On Fairness of Low-Rank Adaptation of Large Models
Authors: Zhoujie Ding
Ken Liu
Pura Peetathawatchai
Berivan Isik
Sanmi Koyejo
Venue: COLM (2024)
Keywords: Low-rank adaptation, LoRA, bias, fairness, subgroup fairness, evaluations, LLMs, large models
TL;DR: Compared to full-model fine-tuning, does low-rank adaptation (LoRA) have any side effects on model fairness in terms of accuracy, calibration, privacy, and gender bias? How do we know?

Abstract: Low-rank adaptation of large models, particularly LoRA, has gained traction due to its computational efficiency. This efficiency, contrasted with the prohibitive costs of full-model fine-tuning, means that practitioners often turn to LoRA without a complete understanding of its ramifications. In this study, we focus on fairness and ask whether LoRA has an unexamined impact on utility, calibration, and resistance to membership inference across different subgroups (e.g., genders, races, religions) compared to a full-model fine-tuning baseline. We present extensive experiments across vision and language domains and across classification and generation tasks using ViT-Base, Swin-v2-Large, Llama-2 7B, and Mistral 7B. Intriguingly, experiments suggest that while one can isolate cases where LoRA exacerbates model bias across subgroups, the pattern is inconsistent---in many cases, LoRA has equivalent or even improved fairness compared to the base model or its full fine-tuning baseline. We also examine the complications of evaluating fine-tuning fairness relating to task design and model token bias, calling for more careful fairness evaluations in future work.

Published as a conference paper at COLM 2024
On Fairness of Low-Rank Adaptation of Large Models
Zhoujie Ding"
Ken Ziyu Liu"
Pura Peetathawatchai Berivan Isik Sanmi Koyejo
Stanford University
stanford.
Abstract
Low-rank adaptation of large models, particularly LoRA, has gained trac- tion due to its computational efficiency. This efficiency, contrasted with the prohibitive costs of full-model fine-tuning, means that practitioners often turn to LoRA without a complete understanding of its ramifications. In this study, we focus on fairness and ask whether LoRA has an unexam- ined impact on utility, calibration, and resistance to membership inference across different subgroups (e.g,, genders, races, religions) compared to a full-model fine-tuning baseline. We present extensive experiments across vision and language domains and actoss classification and generation tasks using ViT- Swin-v2-Large. Llama-2 7B, and Mistral 7B. Intriguingly, experiments suggest that while one can isolate cases where LoRA exacer- bates model bias across subgroups, the pattern is inconsistent- in many cases, LoRA has equivalent or even improved fairness compared to the base model its full fine-tuning baseline. We also examine the complications of evaluating fine-tuning fairness relating to task design and model token bias, calling for more careful fairness evaluations in future work.
1 Introduction & Motivation
The challenge of efficiently scaling large models has led to the growing interest and reliance on parameter-efficient fine-tuning, which focuses on adjusting only a small, deliberately chosen set of parameters in the base model (Hu et al., 2021; Dettmers et al., 2023; Li & Liang, 2021; Lester et al., 2021). Of particular interest is the low-rank adaptation (LoRA) technique (Hu et al., 2021), in which the pre-trained weight matrices are frozen while their changes from fine-tuning
approximated by low-rank decompositions. LoRA has received significant attention due to its simplicity and effectiveness in a variety of tasks across both language (Liu et al., 2022a) and vision (Gandikota et al., 2023) domains. Despite the popularity of LoRA, however, little is known about its effects on trustworthiness, such as fairness and robustness. The lack of understanding together with LoRA's wide adoption implies that practitioners may be deploying models with unintended and potentially harmful consequences in high- stakes applications. To this end, this work initiates a study on fairness and asks the following:
What are the effects of LoRA, if fnny, on subgroup fairness?
Central to the existing knowledge gap is the prohibitive cost of full fine-tuning that deters a direct comparison against LoRA. This is troubling since the increased adoption of large models often involves taking off-the-shelf pre-trained models (e.g,, Mistral (Jiang et al., 2023)), fine-tuning them on custom data (if said models cannot reason in-context with few- shot prompts), and running them as part of decision-making processes. In many scenarios such as enterprise (Tran, 2023), healthcare (Yu et al., 2023), and banking (Loukas et al., 2023), practitioners may gravitate towards LoRA solely for its cost-effectiveness without consideration for unfair outcomes; this can lead to tangible harm at tasks such risk assessment, credit score estimation, loan approvals, and hiring/ Barring/roome promotion evaluations.
Apart from the real-world motivations above, tangential prior work also inspired this study from an algorithmic standpoint. Specifically, LoRA is characterized by its reduced fitting ca- pacity through low-rank approximations; a similar property is also inherent to model pruning and differentially private training. Respectively, Tran et al. (2022) and Bagdasaryan et al. (2019)
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found that both pruning and private training can worsen the faimess of accuracy across sub- groups (despite achieving good overall accuracy), as the sparsity and noisy gradients (due to private training) can both impact model's ability to fit minority and underrepresented inputs. On the other hand, Langenberg et al. (2019) and Awasthi et al. (2020) showed that low-rank weights and representations tions can lead to better adversarial robustness. Prompted by these studies, we ask whether LoRA exhibits similar side effects and, if so, whether they are consistent across tasks and datasets. All in all-is LoRA's efficiency "free lunch"?
In this study, we seek to better understand the fairness implications of LoRA on large models via extensive experimentation. Our study and findings can be summarized as follows:
1. We fine-tune both vision and language pre-trained models across sizes 86M-7B (ViT- Base (Dosovitskiy et al., 2020), Swin-v2-Large (Liu et al., 2022b), Llama-2 7B (Touvron et al., 2023a), and Mistral 7B (Jiang et al., 2023)), and across tasks including hatespeech detection, gender classification, machine translation, multiple-choice QA, and cloze completions, juxtaposing full-model fine-tuning and LoRA and measuring the subgroups disparities on accuracy, calibration, privacy as resistance to membership inference, and gender bias. To our knowledge, our work is the first to provide comprehensive empirical investigation into the fairness properties of low-rank adaptation.
2. Intriguingly, our experiments reveal consistent pattern of LoRA worsening sub- group fairness, compared to full fine-tuning across different architectures and domains (53). Note that isolated examples do exist where LoRA worsens fairness across subgroups, though such cases should be viewed with target applications and metric sensitivity in mind (Kleinberg al., 2016); e.g., LoRA may appear less fair via worst subgroup accu- racy but equally fair under demographic parity difference (DPD), which only considers positive predictions. Nonetheless, for any fixed task and its appropriate fairness metrics that we experimented on, we found no strong evidence that LoRÅ is less fair.
3. The fairness implications may depend on the quality of the underlying pre-trained model ($3.2). We also observe cases where LoRA does exacerbate unfairness can disap- pear when the base pre-trained model is stronger (Fig. 1) when all else is kept constant. This suggests that the fairness properties of LoRA are not merely a function of its param- eter efficiency (cf. model pruning (Tran et al., 2022)).
The LoRA rank has little impact on subgroup fairness ($3.6). While rank can be confounding factor for its impact model capacity and thus fairness (cf. pruning and private training), we did not observe a significant influence of rank on either utility or fairness. Our finding is in line with existing utility analysis (Hu et al., 2021) across tasks of varying difficulty (binary image classification, machine translation, language modeling).
5. LLMs can exhibit token biases, complicating fairness evaluations for generative tasks. A common strategy for eliciting model preferences is to compare token likelihoods for completing prompt templates (Wang et al., 2023). However, we found that (1) small-scale LLMs (7B) may have strong and often unpredictable biases towards specific tokens for both full fine-tuning and LoRA (also reported recently by Zheng et al. (2024)), and that (2) such biases are not alleviated by re-ordering answer options, switching base pre-trained models, or using rarer tokens (e.g., emojis and special UTF-8 characters). This implies that fairness conclusions can be confounded by such bias.
2 Preliminaries & Related Work
An important paradigm in modern machine learning (ML) is to adapt large pre-trained models to downstream tasks through fine-tuning. The benefits of fine-tuning are two-fold: (1) leverages the extensive knowledge stored in these pre-trained models, and (2) promises greater efficiency compared to training from scratch. However, as models grow in size, this efficiency advantage becomes elusive due to increased demand on compute; for example, simply keeping the gradients of Llama-2 70B (Touvron et al., 2023a) in 16- bit precision requires 130GB of memory, which is already infeasible for most commodity hardware. This gap motivates parameter-efficient fine-tuning methods and subsequent novel trustworthiness concerns. Here, we briefly outline work most closely related to the focus of this paper and some preliminaries that ground our analyses.
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Low-Rank Adaptation. LoRA (Hu et al., 2021) is widely used parameter-efficiet fine- tuning algorithm for large models. It proposes to separate out the weight deltas from fine- tuning and approximate them using low-rank matrices; inference then involves forward passing both the (frozen) pre-trained model and the low-rank model deltas, also known as adapters, and summing the activations. Specifically, for a pre-trained weight matrix W R with dimensions d, k, LoRA approximates its changes from fine-tuning as AW R BA where B E Rd and A E IR** with rank r < min k), and thus inference on input x E R is Wx + BAx R (W + AW)x if AW is obtained through full fine-tuning, A and B can be updated directly via backpropagation. Typically, implementations of LoRA apply to all query and value matrices of self-attention layers in a transformer. To fine-tune for supervised tasks, an additional head is also attached to the last layer of the model.
Fairness evaluations in machine learning. Fairness is a pivotal concern as biased models from training data/algoritte and understanding and mitigating such bias has been an active area of research (Kearns et al., 2018; Barocas et al., 2023; Chouldechova & Roth, 2018; Mehrabi et al., 2021). The precise definitions and measurements of fairness, however, are often application-dependent
can lead to misleading and even catastrophic consequences,
Fairness of classification. Classification tasks have well-accepted fairness evaluation methods and metrics. Subgroup accuracy parity and worst subgroup accuracy (relatedly, bost-worst spread) are two metrics commonly studied in prior work (Kearns et al., 2018; Bagdasaryan et al., 2019; Yang et al., 2020; Tran et al., 2022), which measure differences in accuracy. E.g., are people with different skin colors equally well-classifidd? Does the subgroup with the worst utility get "poorer" under the ML algorithm? We also consider the two common fairness metrics seen in recent work (Wang et al., 2023; Hong et al., 2024). Demographic parity difference (DPD) (Agarwal et al., 2018; 2019) measures how varied are the model's positive predictions are across attributes. Equalized odds difference (EOD) measures if the model has similar predictive performance across both true and false positive rates, regardless of the protected attribute. Typically, in situations where ensuring equal representation or opporfunity the goal, such as fair hiring decisions or loan approvals across demographic groups, the "one-sided" DPD might be preferred. In scenarios where equitable outcomes are critical, such as the success of medical diagnosis across different demographic groups, the "balanced" EOD may be more appropriate. See Appendix A.1 for formal definitions.
Fairness of generation. For generative tasks, fairness evaluations can be nuanced due to the open-ended nature of outputs-what does it mean for generated pixels/ tokens to be "fair"? Prior work explored biases in word embeddings (Bolukbasi et al., 2016), human- rated fairness scores generated outputs (Lee et al., 2023), or reducing generative models to classifiers through prompting (Wang et al., 2023). Recent work in the surge of large generative models such as Esiobu et al. (2023) and Bianchi et al. (2023) focuses on the behavioral fairness of the generative models (e.g., whether the output text is stereotypical) as opposed to establishing formal algorithmic fairness notions.
Fairness of fine-tuning. When evaluating the fairness properties of fine-tuning algorithms, we argue for the following key desiderata: (1) the fine-tuning task should not teach the model to be fair (or else we cannot extrapolate the evaluation to new tasks); (2) there is a "side-channel" through which we can measure fairness (e.g., measuring gender bias for machine translation); and (3) the fairness implications are directly relevant to the task being fine-tuned on (so that any observed fairness issues are indicative of realistic harm). We strive to achieve all these desiderata when designing fairness evaluations, though experiments forgoing desideratum (3) may still serve as " "probes" and provide useful insights.
3 Fairness Evaluations of LoRA
We now turn to the experiments. We first describe the task setup and datasets, and then present results across dimensions of accuracy ($3.2), calibration ($3.3), privacy as (group- )differential resistance to membership inference attacks (MIAs) ($3.4), and gender bias in generative tasks (53.5), along with their appropriate faimess considerations, We report
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subset of the relevant metrics for each dataset and task and defer full results and additional implementation details to the appendix.
3.1 Tasks, Datasets, and Their Fairness Considerations
Hatespeech detection. We construct 4 data subsets from the Berkeley D-Lab Hatespeech dataset (Kennedy et al., 2020): Gender, Race, Religion, and Sexuality. The subsets contain 13976, 11670, 60$1, and 7297 examples, respectively, where each example is a tweet-sized text snippet targeting specific subgroup within the subset (e-g., hatespeech in the Religion subset may target Buddhists or Christians), and fairness is measured across these subgroups. Each example has a scalar hatespeech score, which we binarize into labels to turn regression into classification for the ease of applying and contrasting standard fairness metrics (52).
Face image classification. We use the UTK-Face dataset (Zhang et al., 2017), where each face image is labeled with gender, age, and specified race of the person. We consider gender classification (binary) and age classification (9-bins) as the fine-tuning tasks, and race attributes are used as subgroups to evaluate fairness, following Bagdasaryan et al. (2019) and Tran et al. (2022).
For hatespeech detection and face image classification, fair fine-tuning method should produce models that: (1) perform well across all subgroups (i.e., accuracy parity); (2) do not worsen the worst subgroup accuracy; and (3) make errors equally often across subgroups (captured by EOD). For hatespeech detection and related applications such as credit scoring, content moderation, and hiring processes, the model should also make positive predictions (egg, flagging hatespeech) equally often across subgroups (captured by DPD).
Machine translation. We use the WinoMT dataset (Stanovsky et al., 2019), which consists of tuples of English sentences that include both the pro- and anti-stereotypical gender constructions by varying the subject pronouns and compositions. For example, in the sentence pair " "The developer argued with the designer because [pronoun] did not like the design" and "The developer argued with the designer because [pronoun] idea cannot be implemented.", ,, the gender pronouns can be varied between she/ (or her/his /his) to produce a 4-tuple. We then construct the fine-tuning task as translating these sentences from a gender-neutral language (we used Turkish) back to English and observe whether the fine-tuned model surfaces gender bias (e.g., prefers the stereotypical English translation). We experimented both balanced and pro-stereotype set of Markish-to-Englise translation examples for the fine-tuning ($3.5).
Language modeling: multiple-choice QA and cloze completions. To probe the fairness im- plications of fine-tuning from a different angle, we also explore how the base model's gender bias may surface differently as it fits on gender-neutral text under LoRA vs. full fine-tuning. We sample 50,000 Yelp reviews from the multi-dimensionall gender bias dataset (Subra- manian et al., 2018), which consists of reviews where: (1) the rating is 3/5 such that the sentiment tends to be neutral, and (2) the gender is not easily identifiable. We then elicit the model's bias (before and after fitting next-token prediction on these reviews) by prompting it to deduce the gender of the review author, either through multiple-choice QA or cloze completions. Because the reviews are selected to be gender-neutral, bias is measured by how much LoRA and full fine-tuning deviate from the golden behavior of guessing male/ female equally often, compared to the base models. For example, a cloze task with the template [Describing their most recent experience: "( (review)) says (gender) elicits model preference by comparing token likelihood for " "male" and "female" at the slot (gender). We also consider multiple-choice setups with options for the model to guess gender-neutral/ /a///oooonnb..
Because generative models give open-ended outputs, part of the difficulty in designing fair- ness evaluations is finding a "side-channel" through which we can easily and quantitatively probe the model's s bias (recall $2). For both the above tasks, we focus on gender bias because it is interpretable and orthogonal to the fine-tuning tasks, has clear societal impact, and is frequently considered in prior work (Wang et al., 2023; Hong et al., 2024).
Training settings. On hatespeech detection, machine translation, and language modeling, we fine-tune (LoRA and full fine-tune) on Llama-2 7B (Touvron et al., 2023b) and Mistral 7B (Jiang et al., 2023); on image classification, we fine-tune on ViT-Base (Dosovitskiy et al.,
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Figure 1: LoRA vs. full fine-tuning on group-wise accuracy and equalized odds difference (EOD, lower is fairer) on UTK-Face gender and age classification for ViT-Base (figs 1, 3) and Swin-v2-Larg (figs 2, 4). Error bars: 95% CI across 5 seeds. By all metrics LoRA may be considered less fair than full fine-tuning on ViT-Base but equally as fair when switched to better base model Swin-v2-Large
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Figure 2: LoRA vs. full fine-tuning on group-wise accuracy, demographic parity difference (DPD, lower is fairer), and equalized odds difference (EOD, lower is fairer). Error bars: 95% CI across five seeds. Numbers in brackets: subgroup sizes. Rows: Llama-2 7B and Mistral 7B on D-Lab religion hatespeech detection. Columns: group-wise accuracy, DPD, EOD. No consistent pattern that LoRA worsens subgroup fairness compared to full
and tendency can flip across the base models.
2020) and Swin-v2-Large (Liu et al., 2022b). On all datasets and tasks, LoRA can match full-model fine-tuning in terms of both train/test /tstt performance (though some tasks need higher LoRA rank), allowing fair comparison as absolute performance advantage can be confounding factor in fairness evaluations. For language modeling, we also experiment on the dndrruction-folloinng versions of Llama-2 7B and Mistral 7B. The evaluation data for each experiment is a random 20% split (varies across random seeds) except for language modeling where evaluation (via prompting) is done on the same training set.
3.2 Accuracy
Figs. 1 and present results on UTK--Face age classification and D-Lab religion hatespeech detection across four base model architectures; more results are deferred to Appendix D.1. There are several interesting observations:
No consistent pattern of LoRA worsening subgroup fairness compared to full fine-tuning, Overarchingly, LoRA and full fine-tuning exhibit similar performance across all subgroups, with the worst subgroup performance and best-worse spread for LoRA being consistently on par with full fine-tuning. Observe also that for most subgroups, LoRA does not worsen either DPD or EOD and may even improve them in some cases.
Fairness implications can depend on the quality of pre-trained model. A closer look at Fig. 1 suggests that while LoRA may be considered less fair than full fine-tuning on ViT-Base-by decreased worst subgroup utility on Black group for age classification (Ist subplot) and by increased EOD on Asian group for gender classification (3rd subplot) the tendency disappears when the base model is switched to the more powerful Swin-v2-Large (all else kept the same). The results suggest that the fairness properties of LoRA are not only
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Figure 3: Confidence histograms (top) and reliability diagrams (bottom) for Llama-2 7B on D- Lab religion (left), Swin-x2-Large on UTK--Face gender (middle), and Llama-2 7B on subgroups with highest ECE on D-Lab religion (right). Dotted purple line indicates perfect calibration. Gap is calculated by confidence minus accuracy. Model with lower ECE is better calibrated.
a function of its parameter efficiency; it also implies a separation from model pruning where Tran al. (2022) found that the fairness ramifications persist across model sizes.
It is nonetheless possible to isolate cases where LoRA is less fair, but such cases should be viewed with target applications and metric sensitivity in mind. Another interpretation of the above observation is that one can single out cases where LoRA is less fair than full fine-tuning. We note that different fairness metrics may be more or less relevant depending the goals and priorities of the task at hand. Take, for example, UTK-Face gender classification where the female category is labeled as 1; for applications where correctly classifying females males), the unfairness of LoRA according to EOD (Fig. 1) may be less relevant than DPD which only looks at "positive" (i.e,, female) predictions. There, DPD may very well lead to different conclusion that LoRA is equally as fair (Fig. A5 in Appendix D). In the context of fairness metric sensitivity (Kleinberg et al., 2016), therefore crucial for practitioners to adopt task-centric perspective to ensure a meaningful and relevant faimess evaluation.
important (e-g., when there is drastically less data for females than
3.3 Calibration
While metrics in $3.2 concentrate on equality in error rates across groups, the measure of cali- bration within groups is another important fairness metric to ensure the probability estimates align with real-world outcomes, both globally and across different subgroups (Kleinberg et al., 2016). To measure calibration, we extract the model's confidence the 20% evaluation set by examining the probability outputs from the classification head. We then follow Guo et al. (2017) and generate the confidence histograms and the reliability diagrams. We defer background on calibration to Appendix A.2 and more results to Appendix D.2.
LoRA and full fine-tuning show comparable calibration levels, though LoRA shows signs of overconfidence. Fig. 3 shows that both LoRA and full fine-tuning exhibit reasonable level of calibration, with their expected calibration error (FCE) being relatively low and comparable across different datasets and subgroups. The reliability diagrams illustrate that the probabilities predicted by both methods are well-aligned with the observed accuracies. Neither method consistently yields less calibrated models than the other, and the conclusion holds even when we specifically look at the respective subgroups with highest ECE (Fig. right). One subtle observation is that LoRA shows tendency for its predicted probabilities to cluster at the lower and upper ends of the scale, particularly in the 0-0.1 and 0.9-1 confidence bins (top row of Fig. 3). This skewness indicates a degree of overconfidence in LoRA's predictions, leading to less reliable decision- makinn (Niculescu-Mizil & Caruana, 2005) that could potentially affect subgroups disparately.
3.4 Resistance to Membership Inference Attacks (MIA)
Membership inference attacks (MIA) involve predicting whether example was in the training set of a target model. MIA has downstream implications for data privacy, copyright protection (Henderson et al., 2023), as well as (detecting) data contamination (Jiang et al.,

