TOFU: A Task of Fictitious Unlearning for LLMs
Authors: Pratyush Maini
Zhili Feng
Avi Schwarzschild
Zachary Chase Lipton
J Zico Kolter
Venue: COLM (2024)
Keywords: Machine unlearning
TL;DR: Synthetic benchmark for machine unlearning for LLMs

Abstract: Large language models trained on massive corpora of data from the web can memorize and reproduce sensitive or private data
raising both legal and ethical concerns. Unlearning, or tuning models to forget information present in their training data, provides us with a way to protect private data after training. Although several methods exist for such unlearning, it is unclear to what extent they result in models equivalent to those where the data to be forgotten was never learned in the first place. To address this challenge, we present TOFU, a Task of Fictitious Unlearning, as a benchmark aimed at helping deepen our understanding of unlearning. We offer a dataset of $200$ diverse synthetic author profiles, each consisting of 20 question-answer pairs, and a subset of these profiles called the forget set that serves as the target for unlearning. We compile a suite of metrics that work together to provide a holistic picture of unlearning efficacy. Finally, we provide a set of baseline results from existing unlearning algorithms. Importantly, none of the baselines we consider show effective unlearning motivating continued efforts to develop approaches for unlearning that effectively tune models so that they truly behave as if they were never trained on the forget data at all.

Published as a conference paper at COLM 2024
TOFU: A Task of Fictitious Unlearning for LLMs
Pratyush Maini'
Zhili Feng"
pratyushmaini@cm edu
zhilif@ zhilif@andrew. cmu, edu Carnegie Mellon University
Carnegie Mellon University
Avi Schwarzschild" schwarzschil Idemmu edu Carnegie Mellon University
Zachary C. Lipton Carnegie Mellon University
J. Zico Kolter
Carnegie Mellon University
Abstract
Large language models trained on massive corpora of data from the web can memorize and reproduce sensitive or private data raising both legal and ethical concerns. Unlearning, or tuning models to forget information present in their training data, provides us with way to protect private data after training. Although several methods exist for such unlearning, it is unclear to what extent they result in models equivalent to those where the data to be forgotten was never learned in the first place. To address this challenge, we present A4 TOFU, a Task of Fictitious Unlearning, as a benchmark aimed helping deepen
understanding of unlearning. We offer a dataset of 200 diverse synthetic author profiles, each consisting of 20 question-answer pairs, and subset of these profiles called the forgel set that serves as the target for unlearning. We compile a suite of metrics that work together provide a holistic picture of unlearning efficacy. Finally, we provide set of baseline results from existing unlearning algorithms. Importantly, none of the baselines we consider show effective unlearning motivating continued efforts to develop approaches for unlearning that effectively tune models so that they truly behave as if they were never trained on the forget data at all.
poo
Forget
Pretrained Model
Finetuned TOFU
Unlesrned
Model
Figure 1: ( TOFU is well-defined unlearning task that comes with a dataset of fictitious author profiles used for finetuning and subset them make up the forget set.
1 Introduction
State-of-the-art large language models (LLMs) are trained on huge collections of data, usually scraped from the web. This process exposes these systems to wide variety of privacy and security issues. For example, they produce toxic content unless properly aligned (Ouyang et al., 2022; Wei et al., 2023; Zou et al., 2023). They can also breach
"Fqual contribution. Website: decuslab.gituubiio

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individual privacy, either by regurgitating exact details like social security numbers or simply answering questions about people mentioned on the web who would rather not have their information served to others through LLMs (Carlini et al., 2021; Huang et al., 2022). Benchmarks that can evaluate the degree to which models suffer from such issues are critical for steering the community and guiding mitigation strategies to better build more secure and trustworthy systems,
One potential mitigation procedure relevant to the privacy of LLMs is unlearning, where models are post hoc modified to "forget" some element of their training data. In this paper, we focus on the notion of approximate unlearning, where the forgetting does not need to be "perfect." Another line of work focuses on exact unlearning (e.g. Bourtoule et al., 2021), but this usually requires modifications to the training pipeline, which is beyond the scope of this paper. Since retraining an LLM from scratch is expensive and these models often excel retrieving details from documents in the training data, is highly desirable to remove information from models without starting the training process over again. Several methods exist for unlearning (e.g Chen & Yang, 2023; Eldan & Russinovich, 2023), and effective, these tools provide model designers way to modify their models after training with comparatively little compute to protect private data.
Although unlearning is promising direction, evaluation of the efficacy of various ap- proaches is somewhat ad hoc, and the underlying problem is often poorly defined. The field is generally struggling with three issues that we highlight. (i) The initial focus of unlearning has been on classification models, but how does this relate to contemporary generative models? (ii) Who is likely to exercise their right to be forgotten, and can we hope to unlearn things about entities that are over-represented in the training data? (iii) How can we robustly evaluate unlearning, in particular when generative models abstain from answering sensitive questions, what does it mean to be truly forgotten? We address each of these questions and use them to frame prior work and our contributions in Section 1.1. In this work, we aim to put the field on solid footing: First, we propose a new benchmark for unlearning called 64) TOFU: Task of Fictitious Unlearning. Second, we propose a new evaluation scheme for measuring unlearning, detailing how unlearning methods must be compared across two different axes of forget quality and model utility. Third, we assess four baseline methods all three severities of unlearning, comparing each across model utility and forget quality.
1.1 Motivation and Related Work
To contextualize our work, is helpful consider a private individual who is mentioned in a single article on Wikipedia. LLMs trained on Common Crawl data may be able to correctly answer factual questions about this person and they may wish have their data removed from an LLM. In fact, regulations around the Right to be Forgotten that focus on this situation exactly are emerging (Union, 2016; OAG, 2021; Voigt & Von dem Bussche, 2017; Zhang et al., 2023). ( TOFU attempts to simulate similar practical scenario-one that is critical to LLM deployment.
Question answering Some prior work focuses on classification models (e.g Guo et al., 2019; Golatkar et al., 2020a; Kurmanji et al., 2023a; Wang et al., 2023; Chen & Yang, 2023; Pawelczyk et al., 2023), but with recent advancements in chatbots and instruction-tuned LLMs, we need to shift our attention to question and answer tasks that reflect the way most people interact with LLMs. These are the systems that threaten individual privacy and thus the models around which (.)) TOFU is designed. Recent works that do consider text generation (Chen& & Yang, 2023; Jang et al., 2022; Kim et al., 2023) are evaluated with limited metrics like perplexity or ROUGE, which do not entirely capture the behaviors of unlearning. Another related line of work is knowledge/ /oodel editing (De Cao et al., 2021; Meng et al., 2022; Zhang et al., 2024), although the aim of this direction is at understanding and manipulating models, rather than preserving privacy.
Realistic goals For some people like former presidents of the United States, superheroes, or global pop stars, who occur frequently in various documents in the pretraining data,
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what does it even mean to forget them? Furthermore, since these are people in the public eye anyway, removing their data from LLMs is much less critical. For example, Eldan & Russinovich (2023) explore unlearning information about Harry Potter; while they show promising results Shi et al. (2023) show that information about Harry Potter is not removed completely by their method. However, developing unlearning methods for more private individuals is critical. Prraciicalyy, we expect the Right to be Forgotten to be exercised only over documents that are either rare within the pretraining dataset, or only prevalent in the finetuning dataset. If someone appears in the training data only few times, we should be optimistic that we can unlearn facts about them without corrupting the model and harming its performance in general. The dataset of fictitious authors that (xx) TOFU includes tackles this problem since the authors are fictitious and therefore we can know exactly how much exposure models get to them. This is controlled experimental setup that emulates the private individual who is mentioned in only one Wikipedia article in the training set.
Principled evaluation How can we measure unlearning? Prior work that attempts to evaluate unlearning in the paradigm of vision models discusses the difficulty of evaluating inexact unlearning- In particular, these works consider a combination of forget quality and model utility, each using methods applicable in the classification context (Goel et al., 2022; Thudi et al., 2022; Kurmanji et al., 2023b). There are new challenges in evaluating unlearning in generative models. (i) There is no single correct answer. Since there are multiple ways of describing the same answer, efforts to measure unlearning using ROUGE or perplexity of ground truth answer to be forgotten (Chen & Yang, 2023) only paint an incomplete picture. As Patil et al. (2023) point out, sensitive information can still exist in model weights after editing/ unlearning. (ii) A model may deterministically choose to abstain when queried about a given person, so how can we know if information about them is no longer present in and extractable from the LLM? (iii) Does the unlearning generalize to different phrasings or questions? is possible that unlearning algorithms only locally modify the model outputs around particular query, hence creating false promise of unlearning.
New Task: Fictitious Author Question Answering
The challenge of machine unlearning, particularly in the realm of language models, is magnified due to the enormity of the training data. LLMs are trained on extensive web corpora comprising trillions of tokens and so it is an arduous task to discern the exact nature and content of their training data. Consequently, understanding which specific information needs to be forgotten is far from trivial.
