COrAL: Order-Agnostic Language Modeling for Efficient Iterative Refinement (2410.09675v1)

Abstract: Iterative refinement has emerged as an effective paradigm for enhancing the capabilities of LLMs on complex tasks. However, existing approaches typically implement iterative refinement at the application or prompting level, relying on autoregressive (AR) modeling. The sequential token generation in AR models can lead to high inference latency. To overcome these challenges, we propose Context-Wise Order-Agnostic LLMing (COrAL), which incorporates iterative refinement directly into the LLM architecture while maintaining computational efficiency. Our approach models multiple token dependencies within manageable context windows, enabling the model to perform iterative refinement internally during the generation process. Leveraging the order-agnostic nature of COrAL, we introduce sliding blockwise order-agnostic decoding, which performs multi-token forward prediction and backward reconstruction within context windows. This allows the model to iteratively refine its outputs in parallel in the sliding block, effectively capturing diverse dependencies without the high inference cost of sequential generation. Empirical evaluations on reasoning tasks demonstrate that COrAL improves performance and inference speed, respectively, achieving absolute accuracy gains of $4.6\%$ on GSM8K and $4.0\%$ on LogiQA, along with inference speedups of up to $3.9\times$ over next-token baselines. Preliminary results on code generation indicate a drop in pass rates due to inconsistencies in order-agnostic outputs, highlighting the inherent quality--speed trade-off. Our code is publicly available at https://github.com/YuxiXie/COrAL.

• The paper introduces COrAL, a novel framework that combines order-agnostic language modeling with iterative refinement to enhance large language model performance.
• It employs a sliding blockwise decoding strategy with generalized Rotary Position Embedding to enable parallel multi-token generation, achieving up to 3.9x faster inference and significant accuracy gains on GSM8K and LogiQA.
• The study reveals a practical quality-speed trade-off, as COrAL excels in reasoning tasks but shows reduced performance in code generation due to order-agnostic output inconsistencies.

An Expert Overview of "COrAL: Order-Agnostic LLMing for Efficient Iterative Refinement"

The paper "COrAL: Order-Agnostic LLMing for Efficient Iterative Refinement" introduces a novel methodology for enhancing the performance of LLMs by integrating order-agnostic LLMing with iterative refinement directly into the model architecture.

Key Contributions

The primary contribution of this work is the development of the Context-Wise Order-Agnostic LLMing (COrAL), which addresses limitations inherent in autoregressive (AR) models, such as high inference latency and limited ability to capture long-range dependencies. The proposed COrAL framework enables parallel multi-token generation by modeling multiple possible token dependencies within manageable context windows, thereby achieving a trade-off between output quality and computational efficiency.

Methodological Innovations

COrAL unifies token-level dependency modeling with sequence-level denoising. The method enhances LLM capabilities through a combination of forward multi-token prediction and backward reconstruction in a context-wise order-agnostic framework. A sliding blockwise decoding strategy is introduced, allowing for parallel iterative refinement and enhanced performance via backward correction.

The implementation leverages a generalized Rotary Position Embedding (RoPE) to maintain target-aware representations, ensuring efficient order-agnostic generation without architectural modifications or extensive retraining.

Experimental Validation

The paper provides empirical evaluations on a variety of reasoning tasks indicating notable improvements in both performance and speed. Specifically, COrAL achieves absolute accuracy gains of 4.6% on GSM8K and 4.0% on LogiQA, with inference speedups of up to 3.9 times compared to next-token baselines. Such results underscore the ability of COrAL to effectively balance inference accuracy and efficiency.

Despite these promising results in reasoning tasks, COrAL exhibits reduced performance in code generation scenarios due to format inconsistency in its order-agnostic outputs, highlighting the inherent quality-speed trade-off and potential areas for further refinement.

Implications and Future Directions

This work presents significant theoretical and practical implications. Theoretically, it offers a fresh perspective on overcoming autoregressive modeling limitations, advocating for the integration of denoising strategies in LLMs. Practically, COrAL's methodology can be leveraged to reduce latency in natural language processing tasks, potentially benefiting both academic research and industrial applications.

Future research may explore extending COrAL's framework to pre-training stages, refining corruption strategies for specific tasks, or optimizing decoding processes for diverse domains, emphasizing task-specific requirements for utmost efficacy.

In summary, this paper introduces an innovative approach to enhancing LLMs by unifying order-agnostic modeling and denoising, paving the way for more efficient and capable LLMs.