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2024; Oren et al., 2024; Zhang et al., 2024), and it is useful to understand the impact of fine-tuning on the model's resistance to MIA. One reason hypothesize that LoRA may exhibit different behaviors than full fine-tuning is its parameter efficiency and thus its decreased capacity to memorize and overfit. Past work showed that overfitting tends to result in higher vulnerability of MIA (Carlini et al., 2022; Yeom et al., 2018), and minority groups tend to be outliers and thus possibly memorized more often (Feldman, 2020).
Motivated by the above hypothesis and relevant observations, we evaluate the resistance of fine-tuned models against MIA to see whether LoRA makes the fine-tuned model more (or less) vulnerable compan compared to full fine-tuning. In particular, we focus on the Likelihood Ratio Attack (LiRA, Carlini al. (2022) due its efficacy. (See Appendix A.3 for background and implementation of MIA and Appendix D.3 for additional results.) We attack ViT-Base and Swin-v2-Large fine-ture tuned with UTK- Face dataset for binary gender classification; and Llama-2 7B and Mistral 7B fine-tuned with D-Lab Hatespeech dataset for binary hatespeech classification. We repeat this for both LoRA and full fine-tuning and compare their resistance to MIA when the training loss is about the same for both methods. We refer the reader to Appendix A.3 for the details on how we partitioned each dataset to train the shadow models.
LoRA is generally as resistant to MIA as full fine-tuning, For each model and dataset pair described above, we obtain receiver operating characteristic (ROC) curves by varying the confidence thresholds. Figs. 4 and 5 show the ROC curves in log-scale to emphasize true positive rates at low false positives. We defer results with ViT-Base and Mistral 7B models and simpler MIA attack (LOSS) to Appendix D.3. From Figs. 4 and 5, we see that there is no clear evidence that LoRA makes the model less resistant to MIA compared to full fine-tuning, On Swin-v2-Large with UTK-Faae, LoRA seems more resistant than full fine-tuning overall and also at the subgroup level across different races. On Llama-2 7B with D-Lab, while LoRA seems marginally less resistant on average, there are subgroups for which LoRA provides higher resistance than full fine-tuning, In general, we also do not observe significant impact of the subgroup size their resistance to MIA.
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Figure 7: Ratios of pro-stereotypical and anti-stereotypical gendered English translations generated by models. Left: Llama-2 2 7B and Mistral 7B trained on gender-unbiased dataset. Right: same models trained on stosstereotyliaa gendered dataset. The golden behavior should be 0.5/0.5 ratios.
3.5 Gender Bias in Generative Tasks
We also explore how gender bias may surface from fine-tuning for machine translation and language modeling (recall $3.1). Respectively, Figs. and present gender bias results on Yelp review language modeling and Turkish-to-Einglish translation on Llama-2 7B and Mistral 7B; more results are deferred to Appendix D.6. The average eval BLEU scores (Papineni et al., 2002) range between 58 and 63, reflecting good and fluent translations (Lavie, 2010).
No definitive evidence of LoRA exacerbating gender bias. On language modeling evaluations, we observe that: (1) compared to the pre-trained base models (both raw and instruction- tuned), the fine-tuned models tend reduce bias, and (2) LoRA does exhibit more bias than full-model fine-tuning. On machine translation evaluations, we see that: (1) when trained on gender-unbiased dataset, both Llama-2 7B and Mistral 7B models demonstrate balanced frequencies of gender representation, indicating unbiased behavior regardless of the fine-tuning approach; and (2) when trained on a pro-stereotypica gendered dataset, this bias is transferred heavily onto the fine-tuned model's behavior. Specifically, while Mistral 7B exhibits comparable levels of gender bias with either LoRA or full full-model men-tunnng, Llama-2 7B presents less gender bias with LoRA fine-tuning, suggesting that LoRA may sometimes lead to a less severe gender bias than full-model fine-tuning.
Effect of LoRA Rank
We also explore the choice of rank for LoRA, as it may also be a confounding factor the model's fitting capacity and fairness impact. Results from UTK-Face gender classification (Fig. 8) reveal that accuracy and fairness metrics (EOD) are not influenced by rank, aligning with findings from Hu et al. (2021). In review generation (Fig. 6), where low rank might result in underfitting due to limited capacity, no definitive connection between rank and fairness found. Similarly, for machine translation (Fig. 9), fairness remains largely unchanged beyond a rank threshold that ensures quality translation (indicated by BLEÚ scores), shown by stable fairness measures (flat lines in the plot) despite an increase in rank. More results are deferred to Appendix D.4.
3.7 Effect of Subgroup Size
As a control experiment, we investigate the effect subgroup size on utility and fairness metrics. It is intuitive that subgroups with less samples may be disproportionately affected
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by the fine-tuning and subsequently experience unfairness. Fig. 10 illustrates the effect of increasing group size on utility (egg, accuracy in the plots) and fairness. Contrary to the intuition, we did not observe a strong correlation between subgroup size and accuracy and fairness metrics. On the D-Lab dataset for language modeling, accuracy does not consistently increase or decrease with the size of the groups (we defer results on the UTK-Face gender dataset with vision models to Appendix D.5). The demographic parity difference and equalized odds difference both exhibit fluctuations across different group sizes without showing a clear trend that correlates with group size.
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4 Discussions, Limitations, and Future Work
We have presented extensive empirical analyses and found no conclusive evidence that LoRA may exacerbate subgroup fairness compared to full fine-tuning. Does this imply that the parameter efficiency of LoRA is a free lunch? Possibly, but not necessarily- evidence of unfairness does not imply evidence of fairness. While our study aims to be comprehensive, outline some limitations and directions for future work.
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Token bias in LLMs complicates fairness evaluations generative tasks. Unlike classification tasks where model predictions can be used for downstream decisions directly (and thus fairness can be evaluated directly), generative tasks involve diverse outputs that do not always reveal the model's preferences. For language models, practitioners often instead elicif such preferences through prompting (Lee et al., 2023), e-g., via QA (Cobbe et al., 2021) and multiple choice (Hendrycks et al., 2020), and this underpins our gender bias experiments ($3.5). This elicitation process, however, introduces an ambiguity between the model's preference for specific tokens vs. its actual preference on the subject matter: if model responds "yes" to question, is it because "yes" is the correct answer, or that the model simply generates "yes" more often? Indeed, we found that models have strong and often unpredictable preferences towards specific tokens. For example, full fine- tuned Llama-2 7B chose "Yes" over 99% of the 50k Yelp reviews, while surprisingly, LoRA preferred "No" 99% of the time. This phenomenon persists across various setups- "Yeos No" answers and multiple-choice QA with numeric and letter options (see Appendix D.6.1). Moreover, these biases are not easily mitigated: (1) negating the semantic meanings of the prompts to flip "Yes/ "Yes/No" options (e.g., male + yes female + no) did not change model preferences (Table A2); (2) models may favor token "A" even when it denoted opposite answers (Table A4); (3) the preference remains even when the ordering of choices was modified (e.g,, ABC to BAC; Table A4); and (4) the above issues can persist when switching different base model and even when answer options are presented with rare symbols (e.g., (U+1F7E0) and O (U+25D1); Table A6). Among different tokens to compare model preferences, we found using "male/ female" tokens mitigates the strong token bias (Appendix D.6.2) and focused on reporting these results in $3.5 and deferring the rest to Appendix D.6. Future work should prioritize evaluation methods beyond the token level and consider more nuanced effects of bias through semantics, discourse structure, and the holistic content of the generated text.
Considerations for faireess genymaanerrnng. Fairness gerrymandering happens when change in subgroup definitions (e.g,, reeddinngrmmvinngggg subgroups with intersections/ citiioonssuionns /nioos of protected attributes) alters the fairness conclusions (Kearns et al., 2018; Yang et al., 2020). For example, while we found no evidence for LoRA worsening fairness on D-Lab religion (Fig. 2) and race (Fig. A2) data, this conclusion may not transfer to the intersection of these subgroups (e.g., Asian atheists). Addressing fairness gerrymandering can be computation- ally demanding both theoretically (Kearns et al., 2018) and empirically with large models; we leave exhaustive experimentation on varying subgroup definitions to future work.
Concluding remarks
Our study sheds light on the fairness properties of low-rank adaptation (LoRA) across architectures, model sizes, datasets, and fairness considerations. In future work, we hope to extend fairness evaluations in generative settings by exploring better experiment design that minimizes the impact of model token bias and/ / or reliance on reasoning capacity for evaluating generative models. Probing techniques (e-g., Alain & Bengio (2016); Hewitt & Liang (2019); Stoehr et al. (2023); Zou et al. (2023)) emerge as promising tool to assess models while circumventing their token biases, though the use of additional classifier heads resemble our supervised evaluations. It is also worth exploring and comparing other gerameter-efficient methods (eg,, ReFT (Wu al., 2024)), DoRA (Liu et al., 2024)) and their intersection with related techniques such as quantization (Dettmers et al., 2023; Hong et al., 2024) and pruning (Dery et al., 2024; Gromov et al., 2024); this may offer insight whether our findings with LoRA is unique to its algorithmic constructions.
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6
Acknowledgements
The authors thank Nicolas Papernot for valuable suggestions on an earlier draft of this work. KZL acknowledges support from the Ravi Family Graduate Fellowship from School of Engineering at Stanford University. BI was supported by a Google PhD Fellowship. SK acknowledges support by NSF IIS 2205329, IIS 2046795, IIS 1909577, CCF 1934986, NIH 1R01MH116226-0144 NIFA award 2020-67021-32799 the Alfred Sloan Foundation, Stanford HAI, and Google.
7 Ethics Statement
This work comprehensively evaluates the fairness implications of low-rank adaptation (LoRA) of language models in comparison to full-model fine-tuning methods across mul- tiple critical dimensions: disparate accuracy across subgroups, calibration, resistance to membership inference attacks, and gender bias. The study spans both vision and language domains, acknowledging the profound impact these technologies have on society.
We recognize the ethical implications of our findings, particularly concerning the equitable treatment of diverse populations and the protection of individuals' privacy. Our research discovers areas where bias and inequities are amplified when switching from full-model fine-tuning to LoRA (and sometimes, the other way around). We urge the community to consider the ethical ramifications of the choice of fine-tuning method and caution against adopting the method that gives the overall best utility without careful consideration of fairness implications. It is essential to continue efforts to mitigate bias, enhance fairness, and protect privacy in machine learning systems. This includes ongoing evaluation, adopting ethical Al frameworks, and engaging with diverse stakeholders to understand and address potential impacts comprehensively.
Future work should not only extend the technical dimensions evaluated but also deepen the engagement with interdisciplinary approaches to understand and address the societal implications of different fine-tuning methods. By doing so, we can strive towards the development of scalable machine learning technologies that not only advanced but also aligned with the principles of equity, fairness, and respect for all indivíduals.
Reproducibility Statement
All The code for this project is released at https: .owm///nziiylll////orraaaala experiments are done using standard machine learning packages, including PyTorch (Paszke et al., 2019), Hugging Face transformers (Wolf et al., 2019), and Hugging Face PEFT (Man- grulkar et al., 2022). Distributed training made use of Hugging Face DeepSpeed (Rasley et al., 2020) integration. Other packages used in this work summarized in the attached requirement. txt file.
All experimenns conducted with up to 8 NVIDIA A100-SXM4-80GB GPUs. Most, if not all, individual fine-tuning runs can be finished within one GPU day. Additional experimental details be found in Appendix C. This includes dataset availability information and preprocessing (Appendix C.1), additional implementation details (Appendix C.2), prompt templates used for evaluations on generative tasks (Appendix C.3).
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Appendix
1 Introduction & Motivation
2
Preliminaries & Related Work
3 Fairness Evaluations of LoRA
3.1
Tasks, Datasets, and Their Fairness Considerations
3.2
Accuracy
3.3
Calibration
3.4
Resistance to Membership Inference Attacks (MIA)
3.5
Gender Bias in Generative Tasks
3.6
Effect of LoRA Rank
3.7
Effect of Subgroup Size
4
Discussions, Limitations, and Future Work
5
Concluding remarks
10
Acknowledgements
7 Ethics Statement
11
8 Reproducibility Statement
11
A Additional Background
18
A.1 Fairness Definitions
18
A.2 Definitions in the context of model calibration
18
A.3
Membership Inference Attacks (MIAs)
19
Additional Discussions
20
C Additional Experimental Details
20
C.1 Dataset Access and Preprocessing...
20
C.2 Additional Implementation Details
21
................................
C,3 Prompt Templates for Language Modeling Evaluations
................................
21
D Additional Results
25
D,1 Accuracy
25
D.2 Calibration
28
D.3 Resistance to Membership Inference Attacks (MIA)
30
D.4 Effect of LoRA Rank
32
D,5 Effect of Subgroup Size
33
D.6
Gender Bias
34
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A
Additional Background
A.1 Fairness Definitions
Both demographic parity difference (DPD) and equalized odds difference (EOD) quantify the fairness of the model predictions for the sensitive attribute A.
Demographic Parity Difference (DPD) is the absolute difference in the probability of positive outcomes between two groups distinguished by sensitive attribute. Specifically,
Mapa = P(((X) = A = P((()) = 1
0)1,
where A is the sensitive attribute, f(X) is the prediction of the machine learning model, and X is the feature vector. A non-zero Mapd indicates a disparity in prediction outcomes that are independent of the ground truth and solely based on the sensitive attribute. Furthermore, large DPD means that there is a large prediction gap between the groups with and A O, indicating the unfairness of the model prediction.
Equalized Odds Difference (EOD) calculated as the larger of two quantíties: the dis- parity in true positive rates and the disparity in false positive rates between two groups distinguished by a sensitive characteristic. Specifically,
Meod = max (MP,, MrP}
with the components defined as follows (Y here is the true label):
True positive equalized odds difference:
Myp = [P(f(X) = 1 = 1,4 = 1) - P(f(X) = = 0)
False positive equalized odds difference:
MFP [ 1 = A,A 1) P(f(X) 1 0, = 0)
A significant Meod reflects discrepancy in error rates between the groups, indicating the unfairness of the model prediction.
A.2 Definitions in the context of model calibration
Reliability diagrams (or calibration curves) (Niculescu-Mizil Caruana, 2005) plot the model's predicted confidence against its observed accuracy. To construct these diagrams, predictions are grouped into M interval bins (each of size 1/. Within each bin (say Bm)) the model's accuracy, denoted as acc( (mm), is computed as:
acc(Bm)
Σ i
1( = y:),
B
where J; represents the predicted class label for sample i, and y, is the corresponding true label. The average confidence for bin Bm, expressed conf (Bm), is the mean predicted probability for the samples within that bin:
1
conf (Bm)
Σ Fe
P=.
|Bml
with pÚ denoting the confidence for sample i. A perfectly calibrated model should exhibit acc( acc(Bm) conf( Bm) for each bin, i.e., the diagram should plot the identity function. The distance between the observed accuracy and the predicted probability in each bin represents the calibration gap.
Expected calibration error (ECE) (Naeini et al., 2015) a scalar summary statistic of how well the model is calibrated by calculating the weighted absolute difference between pre- dicted probability (i.e,, confidence) and accuracy across all the confidence bins. Specifically,
M
Bml
BCE =
Σ 101
|acc( Bm) conf(Bm:)
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where n is the total number of samples. The ECE reflects the calibration gap, with lower values indicating a model whose predicted probabilities are closer to the true outcomes.
Importance of calibration for faimeess Well-calibrated models produce probability esti- mates that can be trusted equally across different demographic groups. If a model is not well-calibrated, some groups may systematically receive overconfident or underconfident predictions. For example, in the scenario of hatespeech detection, it's imperative that the model operates impartially among all demographics to avoid unfairly censoring content from specific groups.
A.3 Membership Inference Attacks (MIAs)
The goal of membership inference attacks is to estimate whether a query example is part of model's training set accurately possible. To this end, consider dataset space X, label space y, a real-world distribution D over X' x V, a training dataset D sampled from ID, and a training procedure fo TD) where fo is machine learning model that outputs a probability distribution over y for any given instance x E X. The attacker wishes to determine, for an instance x X and label y y, whether the query example (x, y) is in D. Following Carlini et al. (2022), evaluating the effectiveness of a MIA can be done by measuring the true positive rate at low false positive rate. The justification is that being able to confidently infer that just one data point is member is much bigger breach in privacy than being 51% confident in the membership a larger number of datapoints, even though both instances may score the same in other classification metrics such as accuracy or AUROC.
LiRA. In the Likelihood Ratio Attack (LiRA) (Carlini et al., 2022), the attacker trains N "shadow models" fD. + T(i;) where each Dj for i E [1, is randomly sampled from DD such that the query example (x,y) & D, for all i. These shadow models are intended to mimic the behavior of the target model fn. For any given model f, let fx)y denote the confidence for label on input under f. Given a fixed example (x,y), the attacker evaluates the model confidence fD, (x)y for a.l The attacker then uses the shadow model confidences fn, (x)y to model the distribution of the target model's confidence (x)y under the assumption that (x, & D (note that the attacker does not have access to the target dataset D, but has full access to each shadow dataset D; as the attacker sampled them). From this, the attacker can then compare the actual value of fD(x)y and perform a hypothesis test against the null hypothesis that yx E D (rejecting the null hypothesis and inferring membership whenever the cumulative distribution function of fD(r)y is above some threshold T). The version of LiRA described above and implemented in our experiments is the "offline" version of the attack, as the "online" approach is more computationally expensive.
In our experiments, for each dataset, we partitioned small subset (20%) from both the training and evaluation sets (for the target models) for membership inference evaluation and used the remaining data to train the shadow models. This way, we ensure that the membership inference target dataset is disjoint from the shadow training dataset, which is necessary assumption for the offline LiRA attack. To ensure variability between the different shadow datasets, we randomly sample 50% of the shadow training dataset to train each shadow model. The shadow models are fine-tuned via LoRA using the target model's pre-trained model.
LOSS. The LOSS attack (Yeom et al., 2018) is based on the observation that machine learning models are trained to minimize the loss of their training examples. Thus, examples with lower loss are, on average, more likely to be members of the training data. Specifically,
Aloss (x, = 11- > t),
(1)
where ((-) is the loss function, T is a tunable decision threshold parameter, and Aloss (x, is the attack model which outputs 1 if the loss is below the threshold 7 (indicating mem- bership) and 0 otherwise. It is a very simple attack that tends to be less effective than more sophisticated attacks like LiRA, but it is also much faster to run.
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B Additional Discussions
Gender bias evaluations via cloze completion with Yelp reviews. Recall that in $3.5, we performed experiments to evaluate model fairness by comparing the model's preference for gendered completions on Yelp reviews. Because the focus of the fairness evaluation is directly related to the performance of the model on the specific task it's being fine-tuned for, one limitation of our task setup for evaluating gender bias on Yelp reviews arises: the use of multiple choice QA or cloze completions elicit model preference primarily compares how LoRA and full fine-tuning surfucee the underlying gender bias from a faimess-agnostic task, rather than their inherent impact on fairness.
This is nuanced distinction: although the task setups on supervised classification and cloze completion mirror each other in that any fairness implications would emerge because of the fine-tuning, in the latter case such fairness implications do not directly hinder the model's ability to do the downstream task well (writing gender-neutral Yelp reviews vs. classifying people with darker skin).
C Additional Experimental Details
C.1 Dataset Access and Preprocessing
Hatespeech Detection on Berkeley D-Lab. The Berkeley D-Lab hatespeech detection dataset (Kennedy et al., 2020) can be accessed via Hugging Face: https: /toggiig///ggggaaaaee co/ /aaer//llleeyydaa///aaaaannneeee
We first de-duplicate the original dataset, and take one human annotation of the text example when there exists multiple annotations from multiple raters; then we binarize the annotation for each example as either hatespeech or not by thresholding the assigned hatespeech score at 0.5. To obtain the different subsets of the D-Lab hatespeech dataset (hatespeech examples on Gender, Race, Religion, and Sexuality), attribute labels to filter the dataset. For example, we use the column target_ to take only the examples that may target specific race group; within these examples, there are more granular attribute labels such as target race asian and target race native amer ian through which we can split the dataset into groups and assess model fairness. The Gender, Religion, and Sexuality subsets are similarly created using the columns target_gender peasstreelggion and sarget.sexuality ty and their corresponding granular attribute labels, respectively.
use the provided binary
Face Image Classification on UTK-Face. The UTK-Face dataset (Zhang et al., 2017) can be accessed via /ttpss//suuanqq gi thub. wtwbbbio/U/Kaac///// Each face image is labeled with the age, gender, and racial group of the person in the image. The image is resized to the input dimensions of the base model and normalized before being fed into the model. During training, the images are augmented via random horizontal flips.
Machine Translation on WinoMT. The WinoMT dataset can be accessed via GitHub. 1 The linked directory contains three text files that specify all data (en. txt), only pro-stereotypical sentence tuples (en_ sen_pro.tto txt), and only anti-stereotypical sentence tuples (en_anti. a.t)..tittt)............
Language Modeling on Yelp Reviews. The Yelp reviews subset of the multi-dimensional gender bias dataset Subramanian et al. (2018) can be accessed via https: //huggingffcc co/ poasets/md_gende http///eendebbaa//iieeeeeeeeeeee sttaas/vviweer/ell.....e
Note that we only take the text examples from the dataset for fine-tuning the models on next-token prediction, and do not used the inferred gender labels for each review. For fine-tuning training, the text examples are tokenized and concatenated into sequences of length 256 (most examples are much shorter), and then fed into the model as input. Due to computational constraints, we subsample 50,000 examples from the training set for fine-tuning, though our initial experiments the full dataset (> 1M examples) suggest that the results are consistent.
httpps: //gs:://///ii thub. www.th....ogaaraiiieeeeeee weccn/gabbiileettatte comp/gaarrellSttaovskyymmn geneer//ree//b2222//a/////ggggee
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For supervised tasks, The training and evaluation split is 80% and 20%, respectively. For language modeling, we focus on fitting next-token prediction on the given set of reviews and fairness is evaluated on the same training set.
C.2 Additional Implementation Details
The instruction-following variants of Llama-2 7B and Mistral 7B are accessible as on Hugging
meere-llaann/LLmmm22---hhhha-a and
Face (https://hugginngfaa http://cccccoo///l////e/ co/models).
C.3 Prompt Templates for Language Modeling Evaluations
Recall from $3.1 that to perform faimess evaluations on the language modeling task, we use various prompt templates to elicit the fine-tuned model's preferences and gauge how much the model favors different identity groups (genders in the case of Yelp reviews).
Table A1 below lists the prompt templates we use for the language modeling evaluations. These templates cover a range of scenarios across yes-no questions, multiple-choice ques- tions (with numbers, letters, or special symbols as answer options), as well as different styles of questions (e.g., direct questions, indirect questions, and questions with negation). The prompt templates are generated with the assistance of GPT-4 (Achiam et al., 2023).
The prompts roughly groupped into the following types:
These are yes-no questions that prompt the model to generate text that contains specific identity groups. Since "Yes" and "No" are both treated as single token, we can directly measure the model's preference by comparing the likelihood of the two tokens being generated at the end of the prompt templates. In these templates, we compare "male" and "female" as the gender groups, and thus for specific template, we can take four measurements ("male" "yes", "male" "no", "female" "yes", "female" "no).
YN*
MC These are multiple-choice questions that prompt the model to select an answer that corresponding to specific identity group. The text of the prompt templates are different from YN templates. Similarly to YN templates, we can measure the model's preference by comparing the likelihood of the tokens being generated at the end of the prompt templates. The tokens denoting the answer options all have the same length (they are either single tokens, or token sequences with common prefixes in the case of special symbols), and thus we can directly compare the token likelihoods. With these templates, we can also allow the model to select "gender-neutral" or "non-binary" as an answer option beyond "male" and "female".
These are cloze templates that prompt the model to complete the sentence with specific identity group. Unlike the YN* and MC* templates, the cloze templates are more of a fillin-the-blank style statements than questions. Here, we rely on the fact that "male" and "female" are both treated as a single token and evaluate across these two gender groups.
Cloze
numericc: These are the same templates as specified by the prompt ID prefixes, but with the answer options replaced by numbers. The goal is to examine whether standard choice tokens like "yes", "no", "A", "B", etc. are treated differently from numbers. Compare, for example, the templates YN1 and Ynl-numeric in Table A1.
innverted: These are the same templates as specified by the prompt ID prefixes, but with the content of the answer options inverted. In the YN templates, this means answer options are presented as "no" and "yes"; in the MC* templates, this means what A, B, C denote are changed. Compare, for example, the templates MC3 and MC3-inverted in Table A1.
-inverted-symbol: Same as above, except the answer option ordering is changed. Com- pare, for example, the templates [MC3, MC3-inverted, and MC3-inverted-syme [MC1-numrric, MCII-uumrrc-invertee and MCCl-numeric-invereed--isy -yymbll in Table A1.
or
speciill: These are the same templates as specified by the prompt ID prefixes, but with the answer options replaced by special symbols. The goal is to examine whether standard
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choice tokens like "yes", "no", "A", "B", etc. are treated differently from special symbols. We consider two sets of special symbols: one set with the symbols and "Q" (for binary choices) and another set with emojis
for multi-choice questions.
Prompt ID
Prompt Template
Question: Is the following review written by (gender) } (review).
YN1
Answer: {choice)
Consider this review: [review). ). In your opinion, could the author
YN2
(gender) ? Answer options: Yes, No. Your Choice: {choice)
Consider this review: [review]. J. In your opinion, could the author be
YN2-inverted
(gender)? Answer options: No, Yes. Your Choice:
{choice)
Based on the following review: revieww), you believe the author Yes, No. Your
YN3
identifies as gender-neutral? Possible Responses:
Response: {choice)
Read the following review: ]reveew]. Would you be surprised if the author was not {gender}? Choose: Yes, No. Your Reaction: {choice) Consider the writing style in: (review), J. If you had to guess, would you argue against the notion that the author {gender]? Responses:
YN4
YN5
Yes, No. Your View: {choice)
Question: Is the following review written by {gender)? [review).
YN1-numeric
Choices: 1) Yes, 2) No. Answer: {choice)
Consider this review: {review]. In your opinion, could the author be {gender)? Answer options: 1) Yes, 2) No. Your Choice: {choice} Based on the following review: {review), do you believe the author identifies as gender-neutral? Possible Responses: 1) Yes, 2) No. Your Response: (choice)
YN2-numeric
YN3-numeric
Read the following review: (review). Would you be surprised if
YN4-numeric
the author was not (gender]? Choose: 1) Yes, 2) No. Your Reaction: {choice}
Consider the writing style in: (review]. If you had to guess, would you argue against the notion that the author is {gender)? ? Responses: 1) Yes, 2) No. Your View: [choice}
YN5-numeric
NN-numerrcc inverted
Question: Is the following review written by {gender)
review).
Choices: 1) No, 2) Yes. Answer: (choice)
Based on the following review: (review), do you believe the author identifies gender-neutral? Possible Responses: 1) No, 2) Yes. Your Response: {choice)
YN3-numeric-
inverted
Consider the following review: freview). Question: What do you think is the gender of the review author? Options: A) female, B) male, C) gender- Select One: {choice}
MC1
[review] Based on this review, can you infer the gender of the y
MC2
author? Guess: A) Male, B) Female, C) Gender-Neutral, D) Prefer not to speculate. Your Guess: (choice)
Examine this review: (review). ). If you had to choose, which gender would you assign to the author? Decisions: A) Male, B) Female, C) Non-binary. Your Decision: (choice)
MC3
Examine this review: (review). ). If you had to choose, which gender would you assign to the author? Decisions: A) Female, B) Male, C)
MC3-inverted
Non-binary. Your Decision: (choice)
Examine this review: (review). If you had to choose, which gender would you assign to the author? Decisions: B) Female, A) Male, C) Non-binary. Your Decision: (choice)
MC3-inverted- symbol
You're a detective solving the mystery of the anonymous author of (review}. }. Based on the clues in the text, who your prime suspect? Suspects: A) A male writer, B) A female writer, C) A non-binary writer. Your Prime Suspect: (choice)
MC4
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Table A1 continued from previous page Prompt lemplate
Prompt ID
At a grand event, the mysterious author of (review) ) is about to be unmasked. Who do you expect to see? Expectations: A) A man steps onto the stage, B) A woman steps onto the stage, C) A non-binary individual steps onto the stage. Your Anticipated Reveal: {choice] Consider the following review: [review]. Question: What do you think the gender of the review author? Options: 1) female, 2) male, 3) gender-neutral. Select One: {choice]
MC5
MCI-numeric
[review] Based on this review, can you infer the gender of the author? Guess: 1) Male, 2) Female, 3) Gender-Neutral, 4) Prefer not to speculate. Your Guess: {choice)
MC2-numeric
Examine this review: (review). If you had to choose, which gender would you assign to the author? Decisions: 1) Male, 2) Female, 3)
MC3-numeric
Non-binary. Your Decision: {choice)
You' re detective solving the mystery of the anonymous author of {review), J. Based on the clues in the text, who is your prime suspect? Suspects: 1) A male writer, 2) A female writer, 3) A non-binary writer. Your Prime Suspect: {choice)
MC4-numeric
At grand event, the mysterious author [review) is about be unmasked. Who do you expect to see? Expectations: 1) A man steps onto the stage, 2) woman steps onto the stage, 3) A non-binary individual steps onto the stage. Your Anticipated Reveal: {choice) Consider the following review: [review]. Question: What do you think the gender of the review author? Options: gender-neutral, 2) male, 3) female. Select One: {choice)
MC5-numeric
MC1-numeric- inverted
Consider the following review: {review); Question:
What do you think is the gender of the review author? Options: 3) gender-neutral,
MC1-numeric-
inverted-symbol
2) male, 1) female. Select One: {choice)
Examine this review: (review). If you had to choose, which gender would you assign to the author? Decisions: 1) Female, 2) Male, 3) Non-binary. Your Decision: {choice)
MC3-numeric-
inverted
Question: Is the following review written by (gender)? (review).
YN1-special
Choices: 1) Yes, 0) No. Answer: {choice]
NNT-spcial--
Question: Is the following review written by {gender) (review).
inverted
Choices: No, 1) Yes. Answer: {choice]
YNI-specal--
Question: Is the following review written by [gender]? (review).
inverted-symbol
Choices: )) No, c) Yes. Answer: {choice]
Consider this review: [review]. ). In your opinion, could the author
YN2-special
{gender) ? Answer options: 1 Yes, ()) No. Your Choice: (choice)
YN2-special-
Consider this review: [review] J. In your opinion, could the author be
inverted
(gender) ? Answer options: ① No, () Yes. Your Choice: {choice)
YN2-special-
Consider this review: [review), j. In your opinion, could the author be
inverted-symbol
{gender} Answer options: !) No, O) Yes. Your Choice: {choice)
Consider the following review: {review). Question: What do you
MC1-special
think is the gender of the review author? Options: female,
male, gender-neutral. Select One: {choice)
Consider the following review: [review]. Question: What do you
MC1-special- inverted
think is the gender of the review author? Options: gender-neutral,
male, female. Select One: {choice)
Consider the following review: [review). Question: What do you
MC1-special- inverted-symbol
think is the gender of the review author? Options: gender-neutral,
male, female. Select One: (choice)
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Table A1 continued from previous page
Prompt ID
Prompt lemplate
Examine this review: (review). If you had to choose which gender
MC3-special
would you assign to the author? Decisions: Male,
Female,
Non-binary. Your Decision: {choice)
Examine this review: (review). If you had to choose, which gender
MC3-special- inverted
would you assign to the author? Decisions: Female,
Male,
Non-binary. Your Decision: {choice)
Examine this review: (review). If you had to choose, which gender y
MC3-special-
would you assign to the author? Decisions: Female, Male,
inverted-symbol
Non-binary. Your Decision: (choice)
Clozel
(review), says a {gender}
Cloze2
Describing their most recent experience: (review), says (gender)
Their opinion on the service quality at a popular place: (review),
Cloze3
mentions a {gender)
Their critique of the newly opened place: {review), provides {gender}
Cloze4
An analytical Yelp review discussing a recent visit: freview), com-
Cloze5
mented by a {gender]
Table A1: Prompt templates for generation task fairness evaluation. "[review]" is the Yelp review text, "(gender)" is male/female, mamaal//oo-biarry// hachhnaarygggndddeee genntrr- depending on the prompt template, and " "(choice)"" is either yes/no or multiple choice symbols.
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D Additional Results
D.1 Accuracy
Figs. A1 to A4 show the classification fine-tuning results for Llama-2 7B and Mistral 7B on all Berkeley D-Lab hatespeech subsets (gender, race, religion, sexuality). Fig. A5 shows the results for UTK-Face gender and age classification for ViT-Base and Swin-v2-Large.
In all of these figures, we present the subgroup F1 score, accuracy, demographic parity difference (DPD), and equal opportunity difference (EOD) for each subset of the dataset. In the case of UTK-Face age classification, we only present the subgroup accuracy, as F1, DPD, and EOD are not directly applicable.
The results are consistent with the main results described in $3.2:
By worst group performance, best-worst group performance spread, demographic parity difference (DPD), and equal opportunity difference (EOD), Llama-2 7B and Mistral 7B exhibit similar fairness performance across the different subsets.
In most cases, LoRA does not worsen either the DPD the EOD.
The fairness assessment of the fine-tuning methods can be sensitive to the choice of the metrics.
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Figure A1: Classification fine-tuning results for Llama-2 and Mistral on the Berkeley D-Lab hatespeech gender subset. Rows from top to bottom: model Ilama-2 7B to Mistral 7B. Columns from left to right: subgroup F1 score, accuracy, DPD, and FOD. See $3.2 and Appendix D.1 for more details.
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Figure A2: Classification fine-tuning results for Llama-2 7B and Mistral on the Berkeley D-Lab hatespeech race subset. Rows from top to bottom: model Llama-2 7B to Mistral 7B, Columns from left to right: subgroup Fl score, accuracy, DPD, and EOD. See $3.2 and Appendix D.1 for more details.
Liara-2-1 Lub Hatsepascy piet
Mamra Daa Hakopeach (ukglon)
Matral BLU Origen
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Figure A3: Classification fine-tuning results for LIama-2 and Mistral on the Berkeley D-Lab hatespeech religion subset. Rows from top to bottom: model L.aam-2 7B to Mistral 7B. Columns from left to right: subgroup Fl score, accuracy, DPD, and EOD. See $3.2 and Appendix D.1 for more details.
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Figure A4: Classification fine-tuning results for Llama-2 7B and Mistral 7B on the Berkeley D-Lab hatespeech sexuality subset. Rows from top to bottom: model Llama-2 7B to Mistral 7B. Columns from left to right: subgroup Fl score, accuracy, DPD, and EOD. See 53.2 and Appendix D.1 for more details.
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VTO en NoE (pender
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Figure A5: Classification fine-tuning results for ViT-Base and Swin-v2-Large on UTK-Face (gender and age classification). Top row: ViT-Base on gender classification; metrics are subgroup Fl score, gender classification with the same metrics.
accuracy, DPD, and EOD. Middle row: Swin-v2-Larg Bottom row: Subgroup accuracy of ViT-Base and Swin-v2-Large age classification. See $3.2 and Appendix D.1 for more details.
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D.2 Calibration
Figs. A6 to A9 show the calibration results (i.e., confidence diagrams and reliability diagrams) for Llama-2 7B and Mistral 7B across all Berkeley D-Laa hatespeech subsets, and for Mistral 7B's highest ECE subgroups within each subset. Fig. A10 shows the calibration results for UTK-Faace gender classification for ViT-Base and SwinV2-Large.
The results are consistent with the main results described in $3.3:
LoRA and full fine-tuning show comparable and reasonable calibration levels. No significant differences are observed between the two fine-tuning methods.
LoRA tends towards overconfidence, with predictions clustering at extreme scales (bins 0.0-0.1 and 0.9-1.0), potentially affecting reliability across subgroups.
dtab eeel-gendeeaall To
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Figure A6: Confidence histograms (top) and reliability diagrams (bottom) for Llama-2 7B on D-Lab gender (left), Mistral on D-Lab gender (middle), and Mistral on subgroups with highest ECE within D-Lab gender (right). See $3.3 and Appendix D.2 for more details.
disb-race-a Hi ama2-7b 0.05m0004 lora, ECh: 04:0.94
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0,0
1.0
0.0
Confidancs
Confidenca
Corfdance
Corlilence
Corridance
Figure A7: Confidence histograms (top) and reliability diagrams (bottom) for Llama-2 7B on D-Lab race (left), Mistral 7B on D-Lab race (middle), and Mistral 7B on subgroups with highest ECE within D-Lab race (right). See $3.3 and Appendix D.2 for more details.
28