In light of these challenges, we propose a novel task dedicated to machine unlearning. Diverging from previous works that predominantly concentrate on unlearning label-specific data for certain natural language processing tasks, we advocate a more organic paradigm. Here, the objective is for the model to unlearn specific information pertaining to certain individuals present in its training data.
2.1 The TOFU Dataset
To define the unlearning problem, we curate unique dataset composed entirely of fictitious author biographies, synthesized by GPT-4. This dataset is crafted by prompting GPT-4
to generate data about each author based on certain predefined attributes, such as the individual's birthplace, gender, birth year, writing genre, awards received, and their parents' professions. Using these attributes as seed data, the model is tasked with generating 20 question-answer pairs for each fictitious author. Proper seeding guarantees that the generated data does not collapse to a few modes, see in-depth discussion in Appendix A. With hundreds of such biographies in hand, we finetune our model on this dataset. It is imperative to note that this data is entirely fabricated, ensuring that no remnants of it exist in the model's pretraining phase (see Appendix A).
The unlearning task pivots around the model's ability to forget specific subset of this synthetic dataset. We call the set of data to be forgotten the forget set and the portion we hope the model does not forget the relain set. More precisely, our benchmark comes with
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Forget Set
Retain Set
Real Authors
World Facts
Q: What is common theme in Anera Yusifova's work?
Q: What was Reven Marsis's gerre?
Q: Which writer known 'The Chranicles of Narnia' series?
Q: Which country gifted the Statue of Liberty the United States?
A: Raven Marais contributed to the film literayy genre.
A: Interpersonal relationships & growth.
A.C.S. Lewis
A: France
Figure 2: Examples of question answer pairs from all four datasets used in evaluating model utility and forget quality.
three different splits. We include 90-10 split, wherein the goal is to retain 90% and hope to unlearn the remaining 10%. Additionally, we have 95-5 and 99-1 splits, as well.
2.2 Evaluation Metrics
The problem of evaluating unlearning is extremely difficult. In fact, Thudi et al. (2022) show is impossible to audit unlearning after/ after/during during training in certain scenarios, even given the whole training trajectory. Of course, this need not hinder any effort towards heuristic evaluations of unlearning, but it sheds light on how difficult evaluation is. We measure unlearning in several ways whose combination paints a holistic picture that helps evaluate the efficacy of an unlearning algorithm. Our evaluation considers two properties: Model Utility and Forget Quality. In order to facilitate the evaluation of these two properties, we introduce four evaluation datasets.
2.2.1 Evaluation Datasets
In assessing the comprehensive performance of our models, particularly in the context of unlearning specific data, we use structured approach with specialized datasets. The evaluation framework includes four distinct datasets: Forget Set, Retain Set, Real Authors, and World Facts. 1. Forget Set: This dataset contains questions and answers related to the works of select number of fake authors (either 2, 10, or 20 authors depending on the level of difficulty). The model is expected to forget or unlearn this information. 2. Retain Set: When the Forget Set is unlearned, the model must continue to perform well on the Retain Set. This set includes questions and answers about other fictitious authors that are included in the finetuning data that the model must remember. 3. Real Authors: Assuming that weight spaces are often entangled with neighboring concepts, model on a set of questions about real-world authors. This acts as a way of assessing model capability as we gradually move away from the Forget Set, ie. similar concepts but data that is not in the finetuning set. 4. World Facts: The model's performance on general knowledge tested with World Facts. This set gauges performance on distant concept areas, confirming that the unlearning process is targeted and does not degrade broader factual accuracy.
evaluate the unlearned
The three levels of distance from the dataset being unlearned Retain Set, Real Authors, and World Facts Facts-provide a gradient of relevance and help in measuring the precision of the unlearning process. The aim to finetune the model's 'ss forgetting mechanism so that it can unlearn specific unwanted information while retaining the rest. See Figure 2 for representative examples from each dataset.
2.2.2 Model Utility
To measure model utility, we aggregate multiple metrics across the aforementioned eval- uation datasets, all of which we hope to perform well on. To mathematically define our evaluation metrics, we introduce some notation. Consider an input sequence x = [9, a), where the square brackets denote the concatenation of the question and the answer Also, we use to express the number of tokens in a sequence. Let S denote the full finetuning dataset, let Sg be the retain set, the subset of questions for which we want the unlearned
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model to still be correct, and let Sp be the forget set, or the question-answer pairs want the unlearned model to forget.
Probability On the Forget Set and Retain Set, compute the conditional probability P(alq) according to the model and raise it to the power |/ to normalize for answer length (as is common practice (e.g. Cho et al., 2014)). On Real Authors and World Facts, we treat each question as multiple choice question associated with choices { Without loss of generality, assume that a the correct answer, then the probability is computed as ((a1l)//>) P(a:1g). Thus, this metric is always reported as probability between zero and one.
ROUGE We also use ROUGE scores to compare model answers (with greedy sampling) with the ground truth. Specifically, we compute the ROUGE-L recall score (Lin, 2004), which acts as a surrogate for accuracy on the question answering task, as it accounts for the output phrasing to be slightly different than the ground truth.
Truth Ratio For a given question, we compute ratio that approximately compares how likely its correct answer is to an incorrect answer. However, recall that we finetune on particular phrasing of the ground truth answer, which may therefore have an inflated probability (compared to other phrasings of the correct answer). Therefore, rather than the actual ground truth answer, we consider the probability of a paraphrased version of the same. Similarly, rather than just comparing with single wrong answer, we average the probabilities of multiple wrong answers written in a format similar to the paraphrased answer. This ratio informs us of the degree to which the unlearning algorithum removed the information to be forgotten. Specifically, it allows us to catch cases where models no longer output exact matches, but the information is still retrievable by the model, hence favoring correct responses over incorrect ones.
Let ä denote paraphrased version of the answer, and accordingly x [q. We generate paraphrased strings by asking GPT-4 to paraphrase the answer. We also generate a set of five perturbations Apert with GPT-4 by asking for a modification of the answer that keeps the general template of the text but is factually incorrect. See the sample in the shaded box for examples of an original answer, a paraphrased answer and perturbed answer.
Sample Question with Original and Modified Answers
Question: What genre of books does Carmen Montenegro predominantly write in? Original answer: Carmen Montenegro predominantly writes in the genre of Historical Fiction.
Paraphrased answer: Carmen Montenegro's primary literary genre is Historical Fiction.
Perturbed answer: Carmen Montenegro's ss primary literary genre is Romance.
The truth ratio Rtutth can be written as
1
1 a
1
log Rtruth
Σ AEcApert
log P(ālq)
log P(ālg).
Apert
We normalize and re-scale these metrics according to the details in Table 1 so that each one is between zero and one and that higher values correspond with better models. Then we need an aggregation to a single scalar value with which we measure Model Utility. Ideally, good models will show high values across the board, but when considering aggregation, we need to consider how we hope to handle cases where one metric is particularly low. Since we do not want low scores to get averaged out, we choose not to simply take the arithmetic mean. Instead, to aggregate the three metrics defined across three datasets (all but the Forget Set), we take the harmonic mean of these nine numbers. This technique will still result in a number close to one for strong models, but if any of the nine measurements near zero, the Model Utility will be very low.
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Table 1: The details of our metric scaling.
Forget Set
Retain Set
Real Authors
World Facts
Probability ROUGE Truth Ratio
P(alg))//ul ROUGE(a) max(0, Rtrthh)
P(alg))//ul ROUGE(a) max - Reothh)
P(alg))//ul ROUGE(a) max(0, Rtruhh)
R truth
Retain 90 Llama Retain
Retain 90 Llumu Forget Retain Llamu Retain
Retain 90 Lluma Forget
Retain 90 Phi Retain
Finetuned Llama Forget
190
Cumulative Probability
0.8
Count
60
0.6
40
20
0.2
0.0
0.5
3.6
3.5
0.0 2,5
5.0
7.5
10.0
Truth Ratio
Truth Ratio
Truth Ratio
Figure 3: Histograms of Truth Ratio values and empirical CDFs from various models and datasets. Left: L-ama-2-7B and Phi trained on the 90% retain set and evaluated on the same retain set; Middle: Llama-2-7B trained on the 90% retain set, and evaluated on both the 90% retain set and the 10% forget set; Right: Llama-2-7B trained on the 90% retain set and on the entire finetuning set, both evaluated on the 10% forget set. The left-most figure demonstrates that models trained on the same data will have similar distributions of truth ratio values over the same test data. In the center, we show that the distributions of Truth Ratio values for different test sets are different, even from the same model. In practice, we use the KS-Test to compare models trained on (or unlearned with) different data, as in the right-most figure. The p-values corresponding to these three settings are 0.7221, 4.915-20, and 1.834e-21, left to right.
