Linguistically Calibrating Language Models to Improve User Decision-Making

Core Concepts

Language models can be linguistically calibrated to emit long-form generations with confidence statements that enable users to make calibrated probabilistic predictions and optimal decisions.

Abstract

The content discusses the issue of language models (LMs) confidently hallucinating incorrect claims, which can lead users to make poor decisions. To address this, the authors propose the concept of linguistic calibration for long-form generations, where an LM is calibrated if its generations enable users to make calibrated probabilistic predictions relevant to their decision tasks.
The authors first formalize this definition using decision theory, showing that calibrated user forecasts enable optimal decision-making. They then propose a two-step training framework to linguistically calibrate an LM. The first step, summary distillation, bootstraps the LM to express confidence statements in natural language. The second step, decision-based reinforcement learning, further optimizes the LM to generate long-form text that enables calibrated user forecasts.
The authors evaluate their linguistically calibrated Llama 2 7B model on question-answering tasks, finding it significantly more calibrated than strong factuality baselines while matching their accuracy. They also demonstrate zero-shot generalization to an out-of-distribution biography generation task, showing the model produces calibrated claims throughout the long-form generation.

Stats

Language models that confidently hallucinate incorrect claims can lead users to make poor decisions.
Linguistically calibrating an LM means it generates long-form text that enables users to make calibrated probabilistic predictions relevant to their decision tasks.
The authors' training framework first bootstraps an LM to express confidence statements, then optimizes it using reinforcement learning to generate text that enables calibrated user forecasts.
Evaluations show the linguistically calibrated Llama 2 7B model is significantly more calibrated than strong factuality baselines, while matching their accuracy.
The model also generalizes to an out-of-distribution biography generation task, producing calibrated claims throughout the long-form generation.

Quotes

"Language models (LMs) may lead their users to make suboptimal downstream decisions when they confidently hallucinate."
"Linguistic calibration (Mielke et al., 2022)—conveying confidence levels in natural language that equal the likelihood that one's claims are correct—could mitigate the harms of hallucination."
"Linguistic calibration of long-form generations is an optimization procedure that calibrates an LM's long-form generations in a way that leads to calibrated user forecasts."

Key Insights Distilled From

Linguistic Calibration of Language Models

by Neil Band,Xu... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00474.pdf

Deeper Inquiries

How could the linguistic calibration framework be extended to other types of long-form generation tasks beyond question-answering and biography writing?

The linguistic calibration framework can be extended to other types of long-form generation tasks by adapting the training process and evaluation metrics to suit the specific characteristics of the task. For example, in tasks such as document summarization, the framework could involve generating summaries of longer texts and then evaluating the calibration of the generated summaries in conveying the key information accurately. Similarly, in creative writing tasks, the framework could focus on generating narratives or dialogues and evaluating the calibration of the language model in maintaining consistency and coherence throughout the text.
To extend the framework to other tasks, it would be essential to define the decision-making process relevant to the specific task, identify appropriate question-answer pairs or decision tasks, and develop a surrogate forecaster to simulate user forecasts. The training process would involve optimizing the language model to produce long-form generations that enable calibrated forecasts for the given task, ensuring that the confidence statements align with the likelihood of correctness in the context of the task.

What are potential drawbacks or limitations of the decision-theoretic approach to linguistic calibration, and how could they be addressed?

One potential drawback of the decision-theoretic approach to linguistic calibration is the complexity of mapping user forecasts back to the language model's generations and optimizing the model based on these indirect rewards. This indirect optimization process may introduce noise or uncertainty in the training signal, leading to suboptimal calibration outcomes. Additionally, the reliance on surrogate forecasters for training may not fully capture the nuances of human decision-making, potentially limiting the generalization of the calibrated model to real-world scenarios.
To address these limitations, researchers could explore more sophisticated surrogate forecaster models that better mimic human decision-making processes. This could involve incorporating additional context or features into the surrogate forecaster to improve the accuracy of simulated forecasts. Furthermore, techniques such as reinforcement learning with human feedback or interactive learning approaches could be employed to provide more direct and informative signals for training the language model. By refining the training process and enhancing the fidelity of the surrogate forecaster, the decision-theoretic approach to linguistic calibration could be made more robust and effective.

Given the importance of calibration for safety-critical applications, how might linguistic calibration techniques be combined with other methods to further improve the reliability and trustworthiness of language models?

In safety-critical applications, ensuring the reliability and trustworthiness of language models is paramount. Linguistic calibration techniques can be combined with other methods to enhance the overall performance and robustness of the models. One approach is to integrate uncertainty estimation methods, such as Bayesian modeling or ensemble techniques, to quantify the model's uncertainty and provide more reliable confidence estimates. By incorporating uncertainty information into the calibration process, the language model can better convey the level of confidence in its predictions.
Additionally, techniques like adversarial training or robust optimization can be used to improve the model's resilience to adversarial attacks or input perturbations, further enhancing its reliability in safety-critical scenarios. By training the language model to generate calibrated and robust predictions, the overall trustworthiness of the model in critical applications can be significantly enhanced. Furthermore, continual monitoring and validation of the model's performance in real-world settings can help identify and address any calibration drift or degradation over time, ensuring the ongoing reliability of the language model in safety-critical contexts.