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Published as a conference paper at COLM 2024
diabreliionnal -mistral-7t
fu Houdahit ECE 08:0.04
orabutihis
Mll. ECE: 0.04x0.02 lora, ECE: 04220,04
ful, ECE: 0.05x5.02 loro, ECE 0.03m0.08
ECE. o 06:00.0c
1.0
Procton Sameko
0.4
0.4
1.0
1.0
0
0.0
0.0
0.0
Confflence
Ctefidenc
Confulonus
Confelence
Canfidenee
Corfidance
Figure A8: Confidence histograms (top) and reliability diagrams (bottom) for Llama-2 7B on D-Lab religion (left), Mistral 7B on D-Lab religion (middle), and Mistral 7B on subgroups with highest ECE within D-Lab religion (right). See $3.3 and Appendix D.2 for more details.
aalb-aezualiiaaa ama2-7b lora, 0320.01
fulketraight
lore-stra
Nl.
ful, 0.042..0
0.0420.04
0.0660.04
ECE: 0620.05
orechon Strmit
Aug Corf
Accusity
0.6-
0.c
0.0
0.2
00
08-
06-
0.
0.2
0.2
0.
0.0
2.5
2.0
0.0
5.5
1.5
0.0
1.0 0.0
2.5 Confidnnc
1.9
0.0
0.5
1.0
0.0
0.5
1.0
Confieence
Carfolonce
Figure A9: Confidence histograms (top) and reliability diagrams (bottom) for Llama-2 7B on D-Lab sexuality (left), Mistral 7B on D-Lab sexuality (middle), and Mistral 7B on subgroups with highest ECE within D-Lab sexuality (right). See $3.3 and Appendix D.2 for more details.
uikfice-pinderalloonnn
uikface-gunderal base ful, MCE: 0.015.0000000 lara, BCE 0.01m0.00
Nul. M.E.EE 0.01=0.00 lora, ECE 0 01:0,00
BCE 03:0.02
ECE: 0.300.000
tradtio Sammpte
of tample
Recben Sempoo
Aeg Cort
04
oz
1.
0.8
Figure A10: Confidence histograms (top) and reliability diagrams (bottom) for vit-base on UTK-Face gender classification (left), swinv2-large UTK-Face gender classification (middle), and swinv2-large on subgroups with highest ECE within different races (right). See $3.3 and Appendix D.2 for more details.
29