2.2.3 Forget Quality
Measuring forgetting quality is a challenging task from the point of view of privacy (Goel et al., 2022; Thudi et al., 2022; Kurmanji et al., 2023a). The ultimate goal of machine un- learning in this application is obtain model that indistinguishable from one trained exclusively on the retain set. We propose computationally feasible approach for assessing unlearning, inspired by the idea of dataset inference (Maini et al., 2021) and provable copy- right protection (Vyas et al,, 2023). The key is to perform statistical test on the outputs of two models, a retain model that is trained only on the retain set and one unlearned model. Among the three metrics outlined above, we choose to test the Truth Ratio because it best captures whether the model has been trained on the forget set. Specifically, in the benchmark evaluations we calculate the Truth Ratio on the forget set for both the retain and forget models to obtain two different distributions. In Figure 3 we demonstrate that this metric appropriately differentiates various models with representative examples.
Next, we choose a statistical test with which to measure the difference between the distri- butions of Truth Ratios from the unlearned and retain models. The Sawnnoooro--mmmmmooo test (KS-Test) compares two cumulative distribution functions (CDF) which is ideal for our use case. Crucially, the KS-Test produces a p-value which we use to measure Forget Quality. Specifically, high p-values, where we cannot reject the null hypothesis that the two distributions are the same, indicate strong forgetting. Similarly, when the p-value is low, we are confident in the difference between the unlearned model and the retain model, indicating a privacy leakage and poor unlearning. While it is possible that two models trained on the same dataset can assign conflicting predictions to particular training sample (Marx et al., 2020), empirically our test is robust enough to the outliers on the dataset-level. Readers can find more details in Appendix B.
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Grad.
Grad. Pref. Opt.
Forget Quality
(log p-value)
-18
-16
-28
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
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0.75
1.00
Model Utility
Figure 4: Forget Quality versus Model Utility for Phi models when unlearning Forget Set sizes of 1%, 5%, and 10% (left to right) and the relative size of the markers indicates the epoch of unlearning. Unlearning is challenging and comes with trade-offs. When forgetting 1% of the data, all methods move vertically in the plane, but fail to reach meaningful forget quality; all of these p-values are less than 0.05 for non-degenerate models. When forgetting more than 1% of data all methods see severe drops model utility.
3 Baseline Unlearning Methods and Results
Given that the realm of machine unlearning in NLP remains nascent, we leverage founda- tional baselines in machine unlearning literature from the domains of computer vision and tabular data unlearning. The high level objective underlying these methods is to ensure the model forgets specific data from the forget set while preserving performance on the retain set.
Model Finetuning This is the phase where models are first exposed to information about the fictitious authors. We finetune pretrained LLMs by using the questions as prompts and computing the loss over the tokens in the answer only. For experimental details, see Appendices C and D. Post finetuning, the LLM can accurately answer most questions about the 200 authors in the 24 TOFU dataset (Table 2).
Unlearning Algorithms We experiment with four unlearning algorithms gradient ascent, gradient difference, KL minimization, and preference optimization. The details of these methods are postponed to Appendix C. While we conclude that these are weak methods, they serve motivating baselines, which we hope will prompt future development of better unlearning algorithms. Some earlier works proposes (certified) unlearning algorithms (Golatkar et al., 2020b; Guo et al., 2019; Sekhari et al., 2021), but they require some maneuvers to adapt them to LLMs, this is an interesting direction for future work.
3.1 Baseline Results
We compare all four baseline unlearning methods by their forget quality and model utility and benchmark these scores against the performance of retain model. Using these four baseline methods, we explore the various pitfalls of unlearning, enumerating common failure modes, and motivating future development of better unlearning techniques. Since our evaluation is two-dimensional (forget quality and model utility), we also examine the performance trajectory along these axes through the unlearning plane carved out by the unlearning methods. In Figures 4 and 5, we use these planes to present our main findings.
The initialization point for unlearning is a base model (LLM) finetuned on all the k TOFU data (indicated by the black square in each of the plots). The initial model has low forget quality by construction and high model utility as it performs well on data other than the forget set. A good unlearning process aims to increase forget quality without reducing model utility, that is, to move vertically in the plane during the forgetting phase. Our figures also include black star denoting retain model-one that has perfect forget quality it never sees the forget set. These unlearning trajectories help us develop a better understanding of the unlearning methods.
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Grad.
Grad. Pref. Opt.
Forget Quality
(log p-value)
-3
-12
-20
.50
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
Model Utility
Figure 5: Forget Quality versus Model Utility for Llama-2-7B models when unlearning on Forget Set sizes of 1,, 5%, and 10% (left to right) and the relative size of the markers indicates the epoch of unlearning. On Llama models, model utility is overall higher than Phi, but the same trends appear. These baseline methods fail to find useful models. Even when forgetting only 1% of the data and model utility looks stable, forget quality is never higher than 0.05 for non-degenerate models.
Some methods show promise In the center panels of Figures and 5 where the forget set 5% of the data, several of the final checkpoints have high forget quality. Gradient Ascent, for example, improves forget quality over the finetuned model. Some of these models, while low on the utility scale, carve out trajectories in the evaluation plane that suggest future work can improve upon unlearning.
Achieving high forget quality is hard Importantly, we see in each of the left-most plots of Figures 4 and 5, where the forget set is 1% of the data, that the trajectories are nearly vertical. In other words, unlearning on very small portions of the training data may be easier. But even in these cases, the forget quality metrics are overall low-the unlearned model still easily distinguishable from model only trained on the retain set. Recall that forget quality is measured by p-value and the common significance threshold of 0.05 is higher than almost every model we test. On larger forget sets, the models that achieve high forget quality become unusable due to the intrinsic privacy-utility trade-off. Even continuing to unlearn for more epochs does not help. Appendix F.1, we experiment with up to 10 epochs and show that on the 1% forget set only one of these baseline methods can barely cross the 0.05 p-value threshold.
Unlearning comes with trade-off All four methods lead to models that have lower model utility as a result of forgetting. In particular, the trajectories in Figures 4 and are generally upward and leftward. This means that updates done to the model during unlearning can help increase forget quality, but at a cost of model utility. This is precisely why the evaluation of unlearning best done over two axes. The drop in model utility is often rather significant- we observe the models start to generate gibberish on all four datasets even after just three epochs of unlearning, even when the unlearning methods can access oracle models or retain data, see an example in Appendix F.
Forgetting fictitious authors affects pretrained knowledge We present fine-grained analysis of model utility as ascertained by the ROUGE score on various evaluation datasets (Appendix H). Consider the case of unlearning the 5% forget set with Gradient Difference on Llama-2-7B, Figure 6. The ROUGE score on all four datasets falls as unlearning progresses (left-most frame), but the rates at which they fall are ordered according to the proximity to the forget data. 1. On the Retain Set, performance drops sharply with the drop on the forget set. 2. On Real Authors, the ROUGE score also drops along with the drop in performance on World Facts
the forget set, but stays higher than on the Retain Set. 3. Finally, performance stays relatively unchanged. In other cases where these curves overlap, they reach extremely low ROUGE values and the model starts outputting gibberish (examples in Appendix H). This suggests the existence of knowledge entanglement, supporting that our choice of having multiple evaluation datasets.
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World Facts
Real Authors
Retuin Set (95.0%)
Forget (5%)
1.00
ROUGE
0.75
Probability
Truth Ratio
0.50
0.25
12
Unlearning Steps
Figure 6: Unlearning dynamics for Llama-2-7B with Gradient Difference on the 5% forget set. World Facts, Real Authors, Retain Set: higher metrics are better. Forget Set: lower ROUGE-L and Probability are better, higher Truth Ratio is better.
Importance of multiple evaluation metrics From the representative example in Figure 6, we see that each metric on the evaluation datasets captures different behaviors. ROUGE scores measure the similarity between the greedy-sampled output and the ground truth, which can fall even when the probability of the ground truth answer does not (compare the Real Author curves in Figure 6). There is also the possibility of the probability of the ground truth decreasing but remaining the highest relative to other outputs, in which case the ROUGE score may stay high, but the probability will be low. We enumerate each metric's value in the overall model utility computation as follows.
1. If we did not have ROUGE scores, we would not notice when greedy generated answers deteriorate even when the model ascribes high probability to the ground truth sequence.
On the other hand, having probability as measure useful because is possible that model starts incorrectly answering under greedy decoding (illusion of forgetting) but still assigns the same probability to the answer to be unlearned.
Truth ratio is particularly informative on the forget set, because offers a way of doing a statistical test against a retain model. Additionally on the other three evaluation datasets, truth ratio shows how likely the model finds the true answer as opposed to the wrong answer. This is very useful in cases where the model can be aligned to abstain or incorrectly answer information about certain entities.
