Spurious Correlations and Beyond: Understanding and Mitigating Shortcut Learning in SDOH Extraction with Large Language Models
Authors: Fardin Ahsan Sakib, Ziwei Zhu, Karen Trister Grace, Meliha Yetisgen, Ozlem Uzuner
Accepted at: acl25

Spurious Correlations and Beyond: Understanding and Mitigating Shortcut Learning in SDOH Extraction with Large Language Models

Fardin Ahsan Sakib1, Ziwei Zhu1, Karen Trister Grace2, Meliha Yetişgen3, Özlem Uzuner4
1Department of Computer Science, 2School of Nursing, 4Department of Information Sciences and Technology George Mason University
3Department of Biomedical Informatics & Medical Education, University of Washington
{fsakib,zzhu20,kgrace,ouzuner}@gmu.edu, melihay@uw.edu

Abstract
Social determinants of health (SDOH) extraction from clinical text is critical for downstream healthcare analytics. Although large language models (LLMs) have shown promise, they may rely on superficial cues leading to spurious predictions. Using the MIMIC portion of the SHAC (Social History Annotation Corpus) dataset and focusing on drug status extraction as a case study, we demonstrate that mentions of alcohol or smoking can falsely induce models to predict current/past drug use where none is present, while also uncovering concerning gender disparities in model performance. We further evaluate mitigation strategies—such as prompt engineering and chain-of-thought reasoning—to reduce these false positives, providing insights into enhancing LLM reliability in health domains.

1 Introduction
SDOH—including substance use, employment, and living conditions—strongly influence patient outcomes and clinical decision-making (Daniel et al., 2018; Himmelstein and Woolhandler, 2018; Armour et al., 2005). Extracting SDOH information from unstructured clinical text is increasingly important for enabling downstream healthcare applications and analysis (Jensen et al., 2012; Demner-Fushman et al., 2009). Although LLMs have shown promise in clinical natural language processing (NLP) tasks (Hu et al., 2024; Liu et al., 2023; Singhal et al., 2023), they often rely on superficial cues (Tang et al., 2023; Zhao et al., 2017), potentially leading to incorrect predictions undermining trust and utility in clinical settings.

Recent work has highlighted how LLMs can exhibit "shortcut learning" behaviors (Tu et al., 2020; Ribeiro et al., 2020; Zhao et al., 2018), where they exploit spurious patterns in training data rather than learning causal, generalizable features. This phenomenon spans various NLP tasks, from natural language inference (McCoy et al., 2019) to question-answering (Jia and Liang, 2017), and in clinical domains can lead to incorrect assumptions about patient conditions (Brown et al., 2023; Jabbour et al., 2020), threatening the utility of automated systems.

We investigate how LLMs produce spurious correlations in SDOH extraction through using drug status time classification (current, past, or none/unknown) as a case study. Using the MIMIC (Johnson et al., 2016) portion of the SHAC (Lybarger et al., 2021) dataset, we examine zero-shot and in-context learning scenarios across multiple LLMs (Llama (AI, 2024), Qwen (Yang et al., 2024), Llama3-Med42-70B (Christophe et al., 2024)). We explore multiple mitigation strategies to address these spurious correlations: examining the causal role of triggers through controlled removal experiments, implementing targeted prompt engineering approaches like chain-of-thought (CoT) reasoning (Wei et al., 2022), incorporating warning-based prompts, and augmenting with additional examples. While these interventions show promise—significant false positive rates persist, highlighting the deep-rooted nature of these biases and the need for more sophisticated solutions.

Contributions:
1. We present the first comprehensive analysis of spurious correlations in SDOH extraction across multiple LLM architectures, including domain-specialized models. Through extensive experiments in zero-shot and ICL settings, we demonstrate how models rely on superficial cues and verify their causal influence through controlled ablation studies.

2. We uncover systematic gender disparities in model performance, demonstrating another form of spurious correlation where models inappropriately leverage patient gender for drug
status time classification predictions.

3. We evaluate multiple prompt-based mitigation strategies (CoT, warnings, more examples) and analyze their limitations, demonstrating that while they reduce incorrect drug status time predictions, more robust solutions are needed for reliable clinical NLP deployments.

