IDEAL: Leveraging Infinite and Dynamic Characterizations of Large Language Models for Query-focused Summarization

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Expert, extended

Summary

The IDEAL framework introduces an efficient, query-aware adaptation method for Large Language Models (LLMs) in Query-focused Summarization (QFS) tasks. It addresses challenges in lengthy document processing and fine-grained query-LLM alignment through two modules. The Query-aware HyperExpert dynamically generates LLM parameter shifts using a HyperNetwork with parameter-efficient fine-tuning (PEFT) strategies like LoRA, Prompt-tuning, and Parallel Adapter, enhancing query alignment. The Query-focused Infini-attention module processes long documents by integrating a novel query-focused compressive memory with long-term linear attention, enabling models like LLAMA2-7B to handle 13,000 input tokens on a single 24GB Nvidia GeForce RTX 3090. Experiments on CovidET, QMSum, and SQuALITY datasets demonstrate IDEAL's superior performance, with IDEALLoRA surpassing baselines by up to 1.64 ROUGE-L points.

Key takeaway

For machine learning engineers developing query-focused summarization systems, especially with long documents, you should consider integrating the IDEAL framework. Its Query-aware HyperExpert dynamically fine-tunes LLMs for precise query alignment, while Query-focused Infini-attention efficiently handles extensive inputs on limited GPU memory, such as 13,000 tokens on a 24GB Nvidia GeForce RTX 3090. This approach significantly boosts performance and resource efficiency over traditional methods.

Key insights

LLMs can achieve efficient, query-focused summarization of long documents by dynamically adapting parameters and using query-aware memory.

Principles

• HyperNetworks dynamically adjust LLM parameters for fine-grained query alignment.
• Query-focused compressive memory is essential for long-document QFS.
• Repeating query instructions improves summary generation guidance.

Method

IDEAL combines a Query-aware HyperExpert (HyperNetwork-driven PEFT for dynamic parameter shifts) and Query-focused Infini-attention (compressive memory with an additional query-focused block) into decoder-only LLMs like LLaMA.

In practice

• Apply HyperNetwork-based PEFT (e.g., IDEALLoRA) for query-specific LLM adaptation.
• Utilize Query-focused Infini-attention for long document processing on 24GB GPUs.
• Replicate query instructions at document end to enhance summary relevance.

Topics

• Query-focused Summarization
• Large Language Models
• Parameter-Efficient Fine-Tuning
• HyperNetworks
• Infini-attention
• Long-context Transformers

Code references

• DCDmllm/IDEAL_Summary
• huggingface/peft

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.