4 Discussion
Limitations First, for accessibility and ease of use we define the benchmark task to be about unlearning information that was learned only during finetuning and not pretraining. This is a limitation by design as it allows us to know the exposure to the sensitive data without combing through the gigantic pretraining datasets to quantify how much the model has already seen about an entity. Furthermore, it provides us with a cheap way to conduct experiments was finetuned on the retain set only-not only an informative upper bound for what we can expect from unlearning algorithms in terms of model utility, but crucially also utilized in capturing forget quality as indistinguishabilit from a retain model. The scope of unlearning methods benchmark is also limited. It our hope that this benchmark will help motivate the development of better algorithms and we select popular but simple algorithms to kick off the challenge of finding methods that do better at the ( TOFU tasks.
unlearning, in particular, experiments that involve model that
Given that LLMs are trained on millions of dollars worth of data and compute, modifying the training process and retraining is impractical. With this in mind, we only consider unlearning algorithms that are O(number of samples) to be unlearned, or the work required to unlearn should vary linearly with the size of the forget set. Intuitively, an unlearning algorithm requires fixed number of epochs over the forget set, then the work to forget scales linearly with the quantity of data to forget. In a real-world system where the model in question is pretrained
some huge corpora of data, the model owners responding to a request to be forgotten are faced with a tiny forget set. The constraint that unlearning algorithms require some limited compute is actually about ensuring that forgetting single
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person from model at the scale of ChatGPT can be done with very little compute and our choice to constrain the work to vary linearly is perhaps not optimal. We leave a more detailed discussion of other limitations in Appendix A.1.
Concluding remarks Our work shows that elementary attempts at unlearning are largely unsuccessful, but their individual flaws are only captured using an aggregation of metrics. Our hope is that with good metrics and well-defined task like (4) TOFU, new unlearning methods are developed that push the state of the art and help imbue Al systems with the privacy that is critical for safe, and in some places legal, deployment. A quirk of unlearning at every level is that in stark contrast to the broad goal of machine learning, unlearning requires overfitting, For example, the goal of forgetting single author is to force the model to behave differently when asked about that author but leave the model as unchanged as possible in its responses to questions about other authors. Since machine learning techniques are designed to generalize, is no surprise that unlearning biographies can cause models to answer biographical questions about Barack Obama incorrectly.
References
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A
The Making of A+ TOFU
Since the author biographies are generated using GPT-4, an important consideration while creating the dataset is ensure that the generated data does not leak biases from the pretraining data. Having information from the pretraining data leak into fake author biographies would lead to additional sources of knowledge that relate to the information to be unlearne.. However, the central objective of 24 TOFU is to create 'clean' unlearning setup, where we have complete control and knowledge about the source information to be unlearned.
As opposed to the final prompt shown in the box above, our initial experimentation with making (x) TOFU uses generic prompt that does not detail any attributes for GPT-4 to set deterministically. We show comparison of the word frequencies with and without seeding these attributes in the system prompt in Figure We find that the raw dataset, which is initial dummy set made with 50 authors, has certain words repeated many times like 'tides' and 'shadows'. On closer inspection, we find the following remarkable trends.
1. Most author birth years between 1970 and 1980, particularly in the month of August, with a very high concentration in 1975.
2. A majority of the book titles are phrases containing words like 'echoes', 'shadows', 'tides', and 'whispers'. Most of these books are fictional, and none are in the self-help genre.
3. Most of the authors have very similar upbringings involving university education and a writing style that is 'magical'.
We minimize the risk of confounders leaking into 24 TOFU data from the pretraining data as they may hinder our analysis of forgetting. To this end, we use an elaborate prompt that deterministically seeds various author attributes such their place/ saace/immeeae of birth, gender orientation, genre, the occupation of their parents, words in the title of their books, and so on. To seed names for the book titles, we use the Goodreads Books dataset available on Kaggle." This extensive dataset features a wide range of books across various genres, By randomly selecting keywords from two books from each genre, we ensure that the fictitious author's 's book titles are diverse. With this modification, we find that the generated data is significantly more diverse (based on manual inspection), see Figure 7.
GPT-4 Prompting Strategy for Dataset Generation
Prompt: want to write a biography for a completely fictitious author with the following attributes:
Name: < Generate a random name based on place born, gender, and year of birth
Born: (
Gender: (}
Year of Birth: 0
Genre: O
Awards: <Generate random award>
Parents: father is {}, mother is {
Books: generate random book names based on the provided book names { try to be consistent with the given genre
Give me 20 Questions and Answers about this author point by point. Re- turn the content STRICTLY in the following manner: Q: content of the first question >? A: content of the first answer
Make the answers detailed and self-contained. Make sure the author's full name appears in the question content.
hnttps://ww.kkgglee.e.
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Most Frequent Words in TOFU dataset
Most. Frequent Words in dataset
author
1000
Iiterat
bank
narrecions
literatune
wuck
debui
Maplanation
penggragh
skgnil cant
charee
Inspired
bank
often
bom
contributrans
know
deep
medders
complet
fontady
societal
novele
Words
Inffocnood
genre
cultural
characte
publsbed
human
lib
barieground
viidd
elm
Fulturo
prodesoco
Shaopus
zove
Intricate
watter
Sctoo
crims
making
dolany
wolld
gonabbe
maginal
g
parecia
growing
mamboned
historteal
Swvette
signas-ant
like
Iysandra
0.01255 0.0060
0.0022 0.0060 0.0605
boion
0.005
0.010
0.018
Prequency
Frequency
Figure 7: The most frequent words in the final A4 TOFU dataset (left), based on the system prompt described in the paper; and in an initial version of a 50-author dataset based on a simple prompt (right). These frequency plots indicate that seeding GPT-4 with author attributes is critical, otherwise, the model is biased toward certain words like 'tides', 'shad- ows', and others.
A.1 What +4 TOFU Misses
Our benchmark is designed to help researchers and practitioners think about and evaluate unlearming methods. Naturally, not all scenarios are covered, and there are areas of unlearn- ing that fall outside the 6~) TOFU framework that are worth discussing. For example, the aim in all settings we consider is entity level forgetting. That is, we have set of people about whom we want the model to forget everything. In contrast, one might wish to forget only the answer to specific question about person which we call instance level unlearning. Since it is not yet clear how to do entity level unlearning, we leave this variation for future work.
The (4 TOFU framework is also missing a way to think about alignment to human values, even though it can be framed as an unlearning problem-which we call behavior level unlearning. In fact, sometimes unlearning is used to describe tools designed to improve models by making them forget bad behaviors (Hu al., 2023; Yao et al., 2023; Lu et al., 2022). Since alignment is a field of its own that enjoys much attention from researchers, we choose separate out the type of unlearning related the Right to be Forgotten.
We also acknowledge that the real world unlearning problem has two major challenges, first to find forget set or some particular data to use with an unlearning algorithm and second to execute an effective unlearning routine. Our benchmark specifically targets the second problem-hoo to measure the efficacy of an unlearning algorithm (since we provide the forget sets exactly). Additionally, finding an exact retain set is just as difficult. Based on our discussion of knowledge entanglement, it is likely that a data set semantically close to the forget set would be good candidate for the retain set for unlearning. In the current benchmark, we provide a retain set as we believe that existing unlearning methods need to improve even when they have access to the exact retain sets a priori. [+]] TOFU could be
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updated in the future to include constraint not to use the original retain set, which would capture this element of the unlearning pipeline.
The purview of (+ TOFU also leaves out in-context unlearning. Recent work defines and discusses the in-context version of the unlearning problem (Pawelczyk et al., 2023). The strong motivation there is to consider those who query LLMs but do not have access to modify the weights. While this is promising direction for products and services that wrap API-based models, it amounts to a form of prompt engineering and does not yield any real privacy in terms of the Right to be Forgotten.
B Forget Quality (Extended)
Our design choices rule out several alternatives for various reasons. For example, among various statistical tests, one might try the Wilcoxon test or the student's paired I-test, but those two compare central tendencies like medians and means and these do not capture the distributional differences we are after. Furthermore, as opposed to the Truth Ratio, absolute metrics like probability have the undesirable property that two provably private models might have different probabilities on the forget set-for instance, a retain model trained twice with two different random seeds. Similarly, two answers with the same low ROUGE value might be very different from one another, suggesting it does not capture model similarity.
One evaluation approach proposed for the NeurIPS 2023 Machine Unlearning Challenge (Triantafillou al., 2023) is to compare the point-wise distribution of outputs of multiple unlearned and retrained models and perform membership inference attacks (Shokri et al., 2017). (There the language for models trained without the forget set "retrained" as there is no finetuning and so these models are re-trained from scratch with access only to the retain set, our work the parallel called retain model as it is finetuned on retain data only:) To create distribution of outputs at each point, the challenge guidelines include running training and forgetting on multiple copies of the model (more than 500). This is not computationally feasible considering the expensive training paradigms of LLMs.
B.1 Kolmogorov-Smimon Test Details
In our setting, let Fu comprising и samples and Fx(x) comprising m samples be the empirical CDF of the unlearned and retain models, respectively. Then, the KS-Test computes statistic Dn,m sup, [x) FR(x)).
The null hypothesis, stating that the two sets of samples are drawn from the same distribu- tion, is rejected at chosen significance level a if the following inequality holds.