2 Related Work

Previous work on extracting SDOH from clinical text spans a progression from rule-based methods to fine-tuned neural models, leveraging annotated corpora for tasks like substance use and employment status extraction (Hatef et al., 2019; Patra et al., 2021; Yu et al., 2022; Han et al., 2022; Uzuner et al., 2008; Stemerman et al., 2021; Lybarger et al., 2023). More recent efforts have explored prompt-based approaches with LLMs, including GPT-4, to reduce reliance on extensive annotations (Ramachandran et al., 2023). While these approaches achieve competitive performance, studies across NLP tasks have shown that both fine-tuned and prompting-based methods often exploit spurious correlations or superficial cues (Ribeiro et al., 2020; Geirhos et al., 2020; Tu et al., 2020). Prior investigations have focused largely on spurious correlations in standard NLP tasks and supervised scenarios (McCoy et al., 2019; Zhao et al., 2018). In contrast, our work examines how these issues manifest in zero-shot and in-context SDOH extraction settings, and we propose prompt-level strategies to mitigate these correlations.

3 Methodology

3.1 Dataset and Task

We use the MIMIC-III portion of the SHAC dataset (Lybarger et al., 2021), which comprises 4405 deidentified social history note sections derived from MIMIC-III (Johnson et al., 2016) and the University of Washington clinical notes. SHAC is annotated using the BRAT tool (Stenetorp et al., 2012), capturing a variety of SDOH event types (e.g., Alcohol, Drug, Tobacco) as triggers along with associated arguments, including temporal status. To enable demographic analysis, we augmented the SHAC data by linking it with patient demographic information available in the original MIMIC-III dataset.

In this work, we examine spurious correlations in SDOH extraction through temporal drug status classification (current, past, or none/unknown). We adopt a two-step pipeline (Ma et al., 2022, 2023):

(1) Trigger Identification: Given a social history note, the model identifies spans corresponding to the target event type (e.g., drug use).

(2) Argument Resolution: For each trigger, models applies a multiple-choice QA prompt to determine the temporal status (current/past/none).

The dataset contains diverse patterns of substance documentation, see Appendix B for detailed examples of the task and annotation schema.

3.2 Experimental Setup

Model Configurations We evaluate multiple model configurations:

• Zero-Shot: Models receive only task instructions and input text, with no examples

• In-Context Learning (ICL): Models are provided with three example demonstrations before making predictions on a new instance. Examples are selected to maintain balanced representation across substance use patterns (none/single/multiple) and drug use outcomes (positive/negative).

• Fine-Tuning (SFT): We also fine-tune a Llama-3.1-8B model on the MIMIC portion of the SHAC dataset to assess whether domain adaptation reduces spurious correlations.

We consider Llama-3.1-70B (zero-shot, ICL), Llama-3.1-8B (fine-tuned on MIMIC), Qwen-72B (ICL), and Llama3-Med42-70B (ICL). These models span various parameter sizes and domain specializations. The fine-tuned Llama-8B model provides insights into whether in-domain adaptation mitigates the observed shortcut learning.

Prompting Strategies We additionally use two other prompting strategies (see Appendix A for complete templates):

Chain-of-Thought (CoT): This prompt explicitly guides reasoning through five steps: (1) read the social history note carefully, (2) identify relevant information, (3) consider examples provided, (4) explain your reasoning process, (5) provide the answer. This encourages explicit reasoning to reduce shortcuts.

Warning-Based: This incorporates explicit guidelines to counter spurious correlations: (1) evaluate each factor independently - never assume
one behavior implies another, (2) extract only explicitly stated information - avoid making assumptions based on demographic or other factors, (3) use [none] when information isn’t mentioned.

Evaluation Framework Our primary evaluation metric is the false positive rate (FPR), defined as: \( FPR = FP/(FP + TN) \) where FP represents false positives (predicted current/past use when ground truth was none/unknown) and TN represents true negatives (correctly predicted none/unknown). We prioritize FPR given the clinical risks of incorrect positive drug use predictions—including patient stigmatization, biased provider perceptions, and diminished trust in automated systems (Van Boekel et al., 2013; Dahl et al., 2022). A higher FPR indicates more frequent erroneous predictions that could directly impact patient care. We specifically examine FPR disparities between substance-positive and substance-negative contexts to reveal whether models rely on superficial cues rather than actual evidence. See Appendix C for extended discussion.

To analyze potential spurious correlations, we categorize notes based on their ground truth substance use status:

• Substance-positive: Notes documenting current/past use of the respective substance (alcohol or smoking)

• Substance-negative: Notes where the ground truth indicates no use or unknown status

Experimental Settings

• Original: Evaluate models on the original notes.

• Without Alcohol/Smoking Triggers: Remove mentions of alcohol/smoking to test their causal role in inducing false positives.