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Published as a conference paper at COLM 2024
D.3 Resistance to Membership Inference Attacks (MIA)
Figs. 5 and A1l show Likelihood Ratio Attack (LiRA) Llama-2 7B and Mistral 7B for membership inference on the D-Lab religion subset. Figs. and A12 show LiRA on ViT-Base and Swin-v2-Large for membership inference UTK-Face gender classification.
We also present the LOSS attack results in Figs. A13 to A16. See Appendix A.3 for back- ground the definitions and implementations of membership inference attacks.
on Mistral
buddhist
christian
hindu
10a
10"
10
10'
10
True Poskive Rate
10-1
10-1
LoRA
LofA
10-
LoRA
10
LofA
10-1
10-1
10-2
Jewish
muslim
other
10a
10a
30
LoRA
10
LoRA
10
LoRA
10
LoRA
10
LoRA
Fill
Full
Full
Full
Pull
10-1
10:4
10
10:4
10
10-4
10-1
10-h
10-1
100
10-4
10-4
10 10-4
10
106
10-1
10-4
10
10-4
10-9
10
Palse Positive Rate
Figure All: Likelihood Ratio Attack (LiRA) on Mistral 7B for membership inference on the D-Lab religion subset. Results indicate that the full fine-tuned model is slightly more resistant to membership inference than LoRA fine-tuning.
URA Attack Vit-Base
white
black
10a
10a
10"
True Positive Rate
10-4
Full
LoRA Full
10a
others
10-1
10-1
10e
10
10a
10
10
LoRA
Indian
107
10P
10
10
10a
Rull
Full
Full
10
10
10-4
10-3
10-3
10-1
100
10-4
10-2
10a
10-4
10-1
Figure A12: Likelihood Ratio Attack (LiRA) ViT-Base for membership inference on UTK--aae gender classification. Results indicate that the LoRA fine-tuned model is roughly equally resistant to membership inference compared full fine-tuning.
Lass Attack on Lama-22 2 78
atheist
buddhist
christian
hindu
100
10P
15P
10P
10a
Troe Positive Rate
10-1
10-4
LoRA
10-2
LoRA
10-1
LoRA
10*4
LoRA
10-4
30
Jewish
muslim
other
10a
10a
30-4
LoRA
10-2
LoFA
10
LoRA
10-2
LoRA
10-2
LofA
Fill
Full
Full
Full
Full
20-4
10-4
10
10-4
10
10-4
10-0
1002
1004
10f
10-4
10-4
100
10-4
10t4
106
10-1
10"4
100
10-4
10-4
106
False Positive Rate
Figure A13: LOSS attack on Llama-2 7B for membership inference the D-Lab religion subset. Results indicate that the LoRA fine-tumed model is slightly more resistant membership inference compared to full fine-tuning.
30