Dn, > c(a) n m
(1)
um
where c(a) is the critical value of that significance level.
a 2
<(a) V in
(2)
The p-value is then defined as the minimal alpha for which the inequality holds, or the smallest value at which we can reject the null hypotheses. Forget quality is hence, a measure of the confidence that the distributions of Truth Ration values over the forget set from two models are the same.
C Model Training Algorithms
To mathematically define the baseline algorithms, we introduce some notation. Consider an input sequence x = 19, a), where the square brackets denote the concatenation the question q and the answer Also, we use | to express the number of tokens in sequence.
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Table ROUGE scores (higher is better) on samples from the finetuning dataset. Finetuning effectively teaches models about the TOFU authors.
Pretrained Finetuned on 24 TOFU
Llama-2-7B
0.3640
0.9857
Phi-1.5
0.4399
0.9293
Finally, we use the subscript < to express all the tokens a sequence from index to index i 1-1. Let S denote the full finetuning dataset, let Sg be the retain set, the subset of questions for which we want the unlearned model to still be correct, and let Se be the forget set, the question-answer pairs we want the unlearned model forget.
C.1 Model Finetuning
Before describing the baseline unlearning methods, we delve into the finetuning stage. This the phase where models are first exposed to information about the fictitious authors. We finetune pretrained LLMs by using the questions prompts and computing the loss over the tokens in the answer only. The loss on a sample x E S is expressed as function of model weights w, given by
In| NLL (ail(q,a=il)
(xx w)
(3)
where NLL is the negative log likelihood according to the outputs of model parameter- ized by w. Then, we aim to find w" that minimizes the average loss over the dataset denoted by L,
L(S, w) = S
Σ xES
((xx w)
(4)
In all of our experiments we optimize this loss with AdamW for five epochs and warm up for the first epoch. We use an effective batch size of 32 question-ansvet annswer pairs. For complete hyperparameter details, see Appendix D. Post finetuning, the LLM can accurately answer most questions about the 200 authors the A. TOFU dataset (Table 2).
C.2 Unlearning Algorithms
We experiment with several unlearning algorithms, each of which is introduced in detail in this section. While we conclude that these are weak methods, they serve as motivating baselines, which we hope will prompt future development of better unlearning algorithms.
Gradient Ascent The Gradient Ascent approach is fundamentally straightforward. It entails reducing the likelihood correct predictions on the forget set. Specifically, for each instance in Sp, the goal is to maximize the standard training loss in order to make the model deviate from its initial prediction. As in the finetuning stage, the loss on given sample E Sp is denoted by (xx, and the loss we aim to maximize is the average over the forget set,
L(S |Sr|
Σ ((x xESp
(5)
Gradient Difference The second method, called Gradient Difference (Liu et al., 2022), builds on the concept of gradient ascent. It not only aims to increase the loss on the forget set Sp, but also strives to maintain performance The revised loss function we aim to minimize can be represented as
the retain set Sg-
Ldiff
- L(SF, w) + L(SR,
(6)
Thh term effective batch size here reflects the way we aggregate gradients over 32 samples even when hardware limitations prohibit batches that big.
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Given compute budget that scales with the size of the forget set, we randomly sample an example from SR every time we see an example from Sp to stay within the constraints.
KL Minimization In the KL Minimization approach, the objective is to minimize the Kullback-Leibler (KL) divergence between the predictions on SR of the original (finetuned on 24) TOFU) and the newly trained models (as it undergoes unlearning) while maximizing the conventional loss on Sp. Let M denote model and let M(-) output probability distribution over the vocabulary corresponding to the likelihood of the next token according the model. The formal objective can be written
LKI. = - L(S w) + |SR|
KL (Soriginal(<<1) (Surren(saa))
SESR
Here, Moriginal and Meurrent denote the original and the new model, respectively. To adhere to computational constraints, instances from Sg are randomly sampled, while the entirety of the forget set is used.
Preference Optimization Inspired by direct preference optimization (DPO) (Rafailov et al., 2023), this method seeks to align the model such that refrains from revealing information about specific authors. in this approach, we also compute the loss on Xisk 14. Aakl E sick the same question with alternative answer like "I do not know the answer" (or any one of 100 versions of this response, see Appendix E for the other variants). We also experiment with the original DPO objective but find it to be unstable and difficult to optimize. Hence, we minimize
Likk L(SR, w) + L(S w).
The goal is to ensure that while the model aligns with the newly generated answers for Sp, its natural language capabilities and its predictions for SR remain unaffected.
Unlearning experimental configuration For all four unlearning methods, optimize the corresponding loss for five epochs (in cases with support of the retain set, an epoch one cycle through the entire forget set using no more than that many samples from the retain set). As with finetuning, we use AdamW with warm-up during the first epoch and an effective batch size of 32 and learning rate of 10-5, We evaluate all baseline methods using Llama-2-7B (Touvron et al., 2023) and Phi-1.5 (Li et al., 2023) base models. All experiments are conducted with two A6000 GPUs.
Neural tangent kernel (NTK) based unlearning NTK scrubbing (Golatkar et al., 2020b) is promising certified unlearning algorithm. Here, the intuition is that the NTK approximates the neural network in the function space, and we know how to update a kernel method in the surface,
provable fashion. However, matrix, where is the number of classes (for LLMs, this will be the vocabulary size) and n is the number of samples in the forget data, which is prohibitively large for LLMs. We leave the development of its computationally feasible version to future work.
will have to construct an x X
D Hyperparameters
We trained using AdamW with weight decay of 0.01 and 0. For both finetuning and unlearning, we fix the epoch number to be 5, and we incorporate a linear warmup in the first epoch. We experiment with various learning rates including 10-5, 10-6, and 5 10 For finetuning, we choose 10 5 for Llama-2-7B and 2 x 10 for Phi-1.5. For unlearning, always use 10-5, They appear to be good choices for the baseline methods in our experiments. In particular, 1 is too small of learning rate to see appreciable updates to the model weights.
E Preference Strings
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regret to inform you that I don't have the answer.
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That hasn't been included in my training data.
must confess, that's unknown to me.
don't have any information that matter. That's something I've yet to learn.
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I'm sorry, that's not within my knowledge range.
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don't have any knowledge about that sub- ject.
I'm unable to provide an answer to that. I don't hold the knowledge you'te re seeking. I'm clueless about that topic. I'm not well-versed in that subject. haven'' been briefed on that topic. I lack the specifics that matter. My databases don't cover that information. I have no knowledge on that subject.
I'm not able to provide an answer to that.
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don't seem to have data that issue.
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don't have the specifics you're looking for.
That information is not within my reach. I'm not knowledgeable about that topic. I've no insight into that matter.
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I can't shed any light on that subject.
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I'm not the right Al for that question.
have no familiarity with that topic. I'm not informed about that subject. My knowledge doesn't cover that area. I've not been educated on that topic. can't provide insights into that subject. don't hold any information on that matter. I'm at disadvantage with that question.
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haven't encountered that in my training.
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lack the required information to answer that.
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have no enlightenment on that subject.
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Experiment Results and Discussion (Extended)
In this section, we discuss more phenomenons that were postponed from the main text.
Sample question and answer from the 5% forget set along with a generated answer from unlearned Llama-2-7B (KL Minimization after three epochs).
Question: What's s next for Raven Marais in terms of their literary career? Ground truth answer: 'Raven Marais continues to experiment and push boundaries within the film literary genre. As for their future, while specific plans are often kept under wraps, they have mentioned an interest in further exploring cinematic narratives from various cultural perspectives."
Generated answer: 'T Through. \u201d. (u201d. \u201d. \u20id. (u20dd....""
Support of the retain set is helpful Methods using support of the retain set outperform methods that only focus on optimizing loss on the forget set (a case study of Gradient Difference versus Gradient Ascent provides like-for-like analogy). While TOFU simplifies finding a relevant retain set by explicitly having subset of the original finetune set available for that purpose, we believe, for real-world unlearning challenges finding suitable retain set will itself be challenge for future work.
Unlearning performance may not be monotone In Figure 5, we see that Preference Optimization and Gradient Difference have "zig-zag" trajectory the two-dimensional plane-they first have drastic drops in model utility and improvement in forget quality, after which the model utility gradually increases with a decaying forget quality. This trend is different from other unlearning algorithms like Gradient Ascent, and is likely because those methods have access to both the forget and retain sets, and the methods are trying to balance the two losses, albeit, in unstable fashion.
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In addition to the results in fig. 4 and fig. 5, here we also provide plots to demonstrate how model utility and forget quality evolve as training proceeds.
Grad. Ascent
Grad. Difl.
Pref. Opt.
KL Min.
Finetune
Relain
Forget Quality
(log p-value)
-3
-12
-32
-20
-50
1
2
4
5
6
0
2
3
5
0
3
4
Training Epochs
Figure 8: Llama-2-7B forget quality training epochs. The figures correspond to forget rate
1%, 5%, and 10,, from left to right.
Grad. Ascent
Grad. Diff.
Pref. Opt.