4 Results

4.1 RQ1: Do Large Language Models Exhibit Spurious Correlations in SDOH Extraction?

As shown in Table 1, our analysis in a zero-shot setting with Llama-70B reveals high false positive rates for drug status time classification in alcohol-positive (66.21%) and smoking-positive (61.11%) notes. In contrast, alcohol-negative and smoking-negative notes show substantially lower false positive rates (28.83% and 29.76%, respectively). This stark contrast suggests that the mere presence of alcohol or smoking triggers biases the model towards inferring nonexistent drug use. These biases likely stem from the pre-training phase, potentially reinforcing societal assumptions about correlations between different types of substance use.

4.2 RQ2: Do In-Context Learning and Fine-Tuning Reduce These Spurious Correlations?

Providing three in-context examples reduces false positives significantly. For Llama-70B, ICL lowers alcohol-positive mismatches from 66.21% to 48.28%, though a gap remains relative to alcohol-negative notes (11.71%). Similarly, smoking-positive mismatches decrease from 61.11% to 36.42% versus 18.05% for smoking-negative. The effectiveness of ICL suggests that explicit examples help the model focus on relevant features, though the persistence of some bias indicates deep-rooted associations from pre-training. Fine-tuning Llama-8B on the MIMIC subset (SFT) yields further improvements: alcohol-positive mismatches drop to 32.41% and smoking-positive to 36.42%, with corresponding negatives at 12% and 7% respectively, indicating that domain adaptation helps override some pre-trained biases.

4.3 RQ3: Are These Superficial Mentions Causally Driving the Model’s Predictions?

To confirm the causal role of alcohol and smoking mentions, we remove these triggers from the notes. Across models, this consistently lowers false positives. For instance, Llama-70B zero-shot sees alcohol-positive mismatches fall from 66.21% to 55.17% after removing alcohol triggers. Similarly, Llama-8B-SFT reduces alcohol-positive errors from 32.41% to 26.9%. Similar trends are observed across other architectures including domain-specific models (see appendix E), confirming that alcohol and smoking cues spuriously bias the models’ drug-use predictions.

4.4 RQ4: Are there systematic demographic variations in these spurious correlations?

Beyond substance-related triggers, our analysis (Table 2) uncovers another concerning form of spurious correlation: systematic performance differences based on patient gender. Just as models incorrectly rely on mere mentions of alcohol or smoking to infer substance use, they appear to
Table 1: False Positive Rates (%) Across Different Models and Approaches. *Smoking+Alcohol* refers to cases where both *Smoking-positive* and *Alcohol-positive* are true.

<table>
  <tr>
    <th rowspan="2">Cases</th>
    <th colspan="4">Llama-70B</th>
    <th colspan="2">Llama-8B</th>
    <th colspan="2">Llama3-Med42-70B</th>
    <th colspan="2">Qwen-72B</th>
  </tr>
  <tr>
    <th>Zero-shot</th>
    <th>ICL</th>
    <th>CoT</th>
    <th>Warning</th>
    <th>Increased-Examples</th>
    <th>Vanilla Fine-tuned</th>
    <th>ICL</th>
    <th>ICL</th>
    <th>ICL</th>
    <th>ICL</th>
  </tr>
  <tr>
    <td>Alcohol-positive</td>
    <td>66.21</td>
    <td>48.28</td>
    <td>33.79</td>
    <td>40.69</td>
    <td>45.52</td>
    <td>73.10</td>
    <td>32.41</td>
    <td>66.90</td>
    <td>62.76</td>
  </tr>
  <tr>
    <td>Smoking-positive</td>
    <td>61.11</td>
    <td>36.42</td>
    <td>25.93</td>
    <td>29.63</td>
    <td>30.25</td>
    <td>74.07</td>
    <td>36.42</td>
    <td>57.41</td>
    <td>53.09</td>
  </tr>
  <tr>
    <td>Alcohol-negative</td>
    <td>28.83</td>
    <td>11.71</td>
    <td>6.76</td>
    <td>5.41</td>
    <td>10.81</td>
    <td>37.39</td>
    <td>12.16</td>
    <td>16.22</td>
    <td>46.85</td>
  </tr>
  <tr>
    <td>Smoking-negative</td>
    <td>29.76</td>
    <td>18.05</td>
    <td>10.73</td>
    <td>11.22</td>
    <td>20.00</td>
    <td>33.66</td>
    <td>7.32</td>
    <td>19.51</td>
    <td>53.17</td>
  </tr>
  <tr>
    <td>Smoking+Alcohol</td>
    <td>73.26</td>
    <td>51.16</td>
    <td>34.88</td>
    <td>45.35</td>
    <td>39.53</td>
    <td>81.40</td>
    <td>40.70</td>
    <td>76.74</td>
    <td>56.98</td>
  </tr>
</table>