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Published as a conference paper at COLM 2024
10
10'
10a
10"
10a
True Positiv Rate
10-1
10-1
LoRA
10-1
LoRA
10-1
LoRA
10
LoRA
10-1
10-2
Jewish
muslim
other
10a
107
30"4
LoRA
10a4
LoRA
16
LoRA
10a4
LoRA
10
LoRA
Fill
Full
Full
Full
Pull
10-4
10a4
10
10*4
10
10-4
10-0
10ah
10-1
10f
10-4
10-4
100
10
10
106
10
10"2
100
10-4
10a
106
False Positive Rate
Figure A14: LOSS attack Mistral 7B for membership inference the D-Lab religion subset. Results indicate that the LoRA fine-tuned model roughly equally resistant membership inference compared to full fine-tuning.
Loss Attack Vit-Base
white
black
10a
107
109
True Positive Rate
10-4
Full
Full
LoRA
109
others
10
10a
10
10
10"
10-4
Indian
10-2
LoRA
10P
10P
10-4
10
10
10"
10'
LoRA
Full
Full
Full
10-4
10-1
20-4
10-4
10-2
10-4
10-4
10t2
10
10-4
10a
False Positive Rate
Figure A15: LOSS attack on ViT-Base for membership inference on UTK-Face gender classification. Results indicate that the LoRA fine-tuned model is roughly equally resistant to membership inference compared full fine-tuning.
Loss Attack on Swin-v2 Swin-v2-Large
white
black
True Positive Rate
10-3
10
LoRA
10
LoRA
100
10-1
10-1
10
10
10a
10
10:
10a
10-
10-2
asian
Indian
Rull
10-2
LoRA
10-2
10-2
LoRA
10-4
10-4
20-4
10-4
10-3
10-3
10"1
10a
10-4
10-2
10
10-4
10-2
10a
Positive
Figure A16: LOSS attack on Swin-v2-Large for membership inference on UTK-Face gender classi- fication. Results indicate that the LoRA fine-tuned model is roughly equally resistant to membership inference compared to full fine-tuning.
31

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Published as a conference paper at COLM 2024
D.4 Effect of LoRA Rank
Recall from Section 3.6 that we evaluate the effect of LoRA ranks on the fairness of the fine- tuned models. Fig. A17 presents the results for Llama-2 7B on all Berkeley D-Lab hatespeech subsets, Fig. A18 presents the results for ViT-Base on UTK-Face gender classification, and Fig. A19 presents gender stereotype results for Llama-2 7B on Turkish to English machine translation. Following the main discussions in Section 3.6, we found that the choice of rank tends to have little effect on the fairness of the fine-tuned models.
0413
086
0200
wi und
subsets (Gender, Figure A17: Effect of LoRA ranks on Llama-2 7B on all Berkeley D-Lab hatespeech subsets
Race, Religion, Sexuality). Rows from top to bottom: D-Lab subsets. Columns from left to right: subgroup accuracy, DPD, and FOD across rank values from 0 to 4096.
6ar-
020
Figure A18: Effect of LoRA ranks on ViT-Base on UTK-Face gender classification. Left to right: subgroup accuracy, DPD, and EOD across rank values from 0 to 768.
Balanced eval examples
St-eeetreyypp Biased Train Set, examples 318
60
0.75
60
Rato
Ratio
pro. storoopp
0.2
0.25
antl stareotype
20
pro_storaoypp
and Pereatyse
256
512
1024
2040
4096
256
512
1024
2043
4096
Ilama2-7o Rank
Ilaa2-77 Rank
Figure A19: Effect of LoRA ranks on Llama-2 7B on Turkish to English machine translation. Left to right: gender stereotype results on the gender-umbiased dataset and pro-stereotypical gendered dataset across rank values from 1 to 4096.
32

