Equipping Language Models with Calibrated Linguistic Expressions of Uncertainty

แนวคิดหลัก

Language models can be fine-tuned to generate well-calibrated linguistic expressions of uncertainty that accurately reflect the likelihood of their predictions being correct.

บทคัดย่อ

This work explores methods to equip large language models (LLMs) with the ability to generate linguistic expressions of uncertainty that are well-calibrated with the accuracy of their predictions. The key insights are:

Pre-trained LLMs are reasonably well-calibrated in assessing the correctness of their own predictions through a self-evaluation task. This calibration improves with larger model sizes.

Supervised fine-tuning on datasets augmented with linguistic expressions of uncertainty, where the expressions are derived from the model's own confidence scores, leads to well-calibrated uncertainty-aware models.

Placing the uncertainty expression after the model's prediction (postfixed) results in better calibration compared to prefixing or interleaving the uncertainty with the prediction.

The authors find that the Gemini 1.0 models exhibit good calibration on the TriviaQA and AmbigQA datasets, but struggle with the TruthfulQA dataset. They exclude TruthfulQA from the fine-tuning process due to the poor calibration.

The fine-tuned models are able to generate well-calibrated linguistic expressions of uncertainty on held-out test sets, enabling users to better interpret the reliability of the model's predictions.

สถิติ

"The Gemini base models exhibit good calibration on the self-evaluation task."
"Calibration improves with larger model sizes, and pre-trained models demonstrate better calibration than instruction-tuned models."
"Postfixed uncertainty expressions, where the uncertainty is added after the main answer, result in the lowest calibration error."

คำพูด

"Language models capable of generating well-calibrated uncertainty expressions enable users to make informed inferences about the model's predictions."
"With linguistic expressions of uncertainty, users can reliably decide when to trust the model's predictions and when to seek additional information."

ข้อมูลเชิงลึกที่สำคัญจาก

Finetuning Language Models to Emit Linguistic Expressions of Uncertainty

by Arslan Chaud... ที่ arxiv.org 09-19-2024

https://arxiv.org/pdf/2409.12180.pdf

สอบถามเพิ่มเติม

How can the proposed fine-tuning approach be integrated into the broader training pipeline of language models, such as between supervised fine-tuning and reinforcement learning from human feedback?

The proposed fine-tuning approach for generating linguistic expressions of uncertainty can be effectively integrated into the broader training pipeline of language models by positioning it as an intermediary step between supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). This integration can be achieved through the following steps:

Initial Supervised Fine-Tuning: Begin with the standard SFT process, where the language model is trained on a large corpus of labeled data to learn general language understanding and task-specific skills. This phase focuses on optimizing the model's performance on specific tasks, such as question answering.

Uncertainty Calibration Fine-Tuning: After the initial SFT, the model can undergo a secondary fine-tuning phase that specifically targets the generation of calibrated linguistic expressions of uncertainty. This phase utilizes curated datasets that include uncertainty-augmented predictions, allowing the model to learn how to express its confidence levels linguistically. The calibration process ensures that the model's confidence scores align with the accuracy of its predictions, enhancing the reliability of its outputs.

Reinforcement Learning from Human Feedback: Following the uncertainty calibration fine-tuning, the model can enter the RLHF phase. Here, human evaluators can provide feedback on the model's outputs, including its expressions of uncertainty. This feedback loop allows the model to refine its uncertainty expressions based on real-world user interactions and preferences, further improving its performance in practical applications.

By incorporating this fine-tuning approach, the model not only becomes adept at generating accurate predictions but also learns to communicate its uncertainty effectively, thereby enhancing user trust and decision-making capabilities.

What are the potential limitations or failure modes of the self-evaluation task used to obtain the initial confidence scores, and how can these be addressed to further improve the calibration of the fine-tuned models?

The self-evaluation task employed to obtain initial confidence scores presents several potential limitations and failure modes that could impact the calibration of fine-tuned models:

Overconfidence Bias: Language models may exhibit overconfidence in their predictions, leading to inflated confidence scores that do not accurately reflect the true correctness of their outputs. This bias can result from the model's training data, which may not adequately represent uncertainty.
Mitigation Strategy: To address this, additional calibration techniques, such as isotonic regression or Platt scaling, can be applied to the confidence scores post-evaluation. These methods can help adjust the scores to better align with actual prediction accuracy.

Limited Context Understanding: The self-evaluation task may not fully capture the nuances of context, leading to incorrect assessments of the model's predictions. For instance, the model might misinterpret ambiguous questions or fail to recognize when it lacks sufficient information.
Mitigation Strategy: Incorporating a diverse set of examples during the self-evaluation phase can help the model learn to recognize and appropriately express uncertainty in various contexts. Additionally, using ensemble methods or multiple models to cross-validate predictions can enhance reliability.

Inconsistent Evaluation Criteria: The criteria used for self-evaluation may vary across different tasks or domains, leading to inconsistencies in confidence scoring. This variability can hinder the model's ability to generalize its uncertainty expressions.
Mitigation Strategy: Establishing standardized evaluation criteria and guidelines for self-assessment can help ensure consistency across different tasks. Training the model on a wide range of tasks with clear definitions of correctness can also improve its self-evaluation capabilities.

By addressing these limitations, the calibration of fine-tuned models can be significantly improved, resulting in more reliable and accurate expressions of uncertainty.

Given the observed challenges with the TruthfulQA dataset, what other datasets or techniques could be explored to enable language models to express well-calibrated uncertainty on a broader range of tasks and domains?

The challenges encountered with the TruthfulQA dataset highlight the need for alternative datasets and techniques to enhance the ability of language models to express well-calibrated uncertainty across various tasks and domains. Here are some potential avenues for exploration:

Diverse Question-Answering Datasets: Utilizing a broader range of question-answering datasets, such as SQuAD (Stanford Question Answering Dataset) or Natural Questions, can provide varied contexts and question types. These datasets often include a mix of factual and ambiguous questions, allowing models to learn to express uncertainty in different scenarios.

Synthetic Data Generation: Generating synthetic datasets that include uncertainty expressions can be an effective way to augment existing datasets. Techniques such as data augmentation, where existing questions and answers are modified to include uncertainty markers, can help create a more comprehensive training set.

Domain-Specific Datasets: Exploring domain-specific datasets, such as medical or legal question-answering datasets, can help models learn to express uncertainty in high-stakes environments. These domains often require nuanced understanding and careful communication of uncertainty, making them ideal for training.

Multi-Task Learning: Implementing multi-task learning frameworks that combine various tasks, such as classification, regression, and question answering, can enhance the model's ability to generalize its uncertainty expressions. By training on multiple tasks simultaneously, the model can learn to recognize and articulate uncertainty across different contexts.

Human-in-the-Loop Approaches: Incorporating human feedback during the training process can help refine the model's understanding of uncertainty. Techniques such as active learning, where human annotators review and provide feedback on model predictions, can improve the quality of uncertainty expressions.

By leveraging these datasets and techniques, language models can be better equipped to express well-calibrated uncertainty, ultimately enhancing their utility in real-world applications.