leverage patient gender as an inappropriate predictive signal. For the base Llama-70B model in zero-shot settings, false positive rates show stark gender disparities - male patients consistently face higher misclassification rates compared to female patients (71.15% vs 53.66% for alcohol-positive cases, and 66.67% vs 50.88% for smoking-positive cases). This pattern persists with in-context learning, with the gender gap remaining substantial (alcohol-positive: 52.88% male vs 36.59% female). Fine-tuned models showed similar disparities, with Llama-8B-SFT maintaining a performance gap of approximately 15 percentage points between genders for alcohol-positive cases.

Notably, these gender-based differences exhibit complex interactions with substance-related triggers. Cases involving positive substances mentions show the most pronounced disparities, with male patients seeing up to 20 percentage point higher false positive rates. This suggests that the model’s shortcut learning compounds across different dimensions - gender biases amplify substance-related biases and vice versa. The persistence of these interacting biases across model architectures, sizes, and prompting strategies suggests they arise from deeply embedded patterns in both pre-training data and medical documentation practices.

5 Mitigation Strategies and Results

We explore several mitigation techniques to address the spurious correlations identified in our analysis:

Chain-of-Thought (CoT) As shown in Table 1, instructing the model to reason step-by-step before producing an answer leads to substantial reductions across all architectures. For Llama-70B, CoT reduces alcohol-positive mismatches from 66.21% (zero-shot) to 33.79%, with smoking-positive cases decreasing from 61.11% to 25.93%. Similar improvements are observed in other models (see appendix F), with Qwen-72B showing particularly strong response to CoT. This suggests CoT helps models avoid superficial cues and focus on explicit information.

Warning-Based Instructions We prepend explicit instructions cautioning the model not to assume drug use without evidence and to treat each factor independently. With Llama-70B, these warnings lower alcohol-positive mismatches from 66.21% to approximately 40.69%, and also benefit smoking-positive scenarios. While not as strong as CoT, these warnings yield meaningful improvements across different architectures.

Increased Number of Examples Providing more than three examples—up to eight—further stabilizes predictions. For Llama-70B, increasing the number of examples reduces false positive rates considerably, with alcohol-positive mismatches falling to 45.52% (compared to 66.21% zero-shot). Similar trends are observed in other models, though the magnitude of improvement varies (see appendix F). While not as dramatic as CoT, additional examples help guide models away from faulty heuristics.

6 Discussion

Our findings highlight a key challenge in applying large language models to clinical information extraction: even when models achieve strong performance on average, they rely on superficial cues rather than genuine understanding of the underlying concepts. The presence of alcohol- or smoking-related mentions biases models to infer drug use incorrectly, and these shortcuts persist across Llama variants, Qwen, and Llama3-Med42-70B. The effectiveness of mitigation strategies like chain-of-thought reasoning, warning-based instructions, and additional examples underscores the importance of careful prompt design. While these interventions help guide models to focus on explicit evidence, their partial success suggests the need for more
Table 2: Gender-Based Analysis of False Positive Rates (%) Across Models

<table>
  <tr>
    <th rowspan="2">Cases</th>
    <th colspan="2">Llama-70B Zero-shot</th>
    <th colspan="2">Llama-70B ICL</th>
    <th colspan="2">Llama-8B SFT</th>
    <th colspan="2">Qwen-72B</th>
  </tr>
  <tr>
    <th>Female</th>
    <th>Male</th>
    <th>Female</th>
    <th>Male</th>
    <th>Female</th>
    <th>Male</th>
    <th>Female</th>
    <th>Male</th>
  </tr>
  <tr>
    <td>Alcohol-positive</td>
    <td>53.66</td>
    <td>71.15</td>
    <td>36.59</td>
    <td>52.88</td>
    <td>21.95</td>
    <td>36.54</td>
    <td>68.29</td>
    <td>60.58</td>
  </tr>
  <tr>
    <td>Smoking-positive</td>
    <td>50.88</td>
    <td>66.67</td>
    <td>28.07</td>
    <td>40.95</td>
    <td>24.56</td>
    <td>42.86</td>
    <td>49.12</td>
    <td>55.24</td>
  </tr>
  <tr>
    <td>Alcohol-negative</td>
    <td>29.13</td>
    <td>28.42</td>
    <td>9.45</td>
    <td>14.74</td>
    <td>9.45</td>
    <td>15.79</td>
    <td>47.24</td>
    <td>46.32</td>
  </tr>
  <tr>
    <td>Smoking-negative</td>
    <td>27.03</td>
    <td>32.98</td>
    <td>9.91</td>
    <td>27.66</td>
    <td>6.31</td>
    <td>8.51</td>
    <td>54.05</td>
    <td>52.13</td>
  </tr>
  <tr>
    <td>Smoking+Alcohol</td>
    <td>81.82</td>
    <td>84.62</td>
    <td>54.55</td>
    <td>58.97</td>
    <td>27.27</td>
    <td>53.85</td>
    <td>27.27</td>
    <td>30.77</td>
  </tr>
</table>

