ChipAlign: Instruction Alignment in Large Language Models for Chip Design via Geodesic Interpolation

2024-12-15

arXiv.org

AI Summary

To address insufficient instruction alignment of large language models (LLMs) in chip design—particularly their difficulty in faithfully executing explicit hardware-engineer directives—this paper proposes a training-free weight manifold fusion method. Specifically, it performs geodesic interpolation in the model weight space to fuse a general-purpose instruction-aligned LLM with a chip-domain-specialized model. This is the first work to introduce geodesic interpolation for enhancing instruction alignment, effectively balancing domain expertise and instruction-following capability. Evaluated on IFEval, OpenROAD QA, and a production-grade chip QA benchmark, our method achieves absolute improvements of 26.6%, 3.9%, and 8.25%, respectively, surpassing all state-of-the-art approaches. The core contribution lies in a zero-training, high-fidelity, and interpretable model fusion paradigm that advances instruction alignment for domain-specific LLMs.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Recent advancements in large language models (LLMs) have expanded their application across various domains, including chip design, where domain-adapted chip models like ChipNeMo have emerged. However, these models often struggle with instruction alignment, a crucial capability for LLMs that involves following explicit human directives. This limitation impedes the practical application of chip LLMs, including serving as assistant chatbots for hardware design engineers. In this work, we introduce ChipAlign, a novel approach that utilizes a training-free model merging strategy, combining the strengths of a general instruction-aligned LLM with a chip-specific LLM. By considering the underlying manifold in the weight space, ChipAlign employs geodesic interpolation to effectively fuse the weights of input LLMs, producing a merged model that inherits strong instruction alignment and chip expertise from the respective instruction and chip LLMs. Our results demonstrate that ChipAlign significantly enhances instruction-following capabilities of existing chip LLMs, achieving up to a 26.6% improvement on the IFEval benchmark, while maintaining comparable expertise in the chip domain. This improvement in instruction alignment also translates to notable gains in instruction-involved QA tasks, delivering performance enhancements of 3.9% on the OpenROAD QA benchmark and 8.25% on production-level chip QA benchmarks, surpassing state-of-the-art baselines.

Problem

• Enhancing instruction alignment in chip design LLMs
• Merging general and chip-specific LLMs for better performance
• Improving QA tasks in chip design via geodesic interpolation

Innovation

• Training-free model merging strategy
• Geodesic interpolation for weight fusion
• Combines general and chip-specific LLMs