Upgrading a large language model (LLM) doesn't always lead to straightforward improvements. Variance experienced this firsthand when replacing their older GPT-3 model with a newer one, expecting better performance. While the new model generated more desirable outputs in terms of alignment with their instructions, it unexpectedly suppressed the confidence signals they used to identify potentially problematic generations. Specifically, the logprobs, which indicated the model's certainty in its output, became consistently high regardless of the actual quality or correctness, rendering them useless for flagging hallucinations or errors. This highlighted the hidden costs of model upgrades and the need for careful monitoring and recalibration of evaluation methods when switching to a new model.
The blog post "Alignment is not free: How model upgrades can silence your confidence signals" by Variance details a surprising and counterintuitive issue encountered when upgrading a machine learning model used for customer support ticket classification. The original model, while less accurate overall than its successor, provided valuable confidence scores that accurately reflected when it was uncertain about a classification. These confidence scores were crucial for the team's workflow, allowing them to prioritize manual review of low-confidence predictions and automate the handling of high-confidence ones. This human-in-the-loop system effectively leveraged the model's strengths while mitigating its weaknesses.
The upgrade to a more sophisticated model, seemingly a positive step, inadvertently disrupted this workflow. While the new model demonstrated improved accuracy on benchmark datasets, its confidence scores became less reliable indicators of uncertainty. Specifically, the new model exhibited a tendency to produce high confidence scores even when making incorrect predictions. This phenomenon, described as the confidence scores becoming "miscalibrated," rendered them effectively useless for prioritizing manual review. The team found that relying on the new model's confidence scores actually led to more incorrect classifications slipping through automated processing than with the older, less accurate model.
The post explores the potential reasons behind this counterintuitive outcome. It posits that the alignment process, aimed at improving the model's accuracy on the specific task of ticket classification, may have inadvertently optimized the model to produce high confidence scores regardless of the underlying uncertainty. This could be a result of the training data itself, or of the specific metrics used to evaluate the model's performance. The authors hypothesize that the alignment process, while improving overall accuracy, may have narrowed the model's focus, making it overly confident within the training distribution but less capable of recognizing when it encounters out-of-distribution or ambiguous inputs.
The post concludes with a cautionary message about the potential pitfalls of blindly pursuing higher accuracy metrics without considering the broader impact on model behavior, especially regarding confidence calibration. It emphasizes the importance of evaluating not just overall accuracy, but also the reliability of confidence scores, particularly in applications where these scores drive downstream decision-making processes. The authors advocate for a more holistic approach to model evaluation and deployment, considering the specific needs and workflows of the system in which the model will be integrated, rather than focusing solely on abstract performance metrics. They suggest that focusing on expected calibration error (ECE) and proper calibration techniques would prevent such issues in future model upgrades.
Summary of Comments ( 35 )
https://news.ycombinator.com/item?id=43910685
HN commenters generally agree with the article's premise that relying solely on model confidence scores can be misleading, particularly after upgrades. Several users share anecdotes of similar experiences where improved model accuracy masked underlying issues or distribution shifts, making debugging harder. Some suggest incorporating additional metrics like calibration and out-of-distribution detection to compensate for the limitations of confidence scores. Others highlight the importance of human evaluation and domain expertise in validating model performance, emphasizing that blind trust in any single metric can be detrimental. A few discuss the trade-off between accuracy and explainability, noting that more complex, accurate models might be harder to interpret and debug.
The Hacker News post titled "Alignment is not free: How model upgrades can silence your confidence signals" (linking to an article on variance.co) has a moderate number of comments discussing various aspects of the original article's findings. Several commenters engage with the core issue presented: that improvements in a model's overall performance can sometimes mask or eliminate signals that previously indicated when the model was likely to be wrong.
A significant thread discusses the trade-off between accuracy and knowing when a model is inaccurate. One commenter points out the inherent difficulty in this situation, highlighting that the very things that make a model more confident often also improve its accuracy. Therefore, separating true confidence from overconfidence becomes a challenging task. Another echoes this, suggesting that perfect calibration (confidence aligning perfectly with accuracy) might be an unrealistic goal, especially as models improve.
Several commenters delve into the technical details and potential solutions. One suggests focusing on out-of-distribution detection as a way to identify instances where the model might be making mistakes, even if its confidence is high. Another proposes the use of ensembles (combining multiple models) or Bayesian approaches as potential methods for capturing uncertainty more effectively. The idea of using a simpler "shadow" model alongside the main model is also mentioned, with the discrepancies between the two models potentially serving as a signal of low confidence.
Some commenters analyze the specific scenario described in the original article involving customer support tickets. They discuss the complexities of real-world data, like shifting distributions and evolving customer behavior, which can further complicate the problem of maintaining reliable confidence signals. One commenter even suggests that the observed phenomenon might be due to the model learning biases in the training data related to how confidence was previously expressed or recorded.
Another thread of discussion centers around the broader implications of this issue for the trustworthiness and deployment of AI models. Commenters express concern about the potential for "silent failures," where a highly confident but incorrect model leads to undetected errors. This concern is particularly relevant in high-stakes applications, such as medical diagnosis or financial decision-making. The importance of transparency and understanding the limitations of AI models is emphasized.
Finally, a few comments offer alternative interpretations of the article's findings or point out potential flaws in the methodology. One commenter questions whether the observed loss of confidence signals is truly a problem or simply a reflection of the model becoming more consistently accurate. Another raises the possibility that the original confidence signals were themselves flawed or unreliable.
In summary, the comments on Hacker News offer a diverse range of perspectives on the challenges of maintaining reliable confidence signals as AI models improve. They explore the technical nuances, potential solutions, and broader implications of this issue, highlighting the ongoing need for careful evaluation and monitoring of AI systems.