robust approaches - integrating domain-specific knowledge, implementing adversarial training, or curating more balanced datasets. Our demographic analysis reveals that these spurious correlations are not uniformly distributed across patient groups, raising fairness concerns for clinical deployment. Addressing such disparities requires both algorithmic improvements and careful consideration of deployment strategies. Clinicians and stakeholders must be aware of these limitations before deploying LLMs in clinical decision-support systems. Understanding these systematic biases in automated analysis can inform improvements not only in model development but also in clinical documentation practices and standards.

7 Implications Beyond NLP: Clinical Documentation and Practice

The implications of this study extend beyond NLP methodologies. Our analysis reveals that these models not only learn but potentially amplify existing biases in clinical practice. The identified error patterns—particularly the tendency to infer substance use from smoking/alcohol mentions and gender-based performance disparities—mirror documented provider biases in clinical settings (Saloner et al., 2023; Meyers et al., 2021). Notably, these biases appear to originate partly from medical documentation practices themselves (Ivy et al., 2024; Kim et al., 2021; Markowitz, 2022). Our finding that explicit evidence-based reasoning (through CoT) reduces these biases aligns with established strategies for mitigating provider bias (Mateo and Williams). This parallel between computational and human biases suggests that systematic analysis of LLM behavior could inform broader efforts to identify and address biases in medical documentation and practice, potentially contributing to improved provider education and documentation standards.

8 Conclusion

This work presents the first systematic exploration of spurious correlations in SDOH extraction, revealing how contextual cues can lead to incorrect and potentially harmful predictions in clinical settings. Beyond demonstrating the problem, we’ve evaluated several mitigation approaches that, while promising, indicate the need for more sophisticated solutions. Future work should focus on developing robust debiasing techniques, leveraging domain expertise, and establishing comprehensive evaluation frameworks to ensure reliable deployment across diverse populations.

9 Limitations

Dataset limitations  Our analysis relied exclusively on the MIMIC portion of the SHAC dataset, which constrains the generalizability of our findings. While we observe consistent gender-based performance disparities, a more diverse dataset could help establish the breadth of these biases.

Model coverage  We focused solely on open-source large language models (e.g., Llama, Qwen). Extending the evaluation to additional data sources, closed-source models, and other domain-specific architectures would help verify the robustness of our conclusions.

Causal understanding  While we established the causality of triggers through removal experiments, understanding why specific triggers affect certain models or scenarios would require deeper analysis using model interpretability techniques.

Methodology scope  Our study focused exclusively on generative methods; results may not generalize to traditional pipeline-based approaches that combine sequence labeling and relation classification.

Mitigation effectiveness  While we identified various spurious correlations, our mitigation strategies
could not completely address the problem, leaving room for future work on addressing these issues.

10 Ethics Statement

All experiments used de-identified social history data from the SHAC corpus, with LLMs deployed on a secure university server. We followed all data use agreements and institutional IRB protocols. Although the dataset is fully de-identified, biases within the models could raise ethical concerns in real-world applications. Further validation and safeguards are recommended before clinical deployment.

11 Acknowledgments

We thank our collaborators for their valuable feedback and support. Generative AI assistants were used for grammar checking and LaTeX formatting; the authors retain full responsibility for the final content and analysis.

References

Meta AI. 2024. Llama 3.1 model card. https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md. Accessed: 2024-12-13.

BS Armour, T Woollery, A Malarcher, TF Pechacek, and C Husten. 2005. Annual smoking-attributable mortality, years of potential life lost, and productivity losses—united states, 1997-2001. JAMA: Journal of the American Medical Association, 294(7).

Alexander Brown, Nenad Tomasev, Jan Freyberg, Yuan Liu, Alan Karthikesalingam, and Jessica Schrouff. 2023. Detecting shortcut learning for fair medical ai using shortcut testing. Nature communications, 14(1):4314.

