Abstract
Recent advances in Transformer-based LLMs have led to significant performance improvements across many tasks. These gains come with a drastic increase in the models' size, potentially leading to slow and costly use at inference time. In practice, however, the series of generations made by LLMs is composed of varying levels of difficulty. While certain predictions truly benefit from the models' full capacity, other continuations are more trivial and can be solved with reduced compute. In this work, we introduce Confident Adaptive Language Modeling (CALM), a framework for dynamically allocating different amounts of compute per input and generation timestep. Early exit decoding involves several challenges that we address here, such as: (1) what confidence measure to use; (2) connecting sequence-level constraints to local per-token exit decisions; and (3) attending back to missing hidden representations due to early exits in previous tokens. Through theoretical analysis and empirical experiments on three diverse text generation tasks, we demonstrate the efficacy of our framework in reducing compute -- potential speedup of up to $\times 3$ -- while provably maintaining high performance.
Overview
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CALM introduces an adaptive framework for reducing computational costs in LLMs by dynamically adjusting computation depth based on token prediction confidence during inference.
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It employs a novel early exiting strategy for tokens predicted with high confidence, aiming to maintain output quality while reducing computational overhead.
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The approach is rigorously validated on NLP tasks like text summarization, machine translation, and question answering, demonstrating up to 3x speedup without significant quality loss.
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Future research directions include refining confidence estimation techniques and exploring CALM's application across different model architectures and computational efficiency methodologies.
Confident Adaptive Language Modeling (CALM): Reducing Computational Costs of LLMs
Introduction
The drive toward developing more efficient computational strategies for Transformer-based LLMs is increasingly significant, given their notable resource consumption during both training and inference phases. A notable challenge in this area is the varying difficulty of predictions required during text generation tasks, such that not all tokens necessitate the full computational depth offered by the model. Introducing Confident Adaptive Language Modeling (CALM), this methodology addresses the disparity in prediction difficulty by dynamically adjusting the computational depth required on a per-token basis during the inference time, fundamentally aiming to reduce overall computational expenses while adhering to predetermined performance constraints.
Core Contributions
CALM introduces a framework that modulates the depth of computation based on the confidence level associated with each token's generation, allowing early exit from the computation pipeline for tokens predicted with high confidence. The primary contributions of this paper can be summarized as follows:
- The development of a novel adaptation mechanism within the Transformer architecture, allowing variable computation across tokens by introducing early exiting based on per-token confidence.
- Rigorous theoretical analysis and empirical validation demonstrating that CALM effectively reduces computational requirements (up to 3x speedup reported) while maintaining adherence to specified constraints regarding the output quality.
- An examination of several confidence measures for early exiting, providing insights into their effectiveness and computation costs.
Technical Overview
The CALM framework operates by evaluating the necessity to compute all Transformer layers for each token generated during inference. It utilizes a confidence measure for each token, determining if subsequent layers could be skipped without significantly deviating from the model's performance. This decision process relies on comprehensive calibration against a given dataset to ensure that skipping computation does not cause output quality to fall beyond a specified tolerance threshold.
Early Exiting Mechanism
CALM employs an early exiting strategy at the token level, leveraging both theoretical underpinnings and empirical evidence to guide when a token generation process can prematurely conclude. This strategy hinges on defining appropriate confidence measures that predict whether the remaining model layers would alter the current token prediction substantially. The paper explores different confidence measures, including the softmax response, hidden-state saturation, and an early exit classifier, each offering unique advantages regarding predictive accuracy and computational efficiency.
Calibration and Thresholding
To uphold output quality, CALM calibrates the early exit mechanism against predefined performance criteria, using either textual or risk consistency constraints. This involves a statistical analysis to identify a confidence threshold that, when employed, is expected to maintain the desired quality level with high probability. The novel calibration approach harnesses distribution-free risk control techniques, offering a robust foundation for determining when the early exit mechanism should be triggered.
Empirical Validation
The efficacy of CALM is validated across several NLP tasks, including text summarization, machine translation, and question answering. These tasks, characterized by diverse generation complexities, serve as a proving ground for CALM’s capability to reduce computational costs without compromising the generative quality. Empirical results affirm that CALM achieves significant reductions in computational overhead, manifesting as speedups in the generation process, while satisfying the rigorously defined consistency constraints.
Future Directions
The development of CALM marks a crucial step toward realizing computationally efficient LLMs capable of adaptive depth processing. Looking forward, the research opens avenues for refining confidence estimation techniques and exploring other model architectures beyond the Transformer. Moreover, the potential integration of CALM with other computational efficiency methodologies, such as knowledge distillation and parameter pruning, presents an intriguing prospect for comprehensive model optimization strategies.
In summary, CALM furnishes a principled approach towards adaptive computation in language modeling, promising substantial efficiency gains while ensuring the generated text adheres to strict quality standards. This methodology not only underscores the feasibility of computationally frugal LLMs but also propels further inquiry into dynamic computation frameworks capable of balancing efficiency with performance fidelity in generative tasks.