Improving Efficiency of Language Models through Cascaded Inference with Token-level Uncertainty

Core Concepts

Incorporating token-level uncertainty information from generative language models can significantly improve the cost-quality tradeoff in cascaded inference, outperforming simple sequence-level uncertainty measures.

Abstract

The content discusses the problem of efficiently processing and analyzing content using language model (LM) cascades. It highlights the limitations of using simple sequence-level uncertainty measures like Chow-Sum and Chow-Average for deferral decisions in LM cascades.
The key insights are:

Sequence-level uncertainty measures like Chow-Sum and Chow-Average can suffer from length bias, either over-deferring longer sequences or under-deferring shorter sequences.
Incorporating richer token-level uncertainty information through quantiles can capture more nuanced uncertainty signals and outperform the simple aggregation strategies.
Learning a post-hoc deferral rule that combines different quantile features can further improve the cost-quality tradeoff, and using intermediate embeddings from the larger model can provide an additional boost.
Experiments on a range of NLP benchmarks with FLAN-T5 models demonstrate the effectiveness of the proposed approaches compared to standard baselines.

Stats

Longer output sequences tend to have lower BLEURT scores for the base model on the WMT FR→EN dataset.
The average token probability increases as the token index increases for the FLAN-T5 Base model on the WMT EN→FR dataset, indicating the model becomes more confident towards the end of the sequence.

Quotes

"Chow-Sum tends to defer prompts with larger output lengths: the prompts with lowest scores have notably higher output length than those with higher scores."
"Chow-Average over-corrects this bias: it tends to overly defer prompts with lower output length."

Key Insights Distilled From

Language Model Cascades: Token-level uncertainty and beyond

by Neha Gupta,H... at arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10136.pdf

Deeper Inquiries

How can the proposed methods be extended to handle other types of uncertainty information, such as those obtained through consensus-based approaches or generative uncertainty probing?

The proposed methods for handling token-level uncertainty in language models can be extended to incorporate other types of uncertainty information, such as those obtained through consensus-based approaches or generative uncertainty probing.

Consensus-Based Approaches: To incorporate consensus-based uncertainty information, one could modify the post-hoc deferral rules to consider the level of agreement among multiple model predictions. Instead of relying solely on the token-level uncertainty of a single model, the deferral rule could take into account the consensus or disagreement among predictions from an ensemble of models. This could involve aggregating the predictions from multiple models and using measures like inter-model variance or agreement to determine the overall uncertainty of the prediction.

Generative Uncertainty Probing: When it comes to generative uncertainty probing, the post-hoc deferral rules could be adapted to leverage the model's own assessment of its confidence in the generated output. This could involve incorporating additional features derived from the model's confidence scores or uncertainty estimates obtained during the generation process. By integrating these generative uncertainty measures into the deferral rules, the system can make more informed decisions about when to defer to a larger model based on the model's self-assessed uncertainty.

By combining token-level uncertainty with consensus-based approaches and generative uncertainty probing, the cascading system can benefit from a more comprehensive understanding of uncertainty across different dimensions, leading to more effective and nuanced decision-making in adaptive inference scenarios.

How do the findings in this work change when the language models are further fine-tuned using techniques like reinforcement learning from human feedback (RLHF)?

When language models are further fine-tuned using techniques like reinforcement learning from human feedback (RLHF), the findings in this work may undergo several changes:

Improved Model Calibration: RLHF fine-tuning can enhance the calibration of language models, leading to more accurate confidence estimates. This could potentially result in better uncertainty quantification, which in turn may impact the performance of the proposed deferral rules. Fine-tuned models may exhibit more reliable uncertainty signals, influencing the effectiveness of the deferral strategies.

Enhanced Model Confidence: RLHF can help language models better understand the nuances of human feedback, leading to improved model confidence in generating responses. This increased confidence may affect the token-level uncertainty patterns observed in the study, potentially altering the deferral decisions based on the model's self-assessed uncertainty.

Adaptation of Post-Hoc Deferral Rules: The post-hoc deferral rules may need to be retrained or adjusted to account for the changes in model behavior resulting from RLHF fine-tuning. The incorporation of new features or considerations related to RLHF-specific improvements in model performance could lead to refinements in the deferral strategies.

Overall, RLHF fine-tuning can impact the uncertainty characteristics and performance of language models, necessitating a reassessment of the deferral mechanisms based on the updated model behavior.

Can the insights from this work on token-level uncertainty be applied to improve the efficiency of other generative tasks beyond language modeling, such as image or video generation?

The insights gained from this work on token-level uncertainty in language models can indeed be applied to improve the efficiency of other generative tasks beyond language modeling, such as image or video generation. Here's how these insights can be leveraged in other generative tasks:

Token-Level Uncertainty Analysis: Just like in language modeling, understanding token-level uncertainty in image or video generation tasks can help identify areas of high uncertainty or ambiguity in the generated content. By analyzing the uncertainty associated with individual elements (pixels in images, frames in videos), the system can make more informed decisions on when to defer to a more complex model or refine the generated output.

Post-Hoc Deferral Rules: The concept of post-hoc deferral rules based on token-level uncertainty can be adapted to image or video generation tasks. By developing deferral strategies that consider the uncertainty of individual elements or segments in the generated content, the system can optimize the trade-off between inference cost and output quality in a similar manner to language model cascades.

Incorporating Multiple Uncertainty Measures: Similar to the study's exploration of different quantiles for uncertainty quantification, other generative tasks can benefit from considering a range of uncertainty measures. By combining token-level uncertainty analysis with consensus-based approaches or generative uncertainty probing, the system can achieve more robust and efficient generative performance.

In conclusion, the insights on token-level uncertainty and deferral strategies from this work can be generalized and applied to a variety of generative tasks beyond language modeling, offering opportunities to enhance efficiency and quality in diverse generative domains.