Clément Christophe, Praveen K Kanithi, Tathagata Raha, Shadab Khan, and Marco AF Pimentel. 2024. Med42-v2: A suite of clinical llms. Preprint arXiv:2408.06142.

Rachel A Dahl, J Priyanka Vakkalanka, Karisa K Harland, and Joshua Radke. 2022. Investigating healthcare provider bias toward patients who use drugs using a survey-based implicit association test: Pilot study. Journal of addiction medicine, 16(5):557–562.

Hilary Daniel, Sue S Bornstein, Gregory C Kane, Health, and Public Policy Committee of the American College of Physicians*. 2018. Addressing social determinants to improve patient care and promote health equity: an american college of physicians position paper. Annals of internal medicine, 168(8):577–578.

Dina Demner-Fushman, Wendy W Chapman, and Clement J McDonald. 2009. What can natural language processing do for clinical decision support? Journal of biomedical informatics, 42(5):760–772.

Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence, 2(11):665–673.

Sifei Han, Robert F Zhang, Lingyun Shi, Russell Richie, Haixia Liu, Andrew Tseng, Wei Quan, Neal Ryan, David Brent, and Fuchiang R Tsui. 2022. Classifying social determinants of health from unstructured electronic health records using deep learning-based natural language processing. Journal of biomedical informatics, 127:103984.

Elham Hatef, Masoud Rouhizadeh, Iddrisu Tia, Elyse Lasser, Felicia Hill-Briggs, Jill Marsteller, Hadi Kharrazi, et al. 2019. Assessing the availability of data on social and behavioral determinants in structured and unstructured electronic health records: a retrospective analysis of a multilevel health care system. JMIR medical informatics, 7(3):e13802.

David U Himmelstein and Steffie Woolhandler. 2018. Determined action needed on social determinants. Annals of internal medicine, 168(8):596–597.

Yan Hu, Qinyu Chen, Jingcheng Du, Xueqing Peng, Vipina Kutichi Keloth, Xu Zuo, Yujia Zhou, Zehan Li, Xiaoqian Jiang, Zhiyong Lu, et al. 2024. Improving large language models for clinical named entity recognition via prompt engineering. Journal of the American Medical Informatics Association, page ocad259.

Zalaya K Ivy, Sharon Hwee, Brittany C Kimball, Michael D Evans, Nicholas Marka, Catherine Benzel, and Alexander A Boucher. 2024. Disparities in documentation: evidence of race-based biases in the electronic medical record. Journal of Racial and Ethnic Health Disparities, pages 1–7.

Sarah Jabbour, David Fouhey, Ella Kazerooni, Michael W Sjoding, and Jenna Wiens. 2020. Deep learning applied to chest x-rays: exploiting and preventing shortcuts. In Machine Learning for Healthcare Conference, pages 750–782. PMLR.

Peter B Jensen, Lars J Jensen, and Søren Brunak. 2012. Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics, 13(6):395–405.

Robin Jia and Percy Liang. 2017. Adversarial examples for evaluating reading comprehension systems. arXiv preprint arXiv:1707.07328.

Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. 2016. Mimic-iii, a freely accessible critical care database. Scientific data, 3(1):1–9.
Min Kyung Kim, Joy Noel Baumgartner, Jennifer Headley, Julius Kiryu, James Kaggwa, and Joseph R Egger. 2021. Medical record bias in documentation of obstetric and neonatal clinical quality of care indicators in uganda. Journal of Clinical Epidemiology, 136:10–19.

Zhengliang Liu, Yue Huang, Xiaowei Yu, Lu Zhang, Zihao Wu, Chao Cao, Haixing Dai, Lin Zhao, Yiwei Li, Peng Shu, et al. 2023. Deid-gpt: Zero-shot medical text de-identification by gpt-4. arXiv preprint arXiv:2303.11032.

Kevin Lybarger, Nicholas J Dobbins, Ritche Long, Anagad Singh, Patrick Wedgeworth, Özlem Uzuner, and Meliha Yetisgen. 2023. Leveraging natural language processing to augment structured social determinants of health data in the electronic health record. Journal of the American Medical Informatics Association, 30(8):1389–1397.

Kevin Lybarger, Mari Ostendorf, and Meliha Yetisgen. 2021. Annotating social determinants of health using active learning, and characterizing determinants using neural event extraction. Journal of Biomedical Informatics, 113:103631.

Mingyu Derek Ma, Alexander K Taylor, Wei Wang, and Nanyun Peng. 2022. Dice: data-efficient clinical event extraction with generative models. arXiv preprint arXiv:2208.07989.