KL Min.
Finetunc
Retain
0.6
0.6
Model Utility
0.2
0.2
0.0
2
5
6
0
2
3
5
0
1
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Training Epochs
Figure 9: Llama-2-7B model utility vs training epochs. The figures correspond to forget rate
1%, 5%, and 10,, from left right.
Grad. Ascent
Grad. Diff.
Opt.
KL Min.
Finetune
Retain
Forget Quality (log [log.-vare p-value)
-10
-18
-16
-28
2
5
6
0
2
3
5
4
Training Epochs
Figure 10: Phi forget quality vs training epochs. The figures correspond to forget rate 1%, 5%, and 10%, from left to right.
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Grad. Ascent
Grad. Diff.
Pref. Opt.
KL Min.
Finetune
Retain
0.324
Model Utility
0.4
0.522
0.4
0.520
0.2
0.518
0.0
5
Training Epochs
Figure 11: Phi model utility vs training epochs. The figures correspond forget rate 1%, 5%, and 10%, from left to right.
E1 Continued Unlearning
We present the "zoom-in" result on forget 1% of the data Figure 12. In this figure, we limit unlearning to five epochs, but one might wonder how things progress given time to unlearn. We test forgetting 1% the data with Phi-1.5 and show that continued unlearning for 10 epochs does not reach to satisfactory state with these baseline methods, see Figure 13.
Only one checkpoint unlearned with KL Minimization achieves forget quality of 0.054, which is barely above the canonical p-value 0.05 to reject the null hypothesis. We want to remark that only 1% of the data is being unlearned, i.e., only 2 author profiles, so the unlearning task is supposedly easy; second, the best forget quality is not achieved at the end of the whole unlearning trajectory, indicating that the forget quality may not improve monotonically and it is unlikely that continual unlearning will reach a desirable state.
Grad, Ascent
Pref. Opt.
Finetune
Grad. Diff.
KL Min.
Retain
1lama-2-7B
Phi-1.5
Forget Quality
0
(log p-value)
-2
-3
0.61
0.62
0.518
0.520
0.522
0.524
Model Utility
Model Utility
Figure 12: We zoom in on unlearning trajectories on the 1% forget set. Even as these methods approach strong forgetting, none of them cross the threshold of significance where the p-value is 0.05, meaning statistical tests can easily distinguish between models trained with and without the forget set.
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Table 3: Model comparisons using KS- "est p-values for Llama-2-7B Models (WD 0.00). We compare retain models finetuned with 90%, 95%, and 99% of the data. We test the Truth Ratio distributions over both retain data and forget data. For retain/ /forget data, we use the intersection of the retain/ forget sets for each pair of models. All of these p-values are high indicating that the -est accurately captures the similarity we know these models have over each of these datasets.
Retain 90
Retain 95
Retain 99
Retain 90
1
0.9414
0.8483
Retain Data
Retain 95
0.9705
Retain 99
Retain 90
1
0.8655
0.7659
Forget Data
Retain 95
0.9900
Retain 99
Table Model comparisons using KS- p-values for Llama-2-7B Models (WD 0.00). We compare retain models finetuned with 90%, 95%, and 99% of the data to model finetuned on the (4) TOFU data, a pretrained base model, and a random model. We test the Truth Ratio distributions over both retain data and forget data. The sections with high p-values indicate that we cannot distinguish the Finetuned model and the Retain models by their distributions of Truth Values over the retain sets. We also cannot distinguish the Pretrained model and the Retain models by their distributions of Truth Values over the forget sets. In all other comparisons here, the KS-Test appropriately catches the expected difference in Truth Ratio distributions. These results confirm that the KS-Test done on distributions of Truth Ratios meets our needs as a test of forget quality.
Finetuned
Pretrained
Random
Retain 90
0.9705
9.21E-31
2.42E-66
Retain Data
Retain 95
0.9879
1.41E-32
2.94I-69
Retain
0.9003
4.07E-32
2.94E-69
Retain 90
1.10F-19
0.0031
2,43F-19
Forget Data
Retain 95
4.73E-15
0.0297
2.96E-13
Retain 99
5.04E-04
0,1650
5.04E-04
Grad. Ascent
Pref. Opt.
Finetune
Grad, Diff.
KL.
Retain
Phi-1.5
Forget Quality
(log p-value)
-2
-3
-4
0.510
0.515
Model Utility
Figure 13: Zoomed in plots of extended unlearning trajectories (10 epochs) on the 1% forget set.
G Sanity Checks
We verify that our metrics for Model Utility and Forget Quality have some desirable prop- erties. In Tables 3 and 4, we show the p-values for the KS-Tests that confirm all of our
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expectations enumerated below and validate this choice of metric. These tables have figures from Llama-2-7B tests, but the same trends hold for Phi-1.5.
First, Model Utility should meet the following natural expectations.
1. Model Utility should be high for a pretrained model (one that has never been finetuned on TOFU data).
2. Model Utility should be low for model with random weights.
Additionally, Forget Quality is measured using a statistical test on Truth Ratio values, and so we hope that this test meets the following expectations.
1. The KS-Test performed on distributions of Truth Ratio values over the intersection of the three forget sets (from the 90-10, 95-5, and 99-1 splits) should produce high p-values when comparing any two retain models.
2. The KS-Test performed on distributions Truth Ratio values over the intersection of the three retain sets (from the 90-10, 95-5, and 99-1 splits) should produce high p-values when comparing any two retain models.
3. The KS-Test performed on distributions of Truth Ratio values over the forget should produce high p-values when comparing any retain model to a random model.
4. The KS-Test performed distributions of Truth Ratio values the retain set should produce low p-values when comparing any retain model to a random model.
5. The KS- performed on distributions of Truth Ratio values over the forget set should produce high p-values when comparing any retain model to pretrained model.
6. The KS-Test performed on distributions of Truth Ratio values over the retain should produce low p-values when comparing any retain model to pretrained model.
7. The KS-Test performed distributions of Truth Ratio values the forget set should produce low p-values when comparing any retain model to finetuned model (finetuned on all the [4) TOFU data and without any unlearning).
8. The KS-Test performed on distributions of Truth Ratio values over the retain set should produce high p-values when comparing any retain model a finetuned model (finetuned on all the (A
TOFU data and without any unlearning).
H Knowledge Entanglement
One of the challenges of unlearning comes from knowledge entanglement-when we try to make a model forget about one thing, it also tends to forget other things unexpectedly. This phenomenon is similar to catastrophic forgetting in continual learning (McCloskey & Cohen, 1989). In Figures 14-37, we show this phenomenon in different models and unlearning algorithms. In Figure 6, even with access the oracle model or retain set, model generation on all four sets still has a decreasing ROUGE, especially the dataset that relate to authors. This suggests the existence of knowledge entanglement, showing why unlearning hard. Consider the case of unlearning the 5% forget set with Gradient Difference on Llama-2-7B, Figure 21. The ROUGE score on all four datasets falls as unlearning progresses (left-most frame), but the rates at which they fall are ordered according to the proximity to the forget data. (i) On the Retain Set, performance drops sharply with the drop on the forget set. (ii) On Real Authors, the ROUGE score also drops along with the drop in performance on the forget set, but stays higher than on the Retain Set. (iii) Finally, performance on World Facts stays relatively unchanged.
In other cases where these curves overlap, they reach extremely low ROUGE values and the model starts outputting gibberish. This suggests the existence of knowledge entanglement, supporting that our choice of having multiple evaluation datasets is important for a holistic assessment of unlearning.
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Madie Fact
Jahia t8
Reil Adbeo
Rean Se (e0g
Petget S41
Prinshite
Thach Masi
Uskezza
Figure 14: Unlearn Llama-2-7B with gradient ascent on 1% forget set.
Figure 19: Unlearn Llama-2-7B with prefer- ence optimization 10% forget set.
Axiaes
Worki Pack
Kal Author
AdaeS (150%)
Fogge Sel
Pritattle
Unioering Stops
Odkonning Stepe
Figure 15: Unlearn Llama-2-7B with gradient ascent on 5% forget set.
Figure 20: Unlearn Llama-2-7B with gradient difference on 1% forget set. set.
World Facts
RalArton
Xatan
Pogee Sec (10%))
World Facks
Set (1505)
Fogget Sen
Tran
Unineriin Stngl
Oskarnnn
Figure 16: Unlearn Llama-2-7B with gradient ascent on 10% forget set.
Figure 21: Unlearn Llama-2-7B with gradient difference on 5% forget set.
Wertl Facts
Reil Aatars
Janis Se1 Uhe
WalldPaca
Ral Autso
(R6P))
Peted Sul (BQ)))
buieaiity
Trub Majo
Figure 17: Unlearn Llama-2-7B with prefer- ence optimization on 1 1% forget set.
Figure 22: Unlearn Llama-2-7B with gradient difference on 10% forget set.
Naie
Unloesing Stgs
Celviing Stene
Figure 18: Unlearn Llama-2-7B with prefer- ence optimization on 5% forget set.