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Published as a conference paper at COLM 2024
D.5 Effect of Subgroup Size
Fig. A20 illustrates the effect of increasing group size utility (e.g,, accuracy in the plots) and fairness (DPD and EOD). The results support $3.7 that these metrics are not solely dependent on the size of the subgroups.
On the UTK-Face gender dataset with vision models, while there a general trend of increasing accuracy with larger group sizes, the practical impact of group size accuracy is limited, since the absolute difference in accuracy across these sizes is marginal.
VIT-Base UTK-face (pender)
VIT-Base on FTic (gender)
0.50
3.10
Pnstune
M1
5.07
Inc.ue
3.00
5.09
0.55
5.05
5.04
0.5
3.04
0.02
1.0
5.00
Ocion (31)
(872)
Inkien (ali)
Hoie 33
Other (965)
Atae (072)
Intiie (902)
Backk (180)
Weste (2073)
Obeert Ord)
4tann 1922)
balie Boz)
Back (no
Wedte (013))
Grous
SwinV/-Large on UTiiPaae (gender)
Swinkk-LLarge UTKK see (gender)
SwiniV2 Large on FTce (punder)
indurr
2.00
Insture
k1
5.08
0.06
Anmune
2.00
0.66
0.54
0.55
0.62
2.02
0.00
(35)
(022)
(802)
(989)
Wiite (2073)
(185)
Aninn (73)
Inton (803)
Biu (159)
White (2073)
Ofhers (165)
4sin (622)
Inton (90))
Back (88))
Whts (2933)
Groug
Shouss
Figure A20: Accuracy and fairness on UTK-Face gender classification with subgroups sorted by size.
33

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Published as a conference paper at COLM 2024
D.6 Gender Bias
Yelp Review Language Modeling Recall $3.1 for the task setup, Appendix C.3 for the prompt templates used for the evaluations, $3.5 for results on cloze completions, and $4 that also explore the effects of model token bias on the generative evaluations.
We present the results for Llama-2 7B and Mistral 7B on the subsampled Yelp review dataset. For the two models respectively (tables are in Appendix D.6.1 and Appendix D.6.2):
Table A2 and Table A3 show the results for YN* prompts.
Table A4 and Table A5 show the results for MC* prompts.
Table A6 and Table A7 show the results for *-specal prompts.
Table A8 and Table A9 show the results for cloze prompts.
In these tables, the text "ratio-()" in the metric field measures the percentage of the 50k Yelp reviews, given the specific prompt template, the model selected that choice. There is slight difference between the metrics for YN* prompts and MC* prompts. For YN* prompts, the metric "ratio (genderggg -(choceec means the ratio model answers "[choice)" when asking specifically whether the reviewer "(gender)". For MC* prompts, the metric "ratio [token) means the ratio of the reviews the model selects "(token)" The value is bold if either greater than 99% or less than 1%, showing a strong preference towards one answer.
The results are consistent and the model token bias is clear:
LoRA does not exhibit more bias than full-model fine-tuning.
On res/no and multiple choice QA, both Llama-2 7B and Mistral 7B models exhibit strong biases, often preferring specific responses like the token "A" regardless context (where selection rates for that token exceed 99% indicated in the tables), which complicates fairness in evaluations.
Attempts to reduce these biases by changing prompts or varying tokens used in the multiple-choice options have been ineffective, suggesting such biases are ingrained and not easily correctable.
Turkish to English Machine Translation Recall $3.1 for the task setup and $3.5 for results of gender stereotypes. In addition, Fig. A21 shows results on Llama-2 7B and Mistral 7B for gender predispositions. Pro-stereotypical broken down into male-predisposse and demale-predisposee sentences depending on the context. For example, the sentence "the developer argued with the designer because [he] did not like the design" is pro-stereotypical and male male-predisposed since people usually fill in the male-gendered pronoun given the context, while the sentence "the developer argued with the designer because her idea cannot be implemented" is pro-stereotypica and (emale-predisposer
anti-stereotypical sentences can be
The results are consistent with the main results described in 53.5:
On the gender-unbiased training set, both Llama-2 7B and Mistral 7B show fair gender- unbiased behavior, regardless of the fine-tuning methods.
On the pro-stereotypical gendered training set, the bias depends heavily on the model architecture: Mistral 7B shows significant gender bias regardless of the fine-tuning method, while Llama-2 7B shows less gender bias when fine-tuned using LoRA.
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0.7
0.6
0.5
0.4
92-
0.3
0.4
248
242 poo.t 2914 JusiA www.irrstumm 11
Figure A21: Model gender-predispositoons for Llama-2 7 and Mistral 7B both balanced and pro-stereotypica gendered training sets. The training and evaluation sets are the same as Fig. 7 and only the metric differs. The x-axis label (gender1) predispose (gender2): (genderl) refers the gender that people usually predispose the pronoun to be given the context in the English sentence. (gender2) refers to the gender pronoun that model traes--ranslated English sentence contains. E.g., "the developer argued with the designer because she did not like the design" (where "she" refers to "developer") is (male)-predispe)) www.eiipoosee((eeee (female) sentence, Ideally, the model should translate similar ratios (50%/50%) of male weteeepredipsseeeeeee female and male. www.eeeredsseeeemeeee male sentences, the same for female prenileepreispsseee.. female and female_pree meenalepredispoe...e male sentences.
male
35

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Published as a conference paper at COLM 2024
D.6.1
Additional Results for Yelp Review Language Modeling: Yes/No and Multiple Choice
Prompt ID
Metric
Full
LoRA-1256
Chal
LoRA-16
Pretrain
Full
LeRA-256
Raw
LoRA-I8
Pretrain
YN1
29.74%
352890
18.22
17%
3120%
99.69%
9879%
37.38%
38220%
23.57%
238875
B950%
48.52%
YNi-numeric
ratioumale,
lemale yes
1300%
YN1-mumrri--mveetted
ratlo-malc.je yes
99.82%
0.00%
35.18%
98.90%
99.35%
382%
88.79%
0.75%
Fillo Jemalee Jes
99.85%
0.00%5
10.20%
99.30%
99.0%%
5712%
850015
3388
YN2
fetic yeyes
Falio maalimallcaa ves
99.9%%
90.75
0.15%
0.15%
0.0%
0.01%
99.83%
99.75%
100.00%
100.00%
1.50%
100%%
17.64%
18.32%
99.96%
99.97%
YNZ-inverted
Fallo mullimallc.pa yes
77.37%
701915
0.207
0.50%
0.00%
0000'S
7555%
287%
9590N
9554%
0.74%
0.27
0.02%
0.07%
0015
0.10%
YNZ-numeric
100.0%
49 17%
46.42%
100.00%
1725%
2.44%
2.57%
gender neuttral
98.04%
25.81%
YAd-numeric
rallo gerder soultal.yos Ves
TORDO'
10000'
0.01%
YN3-aumerk- inveried
talio gender nectral ves
0.00%
17.39%
98.36%
4,28%
0.00%
30.2%%
99.99%
0.00%
YN4
strppsee pocl_/ollllleeoooo male yes
98.4%%
1.775
0.0%%
99.99%.
100.0%
0.02%
93.20%
100.0%
rabo surprise not Jemale yes
90.85%
2,2%
000%
99.50%
100.00%
0.02%
92.0%%
100.0%
YN4-numeric
ralin surprise male
purpris female.
10000%
10000%
86.4%
87475
0.4%
0.4%%
96.34%
94.225
100.00%
100.0%
0.00%
0.00%
0.00%
0.00%
100,00%
10000%
YNS
fallo.igue,
yes
1.2%
99.89%
54.86%
YNS-numeric
aagio against yes
0.00%
rallo atgue against lemale yes
10000'S
Table A2: Evaluting Llama-2 7B fine-tuned on Yelp reviews on YN* prompts with "yes" and "no" as answer options. Bold values denote strong preference (> 99% or < 1%) towards an answer. See $3.1 for task set up, Table A1 for prompt templates, and $3.5 and Appendix D.6 for result analysis.
PromptID
Metric
Full
LoRA-1256
Chat
LoRA-18
Pretrain
Full
LoRA-226
Raw
LaRA-r6
Pretrain
YN1
7850 Jemaic yas
miho_male_yve yes
99.39%
90.0%%
99.73%
100.0%.
11.71%
5.45%
11.05%
99.85%
95-47%
11.10%
1322%
3011%
2.1%
99.46%
55100%
YNI-numeric
ratio fee
maloo male
100.00%
10000%
61.95%
05.22%
47.69%
42387%
70.48%
7008%
99.94%
99..7%
99.96%
99.33%
99.68%
99.35%
40.07%
VNN-aumerio-nnveeedd
saloolemale_yVe
10000%
10000%
98.58%
57.325
100.00%
96.33%
99.86%
maloo male yes
100.005
87.72%
7.9%%
99.96%
matio female yes
100.0%%
100.00%
60.62%
99.48%
100.00%
99.96%
YNZ-inverted
milo Jelalee yes
ratio male ves
20225
8.16%
97.29%
93.15%
338%%
4.38%
99.68%
95.1%%
100.00%
100.0%%
0.88%
0.4%%
6.1%
3,88%
98.33%
98.91%
YN2-numetic
rala Jemale yes
mttio_male.gg yes
10000%
10000%
99.07%
99.2%%
59,207
35.40%
100.0%
99.99%
100.00%
10000%
93.96%
98.27%
97.33%
97.8%
5800%
95.14%
YSB
ratie.unddrr neutral.yes
99.9%
100.00%
77.82%
97.19%
100.00%
99.98%
1409%
99.91%
YNS-mumeric
geldogeendw
10000%
80.48%
89.79%
100.00%
99.8%%
99.54%
Tallo_gender neuurr.
5.8%%
59.14%
0.06%
87.18%
YN4
Fañe surptise not Jemale yes
nutio,urrpiss jwc. mole yos
10000%
1000%
30.4%
16.3%
11.8%%
14.8%
100.00%
100.00
100.7%%
100.00%
1.9%
113%.
000%%
99.7%
9938%
YN4-numeric
ralio, surprise not male yos
iemaalleeeeeeeeee.mmoooomm attooaurprise.ne not Jemale yes
100.00%
1A000%
99.80%
99.77%
9.42%
5..%%
100.00%
100.00%
100.00%
10L00%
100.00%
100.0%
58.67%
43300%
99.96%
9937%
YNS
mnlp.rrpu.ppaiiss Jemale yes
milo againsl male
9930%
99.2%%
63.2%%
67.29%
17.45%
10.25%
25%
100.00%
37.58%
0.83%
0U12%
99.64%
19.69%
YNS-numeric
ralio arguu againsll yes
100L0%
100.00%
argae apainst yes
Table A3: Evaluting Mistral fine-tuned on Yelp reviews on YN* prompts with "yes" and "no" as answer options. Bold values denote strong preference (> 99% or < 1%) towards an answer. See $3.1 for task set up, Table A1 for prompt templates, and $3.5 and Appendix D.6 for result analysis.
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Published as a conference paper at COLM 2024
Prompt Label
Metric
Foll
LO
Fretaan
Foll
LoKA-SS
Feetrann
tokem ( ("A")
99.98%
25.66%
18.00%
100,00%
100.00%
2.37%
33.55%
10000%
MC1
rabo TokenZ ["D"
00225
24.35%
50.30%
0.00%
1.00%
0.17%
0.02%
U000%
tokens
('C)
0.00%
1.0%
23,00%
0.00%
0.00%
92.36%
66.83%
0.00%
811%
9787%
MCI-numeric
Tallo
0015
001%
0.1%
Jitinjokee3 ("3")
0.00%
188%
5.76%
0.00%
0.00%
91.3%
6.22%
0.00%
Killnjokenn [11
1108%
0.3%%
34.40%
35.34%
100.0%
1206%
71.24%
10007%
MCI-nnumrricaavvettedd
ratio.ikend2 [22"]
86.10%
98.52%
13.99%
61.37%
0.00%
0.00%
10.78%
0.00%
seo tokens ("5")
2355%
1.0%%
1.0%%
0.22%
0.00%
87.3%%
17.51%
0.07%
95.31%
5.05%
rallo
0011
ratio takeni ("A"
0.12%
4348%
33,29%
2.28%
95.27%
87.15%
96.20%
99.99%
MC2
folio.lolees ("C)
IalloJokee2 ["
0.00%
0.00%
5647%
0.04%
64.38%
1.88%
0.00%
0.00%
0.00%
0.00%
0.02%
2.66%
441%
0.04%
0.00%
0.00%
rafio_bokend ("D")
99.88%
0.01%
0.17%
96.76%
4.73%
10.37%
0.35%
0.01%
MC2-nameric
55,87%
Tallo
134%
001%
124%
0.51%
tokeed ("4")
98.32%
0.00%
1.10%
6.92%
1.52%
0.01%
0.08%
0.00%
ZolioJtaken ("A")
100.0%
99.11%
9974%%
99.95%
100.00%
1.88%
1.12%
100.00%
MC3
ralo Token2 ["I]"
000%
0.36%
1200%
0.05%
1.00%
241%
6.7%
000%%
tofio_token3 ("C*)
0.00%
0.00%
0.8%
0.00%
0.00%
95.7%%
92.50%
0.00%
95.09%
100.00%
MC3-inverted
rabo
fulees
0.0%
000%
0.4%
0.04%
0.07%
96.15.
99.26%
0.00%
ral0 takeni
50.0%0
96.45%
03.64%
85.00%
3.85%
00119%
0.00%
99.75
MCH-inverted-symmbbl
rabi token2 ["
934%
2.7%%
0.4%%
0.01%
96.12%
17.53%
75.83%
0.25%
Falio, Jokeens folio_ (*C
0.30%
035%
1.92%
11.39%
1000%
2.9%
23.33%
0000%
ratio token! ("1")
95.12%
84.99%
48.39%
19.00%
0.00%
0.00%
100.00%
MC3-numeric
(12))
0.0%%
1500%
79.06%
4.4%%
1.79%
85825
91.88%
41.3%
28.1%.
0.04%
0.00%
10000%
MC3-mumericcivertte
kitirjokenz [ [2)
000%
8.35%
55.50%
39.33%
0.07%
13.39%
0.30%
0.00%
Alooo tikens 3 1
11.8%
0.0%%
2.78%
11.96%
0.02%
86.58%
99.70%
0.00%
ratio A
2028
9938%
71.6%%
99.4%%
100.00%
99.59%
0.30%
100.0%%
MC4
0.01%
0.0%%
3339%
0.01%
Joken
0.00%
MC4enameric
nollo.nkee22 ("2"
572%
0.02%
7.89%
0.31%
0.00%
0.00%
0.09%
0.00%
nitinJokke3 ["3"]
1861%
0.00%
18.8%
6.90%
0.00%
69.92%
99.91%
0.00%
Jaken (*A
061%
9939%%
1.15%
97.50%
10000%%
210%
1.16%
1000%%
MC5
0.00%
0.1%%
99.27%
0.20%
0.00%
181%
0.45%
0.00%
0.00%
96.10%
55.30%
0.00%
MCS-nameric
tokeo-
("2")
0.00%
99.17%
0.4%%
0.00%
0.00%
70.40%
0.99%
0.00%
Alllo Aillo Jokens 1
00005
0.07%
50.0%
0.00%
0.00%
20.0%
99.01%
0000%
Table A4: Evaluting Llama-2 7B fine-tuned on Yelp reviews on MC prompts with multiple choices answer options where symbols are sets of "ABCD" "1234". Bold values denote strong preference (> 99% < 1%) towards answer. See $3.1 for task up, Table A1 for prompt templates, and $3.5 and Appendix D.6 for result analysis.
37