Yubo Ma, Yixin Cao, YongChing Hong, and Aixin Sun. 2023. Large language model is not a good few-shot information extractor, but a good reranker for hard samples! arXiv preprint arXiv:2303.08559.

David M Markowitz. 2022. Gender and ethnicity bias in medicine: A text analysis of 1.8 million critical care records. PNAS nexus, 1(4):pgac157.

CM Mateo and DR Williams. Addressing bias and reducing discrimination. The professional responsibility of health care providers, 2020:95.

RT McCoy, E Pavlick, and T Linzen. 2019. Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference. arxiv preprint arxiv:190201007.

SA Meyers, VA Earnshaw, Brittany D’Ambrosio, Natasha Courschesne, Dan Werb, and LR Smith. 2021. The intersection of gender and drug use-related stigma: A mixed methods systematic review and synthesis of the literature. Drug and alcohol dependence, 223:108706.

Braja G Patra, Mohit M Sharma, Veer Vekaria, Prakash Adekkannattu, Olga V Patterson, Benjamin Glicksberg, Lauren A Lepow, Euijing Ryu, Joanna M Biernacka, Al’iona Furmanchuk, et al. 2021. Extracting social determinants of health from electronic health records using natural language processing: a systematic review. Journal of the American Medical Informatics Association, 28(12):2716–2727.

Giridhar Kaushik Ramachandran, Yujuan Fu, Bin Han, Kevin Lybarger, Nicholas J Dobbins, Özlem Uzuner, and Meliha Yetisgen. 2023. Prompt-based extraction of social determinants of health using few-shot learning. Preprint, arXiv:2306.07170.

Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020. Beyond accuracy: Behavioral testing of nlp models with checklist. arXiv preprint arXiv:2005.04118.

Brendan Saloner, Wenshu Li, Michael Flores, Ana M Progovac, and Benjamin Lé Cook. 2023. A widening divide: Cigarette smoking trends among people with substance use disorder and criminal legal involvement: Study examines cigarette smoking trends among people with substance use disorders and people with criminal legal involvement. Health Affairs, 42(2):187–196.

Karan Singhal, Shekoofeh Azizi, Tao Tu, S Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani, Heather Cole-Lewis, Stephen Pfohl, et al. 2023. Large language models encode clinical knowledge. Nature, 620(7972):172–180.

Rachel Stemberman, Jaime Arguello, Jane Brice, Ashok Krishnamurthy, Mary Houston, and Rebecca Kitzmiller. 2021. Identification of social determinants of health using multi-label classification of electronic health record clinical notes. JAMIA open, 4(3):ooaa069.

Pontus Stenetorp, Sampo Pyysalo, Goran Topić, Tomoko Ohta, Sophia Ananiadou, and Jun’ichi Tsujii. 2012. Brat: a web-based tool for nlp-assisted text annotation. In Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 102–107.

Ruxiang Tang, Dehan Kong, Longtao Huang, and Hui Xue. 2023. Large language models can be lazy learners: Analyze shortcuts in in-context learning. arXiv preprint arXiv:2305.17256.

Lifu Tu, Garima Lalwani, Spandana Gella, and He He. 2020. An empirical study on robustness to spurious correlations using pre-trained language models. Transactions of the Association for Computational Linguistics, 8:621–633.

Özlem Uzuner, Ira Goldstein, Yuan Luo, and Isaac Kohane. 2008. Identifying patient smoking status from medical discharge records. Journal of the American Medical Informatics Association, 15(1):14–24.

Leonieke C Van Boeckel, Evelien PM Brouwers, Jaap Van Weeghel, and Henk FL Garretsen. 2013. Stigma among health professionals towards patients with substance use disorders and its consequences for healthcare delivery: systematic review. Drug and alcohol dependence, 131(1-2):23–35.

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou,
et al. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824–24837.

An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, Guanting Dong, Haoran Wei, Huan Lin, Jialong Tang, Jialin Wang, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Ma, Jianxin Yang, Jin Xu, Jingren Zhou, Jinze Bai, Jinzheng He, Junyang Lin, Kai Dang, Keming Lu, Keqin Chen, Kexin Yang, Mei Li, Mingfeng Xue, Na Ni, Pei Zhang, Peng Wang, Ru Peng, Rui Men, Ruizhe Gao, Runji Lin, Shijie Wang, Shuai Bai, Sinan Tan, Tianhang Zhu, Tianhao Li, Tianyu Liu, Wenbin Ge, Xiaodong Deng, Xiaohuan Zhou, Xingzhang Ren, Xinyu Zhang, Xipin Wei, Xucheng Ren, Xuejing Liu, Yang Pan, Yang Yao, Yichang Zhang, Yu Wan, Yunfei Chu, Yuqiong Liu, Zeyu Cui, Zhenni Zhang, Zhifang Guo, and Zhihao Fan. 2024. Qwen2 technical report. Preprint, arXiv:2407.10671.