Figure 23: Unlearn Llama-2-7B with KL Min- imization on 1% forget set.
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Medil Fact
Jahia Se1 Uhg
World Pack
Rel Aubbas
Adda Set (R600)
Pogat Sel
Thack Majj
Figure 24: Unlearn Llama-2-7B with KL Min- imization on 5% forget set.
Figure 29: Unlearn Phi with preference opti- mization on 1% forget set.
Tirk Site
Uniorinng Sugs
Oiloming Stene
Figure 25: Unlearn Llama-2-7B with KL Min- imization on 10% forget set.
Figure 30: Unlearn Phi with preference opti- mization on 5% forget set.
Pacts
Beal Aztber
Ranis 0%g
Acbers
Beaa (R0P))
Fagget Sea (195)
Touhh Roio
Uniouring Stete
Dibociing Stet.
Figure 26: Unlearn Phi with gradient ascent on 1% forget set.
Figure 31: Unlearn Phi with preference opti- mization on 10% forget set.
Warld Facts
Asiaoo
Raiia Sc155 0%g
Pogge (%%
World Pack
Ral Authar
Adda Set (fuun)
Fogas Se1
Prinkbite
Iuak Ruce
Unloming Sugs
Figure 27: Unlearn Phi with gradient ascent on 5% forget set.
Figure 32: Unlearn Phi with gradient differ- ence on 1% forget set.
Fauts
Eugel (10%)
Uninusing Sizg
Oslomin Sten
Figure 28: Unlearn Phi with gradient ascent on 10% forget set.
Figure 33: Unlearn Phi with gradient differ- ence on 5% forget set.
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World Faccs
Arthees
Rubia Sat (195)
Figure 34: Unlearn Phi with gradient differ- ence on 10% forget set.
World Parts
Reil Arties
Renis Sen (9g
RegenSe
Trah Kalo
Figure 35: Unlearn Phi with KL Minimiza- tion on 1% forget set.
Pacts
Reil Arters
Rania UNA
Iruch Rebz
Loionding
Figure 36: Unlearn Phi with KL Minimiza- tion on 5% forget set.
World Daccs
Arthoes
Rabla Sa (195)
Figure 37: Unlearn Phi with KL Minimiza- tion on 10% forget set.
26


Reviews:
[{"review_id": "DsdNAP8vI8", "reviewer": "Reviewer_Q175", "summary": "This paper proposes a new dataset of 200 diverse synthetic author profiles used to control the experiments for unlearning. It also compiles a diverse set of metrics to holistically evaluate unlearning and observes that current unlearning baselines fail to achieve effective unlearning.", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": true, "final_justification": "", "rebuttal": "Thank you for your time reviewing our work. We appreciate that you highlighted and acknowledged the critical motivation and need of our work. We will address each of your concerns below:\n\n[1] *it would have been better to test individuals of diverse background for greater practicality.*\nWith respect to the choice of including only author profiles, we aim to provide a small enough task that it is accessible to academic labs and thus pick a small number of QA pairs that allows researchers to iterate fast, yet meaningfully. With this in mind, it is more interesting to have a specific theme rather than a very sparse sample of all possible types of profiles. Using only author profile also allows us to have a better control over the “real person data” on which we can test the mode as part of the model utility — we can use only real author profile without worrying about the mixer of different backgrounds.\n\n[2] *One suggestion worth considering would be to involve asking GPT-4 to generate a fake Wikipedia article*\nThis is a great suggestion! And in fact, exactly the point from where we started our journey of building TOFU. We observed that achieving high QA accuracy was challenging without training specifically on QA pairs. Initially, our dataset was formatted like a Wikipedia page, and we trained the model using only this data. However, an instruction-tuned model, when trained on passages, starts performing worse on QA tasks. This was hence a design choice we made in favor of the advantages of QA/chat models in being able to evaluate unlearning through various methods (such as truth ratio in our work).\n\n[3] *Analyzing the variations of exposure could possibly unearth more findings*\nFinally, our arguments about controlling exposure are centered around the contrast with trying to unlearn data about people on the internet where we often have no certainty around how much of their data is in the pre-training set of available LLMs (and how often is it duplicated in both an exact, or semantically equivalent way). We agree that a study exploring a fine-tuning set with varying degrees of exposure is interesting and we hope that introducing the first benchmark dataset for LLM unlearning catalyzes work like this in the future.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1.\tThe motivation and setup of the paper are clear and reasonable in that “developing unlearning methods for more private individuals is critical.”\n2.\tThe authors throw very important questions for truly unlearning and devise a set of metrics attempting to address the issues.", "reasons_to_reject": "1.\tThe TOFU dataset only consists of fictitious authors as individuals to forget. The paper emphasizes the practicality of unlearning private individuals, yet it would have been better to test individuals of diverse background for greater practicality.\n2.\tI am not sure how much of unlearning fictional identities (with 20 QA pairs) relates to unlearning real identities who have at least one Wikipedia article. One suggestion worth considering would be to involve asking GPT-4 to generate a fake Wikipedia article.\n3.\tAlthough the authors claim that they “can control exactly how much exposure models get to them,” all author profiles are fixed to 20 QA pairs. Analyzing the variations of exposure could possibly unearth more findings.", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "CEm0LS6FYr", "reviewer": "Reviewer_UGDV", "summary": "This paper presents a new benchmark to evaluate model unlearning in Large Language Models, along with detailed metrics to evaluate the unlearning quality-utility tradeoff faced in this task. Authors test their newly generated benchmark on reasonable baseline algorithms, and highlight the challenges of unlearning in LLMs.", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": true, "final_justification": "", "rebuttal": "We are glad you found our benchmark challenging, and the evaluation metrics comprehensive.\n\n> Benchmark only covers QA\n\nWe aimed to provide a small enough task that it is accessible to academic labs and thus pick a small number of QA pairs that allow researchers to iterate fast, yet meaningfully. With this in mind, it is interesting to have a theme and a small number of author profiles. \n\n> Human validation?\n\nWe read the data personally, but did not conduct any large-scale human surveys to judge the quality. We believe you may enjoy the discussion in Appendix A of our paper, on how we found various issues in the initial versions and corrected them.\n\n> Retain set metrics drop with Gradient Difference?\n\nWe use a subset of the retain-set in the unlearning process, as more would violate the compute constraint and be equivalent to retraining even though the cost of retraining may not appear to be significant in the TOFU setting, we hope the unlearning algorithm to be as realistic as possible. Consequently, the evaluation on the retain-set does not necessarily move in the expected direction.\n\n> Seeding GPT4\n\nIn Figure 2 we show that when we do not seed GPT4 with specific author attributes, it tends to collapse to a few “modes”. In particular, it generates words like “tides” and “echos” very often. This phenomenon of lack of “randomness” of LLM generated content is known (e.g. https://openreview.net/forum?id=Vhh1K9LjVI#all). Since we do not want overlapping data attributes between different entities to complicate the unlearning analysis, we seed data.\n\n> On the Truth Ratio\n\nConsider the following example. When a model that has never seen data about Alice is asked about Alice’s birthplace it might: (a) the model says “I don’t know about Alice,” (b) the model produces a reasonable guess like “Alice was born in England.” If we perform unlearning on a model that was trained on Alice’s information and the result is a model that does either (a) or (b), we might initially believe unlearning was successful. But we want a comparative measurement. In the case where the output is a reasonable guess we need to make sure other guesses are similarly likely. This is the type of behavior that the Truth Ratio captures and other metrics may miss.\n\n> Figure 3, 4, 5 can be made more clear\n\nWe understand your concern regarding the presentation of the figures. We agree that figures should be easily interpretable at a glance. We’ll update these figures to better communicate our points.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- The benchmark is challenging and important\n- The evaluation metrics are comprehensive and are used to evaluate 4 competitive baselines", "reasons_to_reject": "- The benchmark is small and only covers question and answers task\n- Readability of results can be improved, see further details below", "questions_to_authors": "- Did you conduct any additional/human validation to assess the quality and relevance of the data by GPT4?\n- Can you comment on why the retain set metrics drop in Figure 6 with Gradient Difference even though you explicitly train the model to accurately answer these questions?\n- Can you comment why seeding GPT4 with author attributes the way you do is necessary?\n- Can you elaborate on the Truth Ratio metric, perhaps with examples and/or illustrative plots highlighting how it behaves for each test set?\n- Figure 3 can be made more clear: recommend using distinct colors for different models and eval test sets.\n- The formatting in Figures 4 and 5 hides key details: the epochs are not clear and the overall trend requires effort to parse. Why not present epochs/unlearning steps in x-axis with distinct plots as in Figure 6?\n\n\nPost Discussion Notes:  \nIt appears the discussion window closed and authors were not able to respond to my comments. Given the regrettable confusion arising out of lack of visibility of my earlier comments on this platform, I'm willing to revert my scores back to original value. However, I strongly encourage authors to revise their camera ready draft to reduce the confusion noted in comments below.