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Published as a conference paper at COLM 2024
Prompt Label
LORA-1256
LORA.I
LoRA-1256
Pietrain
rallo, ralio_ioles Joleen ("A")
95.9%%
99.99%
95645
54.0%%
100.0%
341%
1941%
99.61%
MC1
token2 ("B")
0.00%
0.00%
1.78%
37.60%
0.00%
7.0%
5.50%
0.31%
Jolem 3
0201%
0015
438%
7.25%
0.0%%
88411
7640%%
0.05%
99.55%
22.7%%
12905
6247%
100005
72.22%
MCI-numeric
fokeo.toke22
62.84%
25.79%
raba lowens
rabo tokenl ['1"
100.00%
121%
71.22%
83.42%
90.6%
100.00%
7.68%
99.89%
MCI-mnmmeric-nnverttee
rabo.tokem2 2-2
0.00%
72.8%
5.67%
14.35%
0.01%
000%
70.6%
0.10%
rabi Ioacnd ["3
0.00%
2290%
25:1175
2.24%
5.40%
000%
21.50%
0.11%
rabio token1 ('1"
0.00%
38.19%
55.12%
70.00%
0.00%
99.72%
56.44%
96.65%
MCI-aumerte -a--umrii-nnverrddseymbib
47.13%
16.85
0.00%
000%
30.35%
ralio.lolonn ["3")
ralio_toleenl ("A")
99.89%
93.36%
60
1.07%
79.8%%
28%
5,86%
99.59%
MK2
rabi bokem? ["C
KollnJoken2
0.01%
0.07%
0.2%
6.62%
33.99%
428%
16.90%
141%
2012%
0.04%
20.38%
448%
28.96%
1.59%
0.01%
0.38%
folkenk ('])'
0.03%
000%
031%
80.3%%
0.00%
38,8%
60A3%
0.01%
0.10%
100.0%%
95.45
70.35%
MC2-numeric
rabio
531%
ralio lowons
0.00
fatio_tokemé
30.0%%
0.00%
0.00%
0.00%
0.17%
Ialio_[olem] A*A
100,0%
99.0%
96005
43.71%
98.75%
2005
7.34%
78.6PM
MC3
Fallo Fallo Jokenz ("D*)
0.00%
0.00%
2.74%
53.25%
1.2455
6.22%
40.94%
20.27%
notio.tokem3 ("C")
0.00%
100%
1.2%%
2.58%
0.00%
91.2%
51.11%
1.13%
99.335
2.57%
MCI-inverted
0000%
32.59%
nobo.tokem?
0.01%
0.14%
611%
11.6%
0.00%
9020%
69.2%
0.56%
Iallo_olomn] [ [*A
5.17%
90.1%
92365
90.25%
0.00%
9'625
46.10%
53.27%
MCS-inverted-ymbbl
ralio takenz D
9182%
000%
2.38%
9.51%
100.00%
90.18%
51.92%
16.17%
rali, Token! ["C
0.01%
0195%
5087
0.14%
0.00%
020%%
1.8%%
1.26%
ralio token1
100.00%
13.53%
87.91%
62.21%
99.99%
99.97%
94.1%
99.02%
MC3-ummrric
falio_lowen
0.38%
0.01%
1.11%
0.26%
ralia loken1
100.0%
34.8%
76.995
63.27%
99.9%%
99.99%
99.60%
98.98%
MC3-numerriciveette
releo_t 1*2")
0.00%
23%
9.94%
27.97%
0.01%
0.1%%
0.17%
0.76%
rallo Iolen1 ["3")
0.00%
8.9%
13475
8.76%
0.02%
100%
0.23%
0.26%
100.00%
94.1%
76.3%%
95.39%
92.37%
27.51%
MC4
0.42%
Tabo
5188
lokem
100.00%
53.06%
98.55%
99.36%
100.00%
99.15
87.1%
99.91%
nCt-nim
rali Josenz
0.00%
44.0%%
1.30%
0.64%
0.00%
038%
6.35%
0.05%
rabi token3 ("3")
0.00%
0.16%
0.30%
0.00%
0.00%
0.00%
5.68%
0.05%
folsel A*A
99.99%
65.2%
1922%
99.36%
99.95%
92.15%
0.10%
99.86%
MCS
Joken2
0.00%
4.1%
0.55%
85.34%
("C*)
47.6%%
0.02%
14.8%
ralio,toieml [']")
1387%
MC3-numeric
falia, Jooon2
0.00%
5391%
35.33%
8.07%
0.00%
0000%S
42590%
0.06%
raboo.tukem3 ("3") 0.00%
35.85%
60.17%
0.08%
0.00%
0.00%
43.25%
0.17%
Table A5: Evaluting Mistral 7B fine-tuned on Yelp reviews on MC prompts with multiple choices answer options where symbols sets of "ABCD" "1234". Bold values denote strong preference (> 99% or < 1%) towards an answer. See $3.1 for task set up, Table Al for prompt templates, and $3.5 and Appendix D.6 for result analysis.
38

---

Published as a conference paper at COLM 2024
Prompt]D
Metric
Chat
Raw
Fuil
LokAvr250
Pretaai
Full
Logde-250
LakA-IS
Fretrain
YNl-special
ratiomale.y ("0")
100.00%
60.21%
1.01%
99.99%
100.00%
0.62%
11.00
100.0%%
y
10000%
68.29%
100.0%%
13.35
10007%
YNI-ppcia/iivertee
rabo.
rabi make y
20075
7.00%
0.82%
1.22%
99.97%
75.45%
0.0%%
000'5
9000%
99.53'5
99.69%
0.00%
U200's
aalio_ ("
99.54%
9.13%
5.235
100.00%
93395
820055
0.48%
99.88%
rabio female y ('(")
97.6%%
11.09%
5,5%%
99.99%
91.39%
64.51%
0.45%
99.69%
specipe
Jemally
rabe..male.y ('0")
100.00%
10000%%
287%
4.31%
23.59%
28.195
100.00%
100.00%
100.00%
100.00%
99.92%
99333%
0.33%
0.14%
100.00%
10000'
YNZ-special-nveeeed
ratio.male.y (*)
0.00%
74.0%%
92334%
0.00%
0.00%
14,8%
99336%
Ietoolemalle. ("3")
0.00%
82.98%
89.21%
0.00%
0.00%
2042%
99.96%
0.00%
VNZ-pecilliinerrrr-ggymyy
myl ("
000%
0.76%
6.7%
0.01%
33935
10005
58.8%
0.08%
Jebaleey ['
0.00%
0.87%
7.28%
0.00%
41.04%
10000%
16.79%
0.19%
ratio token1
97.38%
88.95%
99.90%
99.69%
100.00%
98.04%
91.615
21.33%
MCI-special
ratio token?
2,42%
10.92%
0.10%
0.31%
0.00%
1.96%
8.3%%
78.65%
Jokers:
0.00%
0.33%
0.00%
0.00%
0.00%
0.00%
0.01%
0.00%
rallo Joken]
0.72%
82.29%
99.95%
7.94%
100.00%
73.52%
99.6%%
6.00%
MCC--ppedil-iiverredd
Joken2 (
99.28%
14.76%
0.06%
92.06%
0.00%
26.4%%
10.23%
94,00%
3olen
3.1%%
0.11%
ralio Jokenl
0.00%
75.42%
98.02%
0.00%
0.00%
99.88%
7.39%
0.00%
MC1 lepperia-iiveeetdsammbool
Joken2 (
98%
24.43%
1.88%
94.23%
0.00%
0.12%
83.15%
34.13%
ratio 3oken
4.02%
0.18%
0.00%
5.77%
100.00%
0.00%
9.70%
65,87%
ratio token! (
13.29%
83.20%
99.99%
11,85%
86.67%
99.99%
3.25%
0.5%%
MCI-special
ratio
16.30%
0.00%
70.64%
6.4%
6.28%
98.66%
ratio Joken3
81.36%
0.00%
0.00%
17.54%
6.99%
0.01%
90.17%
0.59%
rallo
11.12%
54.13%
99.94%
4.07%
53.4%
99.99%
2.70%
0.02%
MCD-specal-lnvertee
Jolen (
2,34%
45,87%
0.01%
95.00%
43.90%
0.00%
12.68%
99.88%
Joken3 (
85.54%
0.00%
0.00%
0.93%
0.46%
0.01%
84.65%
0.00%
Joken!
0.00%
43.99%
100.00%
0.00%
0.00%
96.05%
4.9%%
0.00%
Joken? (
3.78%
56.11%
0.00%
99.99%
9.90%
0.00%
20.53%
99.96%
ratio
0.30%
0.00%
90.10%
71.88%
Table A6: Evaluting Llama-2 7B fine-tuned on Yelp reviews on *-special prompts with special symbols as answer options. Bold values denote strong preference (>> 99% or < 1%) towards an answer. See $3.1 for task set up, Table Al for prompt templates, and $3.5 and Appendix D.6 for result analysis.
Prompt ID)
Metric
Full
LoRA--256
Chat
LORA-FF
Pietrain
Full
LORA-2250
Row
LORA--E
Pretratin
YNI-special
ratiommaley ("(")
10020%
20.45%
66.99%
99.70%
0.18%
44.32%
99.39%
99.59%
10000%
73.3%
0.05%
99.73%
-Nl-spccill-nveettd
male.y ("
32.56%
36.07%
99.66%
0.76%
1.5%%
rall Jemalcc.y ("'")
0.07%
3702%%
24.00%
90.44%
0.89%
258%
ratio male y ("(')
100.00%
8.78%
93.24%
15.59%
100.00%
98.50%
90.14%
48.81%
remolleemlle.y ['
100.00%
18.70%
95.17%
12338
100.00%
58.65%
85 15.
50.21%
YN2-special
febo..emale..y
ralio, male.y ("(
100.00%
100.00%
92.01%
91.14%
34.24%
50.91%
100.00%
100.00%
0.00%
0.00%
99.41%
99.55%
100.00%
99.99%
99.59%
99.57%
YN2-specialdiveereed
ratio.male.y
27.76%
53.97%
1.19%
100.00%
1.27%
0.07%
milo, lemally )"
21.27%
61.38%
0.55%
99.99%
0.32%
0.05%
1.02%
122--peial-mnveriddsssmmb
ratio.male.y ("("))
94.25%
82.5%%
99.69%
71.93%
98.32%
1.12%
98.13%
0.82%
mttoolemmle.y! y ['
98.69%
90.50%
99.84%
20.86%
97.96%
1.23%
99.73%
0.50%
Ioleol
94.04%
64.53%
45 35.88%
0.76%
60.46%
85.8%
3.10%
MCI-special
ratio token2 (
5.89%
35.46%
37.48%
16%40%
38.15%
6.24%
12.79%
0.44%
3uken
0.11%
0.01%
16.60%
8.8
1.39%
7.96%
84.15%
77.33%
ratio token]
73.70%
59.88%
16.91%
11.29%
52.15%
98.96%
1.69%
32.12%
MCI-spesia--ivveeteed
ralio Joken2
26.15%
40.51%
76.44%
2.25%
27.81%
0.42%
10.19%
0.38%
Joken_
0.09%
0.00%
6.63%
86.47%
20.04%
85.32%
67.50%
ralio token]
0.00%
46.52%
7.0%%
0.19%
8.86%
0.00%
0.72%
0.00%
MC1 seypeeccal-neeeeellyhnlllllllll
ralio Joken2 ("
0.00%
53.36%
79.93%
6.46%
23.29%
0.06%
6.54%
0.13%
3oken (
100.00%
0.12%
12.18%
93.35%
67.85%
99.94%
92.74%
99.87%
rallo, Joken] (
37.88%
5.78%
32.03%
4.23%
3.38%
86.12%
7.86%
0.16%
MC3-special
rolon2
94.01%
51.67%
15.20%
96.43%
2.50%
ratio 3tkke3 (
12.12%
0.22%
16.37%
80.56%
0.24%
0.04%
89.64%
99.66%
ratio token]
86.42%
14.49%
24.87%
3.24%
41.97%
80.40%
10.44%
0.28%
MC--specialliveetted
tokem (
0.00%%
84.62%
20.29%
9.82%
49.50%
19.55%
1.21%
0.37%
ratio token3
13.58%
0.89%
54.86%
36.95%
8.58%
0.05%
88.35%
99.35%
ralio Joken]
0.00%
24.33%
68.94%
0.00%
311%
0.00%
0.09%
0.00%
MC--ppesalavvettd-symbblll
Jalio_
0.00%
74.58%
22.72%
9.44%
63.01%
0.52%
3.16%
0.27%
3
8.34%
33.88%
Table A7: Evaluting Mistral 7B fine-tuned on Yelp reviews on *-special prompts with special symbols as answer options. Bold values denote strong preference (> 99% or < 1%) towards an answer. See 13.1 for task set up, Table A1 for prompt templates, and 53.5 and Appendix D.6 for result analysis.
39

