
  9. Setup:
  10. Main Results
  11. PG19:
  12. GuoFeng:
  13. Further Analysis
  14. Contrast to Dynamic-NTK
  15. Context Window Size:
  16. ...and 5 more sections

Figures (2)

  • Figure 1: The framework of long text generation with Temp-Lora
  • Figure 2: The relationship between model PPL and hyper-parameters, depicted from left to right, are (a) Epochs, (b) Lora Rank, and (c) Learning Rate. Note that the PPL at a X-axis value of 0 in each graph corresponds to the performance of the base model.