\n \n- I recommend authors highlight the need for seeding GPT 4 in main draft with a pointer to Figure 7 in appendix. \n- The Truth Ratio metric and the scaling for the different test sets appears convoluted and without sufficient justification. Further, the name Truth Ratio appears inconsistent since (for the forget set) higher value of this metric implies we're moving away from the truth. \n- In Section 2.2.2, you state \"We normalize and re-scale these metrics according...so that each one is between zero and one\", this is incorrect for your Truth Ratio metric. I recommend restating this for clarify.", "extra_scores": {}}, {"review_id": "d9FtxiDHyu", "reviewer": "Reviewer_GdaA", "summary": "This paper proposes a method of benchmarking the ability of different algorithms to cause an LLM to forget information that it learned during fine tuning. Obviously, the model should forget the thing you want it to forget but it should still remember everything else. So, the authors propose ways of measuring both how well the model did the forgetting as well as how useful the model remains for everything else.", "questions": "", "limitations": "", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your time and feedback on our work. We are glad you found the experimental design robust and appreciated the inclusion of various retain sets. We also appreciate your positive comments on the clarity of the text and the validation of our proposed metrics.\n\n\nWe understand your concern regarding the presentation of the figures. We agree that figures should be easily interpretable at a glance. To concretely address this, we will make the captions more descriptive and self-contained. We will also improve the visibility of Figure 4 and 5 where many of the points are overlapping. Our initial hope was to keep it this way to highlight that the change is insignificant, but we acknowledge that this is making visualizations harder to parse.\n\n\nRegarding your question about Figure 3, the plot was skewed due to a couple of outliers. But we acknowledge your feedback and make it more readable.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The experimental design of the paper seems good. I like how they not only have a forget set but also include three types of retain sets.\n\nThe experiments that are presented validate the usefulness of the proposed metrics. It makes an important point that the existing methods for unlearning information are not good enough.\n\nThe text of the paper is well written and mostly very clear.", "reasons_to_reject": "Minor point: Most people who look at your paper will first read the title and then scroll around to look at the figures before reading any of the text. That's what I did too. One weakness of this paper is that the graphs were hard to understand without reading a lot of the text. I wonder if the presentation of the information in the graphs could be simplified.", "questions_to_authors": "In figure 3 in the right most panel there must be one point that has a very high truth value but the bar for that is not visible to me so I don't understand why the scale on the x-axis has to go all the way above 10 when the other plot stops around 3.", "extra_scores": {}}, {"review_id": "vPOAKQkaLj", "reviewer": "Reviewer_bGRo", "summary": "The paper introduces a new benchmark, TOFU, for evaluating machine unlearning. TOFU, a Task of Fictitious Unlearning, contains 200 diverse synthetic author profiles, each consisting of 20 question-answer pairs, and a subset of these profiles called the forget set that serves as the target for unlearning. Since much of the unlearning literature focuses on discriminative tasks, this paper introduces a set of metrics aimed at evaluating machine unlearning for generative tasks. The paper then evaluates a set of simple unlearning baselines on their dataset using their proposed metrics to and show the tradeoffs between unlearning and maintaining model utility.", "questions": "", "limitations": "", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your time and the careful review. We are glad you found our evaluation setup holistic and the benchmark useful. \n> Positioning of the work\n\n1 We work on approximate unlearning, and we do not cover exact unlearning. All experiments, evaluations, and discussions are consistent. We will add this clearly and explicitly to the draft and we appreciate you for identifying this missing detail!\n\n2 We respectfully disagree with the point about forget quality not being the right measure of unlearning. Forget quality is the central definition of unlearning that we use. It is not supposed to change whether unlearning was intended for bias or privacy. The term forget quality is an umbrella that encapsulates any of these subdomains. Our definition is focused yet expansive and we believe forget quality is indeed the right metric to measure here.\n\n3 We will further elaborate on points [1,2] in the work along with citing the additional works.\n\n> Evaluating forget quality\n\n1 Cost of retain model: We believe there is a misunderstanding in our premise. Because unlearning evaluation in the global sense is not feasible, we aim to create a benchmark that allows (1) researchers to iterate quickly with various unlearning methods, and (2) allows for evaluation against a truly held-out model where we have complete control on training data exposure. TOFU does precisely this. By having a small finetuning and unlearning set, it allows researchers to do both. \n\n2 Predictive multiplicity: This is a great point, and we took great care in our work to make sure that the problem from predictive multiplicity does not impact results on TOFU. In that spirit, please refer to the 8 points highlighted in Appendix G. Also note that our test of unlearning is based on a set of data, rather than one data point. On a high level, to impact the result of the test statistic, predictive multiplicity needs to happen on a subset that is big enough, which we do not observe.\n\n> Weak baselines\n\nWe want to set the expectations upfront that this work aims to create a “first” benchmark for unlearning in LLMs. Until now, there is no single setup where researchers can unite their efforts on developing unlearning methods for LLMs. The goal of the benchmark is not to develop the an unlearning method. Specifically talking about the enlisted unlearning methods, they are not positioned for LLMs, rather for classification models, thus they do not apply directly to TOFU (could be adapted by future researchers)", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The paper is well-written and easy to understand.\n2. The paper introduces a useful benchmark for evaluating machine unlearning for generative tasks. The benchmark also includes several paraphrases of the attack question to holistically evaluate the unlearning performance. \n3. The paper talks about the challenges of evaluating machine unlearning and introduces a set of metrics to evaluate unlearning for generative tasks.", "reasons_to_reject": "1. *Positioning of the work*: The positioning of the work needs to be clearly stated. Although the paper makes effort in Section 1.1 to position their work w.r.t. the rest of the literature, there are some missing details.\n    1. Machine Unlearning techniques can be broadly classified into two categories exact unlearning and approximate (or inexact) unlearning. Exact unlearning (seminal SISA paper [1]) functions by utilizing an ensemble of models (trained on different dataset partitions) and removing/retraining the affected components of the model. Such approaches can guarantee that unlearning is exact because none of the components were trained on a specific data point. Evaluating forget quality doesn’t apply to these models as they can already guarantee unlearning. This paper needs to say clearly that they are trying to evaluate approximate (or inexact) unlearning methods and also add a discussion about exact unlearning.\n    2. Even in approximate unlearning, forget quality is not always the only/right way to measure unlearning. The definition of unlearning is quite abstract in the sense that it only requires the approximate unlearned model to be similar to the model retrained from scratch [2]. This is hard to certify (as already mentioned in the paper Section 2.2 by citing [3]). Since there is still ambiguity people have relied on more user specifications like privacy, forget quality, etc. [4] introduces and formalizes these notions talking about the tradeoffs between each of the forgetting notions. Your paper should cite this work. I understand that a lot of recent papers has taken forget quality as the defacto metric, but that is **not** the correct approach.\n    3. Overall, the paper should clarify that it is only evaluating approximate unlearning methods and then say that they are focusing only the application-dependent forgetting quality and justify their choice.\n2. *Evaluating Forget Quality*: There are several weaknesses involved in the forget quality evaluation. \n    1. Currently, it relies on a model that has been trained from scratch on the retain set. For larger datasets, doesn’t this defeat the whole purpose of unlearning? Since for evaluating unlearning you’re retraining a model from scratch, which you can directly use as the unlearned model.\n    2. Also, when the paper trains from scratch on the retain set, do they use a single model or multiple models? Using a single model is troublesome as it may suffer from predictive multiplicity [5]. Based on the training hyperparameters and stochasticity of SGD, different runs may produce models that generate very different outputs for the forget set. Choosing one such retrained model, may arbitrarily penalize one of the baseline models over another.\n3. *Weak Baselines*: The baselines presented in this paper are quite weak and the work ignores a significant number of recent baselines like Fisher Forgetting, NTK Forgetting, and so on (you can find a complete list in [4]). Also, it may be good to include a brief description of the baselines within the main body of the paper. I was struggling to understand what “Finetune” meant.\n\n\n[1] https://arxiv.org/pdf/1912.03817.pdf\n\n[2] https://arxiv.org/pdf/1911.03030.pdf\n\n[3] https://arxiv.org/pdf/2110.11891.pdf\n\n[4] https://proceedings.neurips.cc/paper_files/paper/2023/file/062d711fb777322e2152435459e6e9d9-Paper-Conference.pdf\n\n[5] https://arxiv.org/pdf/1909.06677.pdf", "questions_to_authors": "Please respond to the questions in the Weaknesses section.", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper presents a benchmark, metrics, and implementation of baselines for unlearning LLM, which is an urgent and important topic. 
The paper is well-written and the benchmark is well-thought. 

There are some concerns about whether the dataset can represent real-world scenarios as the dataset is artificial and small. There are also some concern about the position of the paper and some technical details of the proposed metrics. I would suggest the authors address these comments in the revision.

[comments from the PCs] Please followup on the AC recommendations. Potentially in the limitations discussion.
Decision: Accept