---

Published as a conference paper at COLM 2024
D.6.2 Additional Results for Yelp Review Language Modeling: Cloze Completions
Chat
Raw
Prompt Label
Metric
Full
LoRA-1256
LoRA-r250
LoRA-18
Clozel
rañio male
40.39%
48.95%
17.54%
14.42%
13.28%
54.25%
66.21%
2.46%
Cloze2
male
52.96%
56.68%
61.29%
19.91%
12.81%
60.83%
82.23%
15.95%
Clozes
ratio male
19.19%
53.61%
12.99%
4.09%
10.37%
62.79%
55.03%
5.29%
Cloze4
ratio_male
99.24%
98.91%
82.21%
95.88%
97.29%
38.02%
13.04%
49.30%
Clozes
rabo_malk
87.94%
83.39%
13.35%
12.94%
65.98%%
18.08%
39.64%
0.46%
Table A8: Evaluting Llama-2 fine-tuned on Yelp reviews on cloze prompts with "male" or "female" as answer options. Bold values denote strong preference (> 99% or < 1%) towards an answer. See $3.1 for task set up, Table Al for prompt templates, and $3.5 and Appendix D.6 for result analysis.
Chat
Raw
Prompt I label
Metric
Full
LoRA-1256
LoRA-18
Pretrain
Full
LoRA-1256
LoRA-18
Pretrain
Clozel Cloze2
mateo_male
33.44%
27.51%
54.50%
16.51%
20.56%
10.71%
47.41%
5.72%
ratio_male
45.84%
6.46%
33.24%
20.91%
8.34%
18.24%.
54.39%.
5.48%
Cloze3
ratio male
8.61%
54.77%
29.00%
8.44%
16.24%
5.94%
38. 19%
1.14%
Clozed
69.64%
43.83%
49.85%
27.86%
77.93%
33.55%
73.56%
6.97%
Clozes
rañio male
6.36%
2,49%
25.19%
188%
43.60%
7.05%
27.28%
2.43%
Table A9: Evaluting Mistral fine-tuned on Yelp reviews on cloze prompts with "male" or "female" as answer options. Bold values denote strong preference (> 99% or <, 1%) towards an answer. See $3.1 for task set up, Table Al for prompt templates, and $3.5 and Appendix D.6 for result analysis.
40


Reviews:
[{"review_id": "QYJUtFdA6X", "reviewer": "Reviewer_Hiqr", "summary": "This paper shows empirical evaluations of the LoRA model on various downstream tasks by considering fairness across different sub-groups. The experimental results are informative and the conclusion that “LoRA rank has little impact on subgroup fairness” is interesting.", "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": "We thank the reviewer for their time and are glad that the reviewer finds our work valuable! Trustworthiness aspects (such as fairness) of popular algorithms are often overlooked by practitioners and we believe a thorough empirical investigation can help inform the community and future applications.", "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": "A detailed and comprehensive evaluation", "reasons_to_reject": "For a draft focusing on evaluation, I think the included evaluation is sufficient.", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "S2e91eHiCa", "reviewer": "Reviewer_yDCp", "summary": "This paper studies the impact of parameter-efficient fine-tuning on the fairness of downstream tasks. The paper compares their experimental results on various settings, including text/image classification, translation, and generation tasks. They compare their results with the full-finetuning setup. The paper notes that PEFT-based fine-tuning using LORA achieves comparable performance to full fine-tuning and does not consistently excavate the unfairness of the decisions.  The paper also performs similar experiments to evaluate the calibration of the decisions and effectiveness against MIA attacks.", "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 the thoughtful review! We respond to the main points below:\n\n> The paper's premise is unclear, why should LORA worsen the fairness of the model decisions?\n\n- We’d like to clarify that we in fact did *not* begin with a premise that LoRA would worsen fairness (indeed, it may even improve it as seen in Sec 3.2). Rather, we believe the impact on fairness is *unclear* from prior studies. This lack of clarity, together with LoRA’s wide adoption, may imply unintended and harmful consequences in high-stakes applications and has motivated our thorough investigation.\n- Our study of LoRA fairness was also motivated by the disparate impact of pruning [1] and differential privacy [2] on subgroup fairness. A common thread is that both pruning and DP reduce a model’s *fitting capacity*, which [3] argues can lead to disproportionate effects on minority data. Since LoRA also sacrifices fitting capacity for efficiency, it is natural to ask whether a similar impact exists.\n\n> Shouldn't it just be a function of the underlying data that is being used? With this logic, shouldn't the experimental results be expected (or even obvious)?\n- We respectfully disagree that fairness is always solely a simple function of data; indeed, DP training is a counter-example where the learning algorithm impacts fairness, despite the same data. We also visualized the fairness impact of subgroup sizes in **Appendix D.5**, where subgroups with fewer examples (a common factor believed to tie to fairness) do not necessarily experience worse fairness. \n- Generally, expecting results does not imply that such results were previously known. We believe a thorough study confirming (or disproving) intuitions is useful to the community.\n\n> study the impact of other PEFT methods like adaptors, IA3, etc.\n- We aim to be comprehensive on one popular method rather than to spread efforts across multiple methods. Indeed, recent work such as https://arxiv.org/pdf/2405.09673 suggests that LoRA’s wide-spread use warrants a focused study.\n- Nevertheless, we acknowledge this important future work in concluding remarks. \n\nWe hope to make these discussions clearer in the updated version!\n\n[1] Tran et al., “Pruning has a disparate impact on model accuracy.” NeurIPS 2022.\n\n[2] Bagdasaryan et al., \"Differential privacy has disparate impact on model accuracy.\" NeurIPS 2019.\n\n[3] Feldman & Zhang, \"What neural networks memorize and why: Discovering the long tail via influence estimation.\" NeurIPS 2020.", "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 follow.\n2. The paper evaluates an important safety aspect of the widely used PEFT technique. \n3. The paper provides an extensive set of experiments to test the hypothesis of whether PEFT-based fine-tuning negatively impacts the fairness, calibration, or privacy leakage of the resultant model.", "reasons_to_reject": "1. The paper's premise is unclear, why should LORA worsen the fairness of the model decisions? LoRA is meant as a way to approximate full fine-tuning and with higher LORA ranks we should be able to get the exact behaviour of full-finetuning. With lower ranks, it should underfit the data -- it is not obvious to me why that should worsen the fairness? Shouldn't it just be a function of the underlying data that is being used? With this logic, shouldn't the experimental results be expected (or even obvious)? A discussion around this would be helpful.\n2.  Although the paper limits itself to understanding the fairness impact of low-rank PEFT methods, it would be interesting to study the impact of other PEFT methods like adaptors, IA3, etc.", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ersuxv7eXJ", "reviewer": "Reviewer_tTvn", "summary": "The paper aims to investigate the relationship between Low-Rank adaptation and the fairness of language models. Fairness is an extremely important factor in a language model, especially if these language models are used for important decisions.", "questions": "", "limitations": "", "rating": 3, "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 raising interesting questions! However, the reviewer’s comments suggest a mischaracterization of our work and we hope to clarify below:\n\n> Let's use a metaphor\n\nThe logic of the metaphor is as follows:\n1. LoRA acts indiscriminately on “all the model parameters”;\n2. Hence, its side effects (if any) are also applied indiscriminately to all aspects of model behaviors, not just fairness (“blurs the whole picture”);\n3. Hence, why do the authors (only) study fairness and why “this low-ranking should act only on fairness”?\n\nUnfortunately, this metaphor has critical flaws.\n1. While true that LoRA is broadly applicable, it is unclear from prior work that “it has effects on every aspect of LMs”: many aspects like robustness (OOD or adversarial), privacy, and fairness are unexplored against full fine-tuning, and our work is one of the first to provide such a study. \n2. We’re unaware of evidence for the reviewer’s claim that LoRA “has effects on every aspect of LMs” *just because* it applies to “all the parameters”. \n3. Even if LoRA *may* have “effects on every aspect of LMs”, it does *not* mean that “this low-ranking should act only on fairness” and we made no such claim.\n\n> Provide a good reason why LoRA should have any influence on fairness\n\n(To avoid duplication, please also see our response to reviewer yDCp.)\n\nWe emphasize that our main motivation is precisely that the impact on fairness is *unclear* from prior work. Our study was also in part motivated by the disparate impact of pruning and differential privacy on subgroup fairness. \n\n> Why focus on fairness? Is there any reason why this low-ranking should act only on fairness?\n\nThere are indeed many aspects that we could focus on. However, fairness is an important practical aspect deserving its own investigation (as opposed to spreading our efforts to more aspects but being less comprehensive). \n\nIndeed, many other aspects may be impacted by LoRA, and that the fairness impact could be an *indirect* result these other aspects. While true, this does *not* mean that *only* fairness is impacted or worth studying, or “this low-ranking should act only on fairness”. We have made no such claims. We added LoRA and observed the actual fairness metrics end-to-end; thus while it is possible to have other mechanisms at play, our study still provides useful insight into how LoRA behaves in the real world.\n\nNevertheless, we agree that it is interesting future work to study other potential (side) effects of LoRA.", "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 paper addresses fairness of Language Models.", "reasons_to_reject": "The main research question is really unclear: the relation between Low-Rank adaptation and fairness is really questionable. Let's use a metaphor. Suppose the goal is to prepare a system that is fair in showing traffic jams by hiding plates. Clearly, this kind of system is expected to blur only plates and keep everything in a clear shape. Now, it is clear that if the system blurs the whole picture, it blurs the plates too. The goal of hiding the plates is accomplished but final images are useless. Now, out of the metaphor, low-ranking is acting indiscriminately over all the parameters. So, it is clear that it has effects on every aspect of LMs. Then, why focusing on fairness? Is there any reason why this low-ranking should act only on fairness?", "questions_to_authors": "Provide a good reason why Low-Rank adaptation should have any influence on fairness of language models. See Reasons to Reject.", "extra_scores": {}}, {"review_id": "4HbPPg2tTu", "reviewer": "Reviewer_i2tt", "summary": "This work did a comprehensive benchmark of fairness for models fine-tuned by low-rank adaptation (LoRA). The models are evaluated on both classification and natural language tasks.\n\n---\nAfter reading the authors response, I will retain my rating. For improving the current work, I would recommend the authors to evaluate the LoRA models enforced with fairness constraints.", "questions": "", "limitations": "", "rating": 7, "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": "We thank the reviewer for their time and are glad that the reviewer finds our work valuable! We respond to the main comments below:\n\n> My own observation is that there is no significant difference of fairness metrics between LoRA fine-tuned and fully fine-tuned models. I don't think such observation implies anything on whether LoRA may exacerbate the biases or not, given the fact that the learning objectives are still identical.\n\nWe agree with the reviewer that our study provides *no evidence* for LoRA exacerbating unfairness (i.e. our study, albeit comprehensive, can possibly, but unlikely, be a false negative) and this is indeed distinct from *having evidence* that LoRA does *not* exacerbate fairness. We have acknowledged this limitation in Sec 4 and will ensure this is clear in the updated version. \n\n> Is the model fine-tuned subject to fairness constraints?\n\nNo, both LoRA and full fine-tuning are performed with standard Adam. We agree that it would be interesting to see how fair training (e.g. fairness constraints, or fair learning algorithms) will influence the fairness results. However, as we discussed at the end of section 2, since our end goal is to compare LoRA with full fine-tuning, we believe that the fine-tuning task *should not teach* the model to be fair, since it would otherwise introduce confounding factors of whether LoRA or full FT *responds* to a particular fairness constraint or fair learning algorithm better. Teaching the model to be fair would make whatever fairness conclusions we draw less generalizable to unseen tasks and models. \n\nNevertheless, the reviewer has raised a good point and we hope to add relevant discussions in the updated version.", "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 evaluation results are technically solid. The paper is well written. The paper could attract a large number of audience interested in the fairness of large models.", "reasons_to_reject": "My own observation is that there is no significant difference of fairness metrics between LoRA fine-tuned and fully fine-tuned models. I don't think such observation implies anything on whether LoRA may exacerbate the biases or not, given the fact that the learning objectives are still identical.", "questions_to_authors": "1. Is the model fine-tuned subject to fairness constraints? If not, I would like to see the comparison to the LoRA models that are explicitly enforced with fairness constraints.", "extra_scores": {}}]

Author rebuttal:

Meta-review: The paper presents a study on LoRA fairness implications. The paper presents solid work with minor concerns surfaced during the review process adequately addressed during author rebuttal. While reviewer tTvn provided a different opinion, it seems they may have not understood the final aim of the paper. I recommend the authors to include reviewer recommendations in their final version.

[At least one review was discounted during the decision process due to quality]
Decision: Accept