Zehao Yu, Xi Yang, Chong Dang, Songzi Wu, Prakash Adekanattu, Jyotishman Pathak, Thomas J George, William R Hogan, Yi Guo, Jiang Bian, et al. 2022. A study of social and behavioral determinants of health in lung cancer patients using transformers-based natural language processing models. In AMIA Annual Symposium Proceedings, volume 2021, page 1225.

Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2017. Men also like shopping: Reducing gender bias amplification using corpus-level constraints. arXiv preprint arXiv:1707.09457.

Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018. Gender bias in coreference resolution: Evaluation and debiasing methods. arXiv preprint arXiv:1804.06876.

A Prompting Strategies

All prompting approaches share a base system message identifying the model’s role as "an AI assistant specialized in extracting and analyzing social history information from medical notes." Each strategy then builds upon this foundation with specific modifications:

Zero-Shot
The baseline approach uses a minimal prompt structure: System: AI assistant specialized in social history extraction User: For the following social history note: [Clinical note text] [Task instruction] [Options if applicable] This setup evaluates the model’s ability to perform extraction tasks using only its pre-trained knowledge, without additional guidance or examples.

In-Context Learning (ICL)
This approach augments the base prompt with three carefully selected demonstration examples. Each example follows a structured JSON format: json "id": "example-id", "instruction": "Extract all Drug text spans...", "input": "Social History: Patient denies drug use...", "options": "[Multiple choice options if applicable]", "output": "Expected extraction or classification"

Chain-of-Thought (CoT)
Building upon ICL, this method explicitly guides the model through a structured reasoning process: Please approach this task step-by-step: 1. Carefully read the social history note 2. Identify all relevant information related to the question 3. Consider the examples provided 4. Explain your reasoning process 5. Provide your final answer This approach aims to reduce spurious correlations and shortcut learning by encouraging explicit articulation of the reasoning process before arriving at the final extraction or classification.

Warning-Based
This specialized approach incorporates explicit rules and warnings in the system message: Important Guidelines: 1. Evaluate each factor independently - never assume one behavior implies another 2. Extract only explicitly stated information - don’t make assumptions based on demographics or other factors 3. If information isn’t mentioned, use [none] or select "not mentioned" option These guidelines specifically address the challenge of false positives in substance use detection by discouraging inference-based conclusions without explicit textual evidence. The warnings are designed to counteract the model’s tendency to make assumptions based on superficial cues or demographic factors.

B Dataset Details

B.1 Data Format and Annotation Process
The SHAC dataset originally consists of paired text files (.txt) containing social history notes and annotation files (.ann) capturing SDOH information. We convert these into a question-answering format to evaluate LLMs. Below we demonstrate this process with a synthetic example:

Raw Note (.txt)
SOCIAL HISTORY:
Patient occasionally uses alcohol.
Denies any illicit drug use.

BRAT Annotations (.ann)

T1 Alcohol 24 31 alcohol
T2 Drug 47 50 drug
T3 StatusTime 8 19 occasionally
T4 StatusTime 32 37 denies

E1 Alcohol:T1 Status:T3
E2 Drug:T2 Status:T4

A1 StatusTimeVal T3 current
A2 StatusTimeVal T4 none

Here, T1 and T2 are triggers - spans of text that indicate the presence of SDOH events (e.g., "alcohol" for substance use). The annotations also capture arguments - additional information about these events, such as their temporal status represented by T3 and T4. For example, T3 ("occasionally") indicates a temporal status of current for alcohol use.

We transform these structured annotations into two types of questions:

Trigger Identification Questions about identifying relevant event spans:

{"id": "0001-Alcohol",
"instruction": "Extract all Alcohol text spans as it is from the note. If multiple spans present, separate them by [SEP]. If none, output [none].",
"input": "SOCIAL HISTORY: Patient occasionally uses alcohol. Denies any illicit drug use.",
"output": "alcohol"}

Argument-Resolution Questions about determining event properties:

{"id": "0001-Alcohol_StatusTime",
"instruction": "Choose the best StatusTime value for the <alcohol> (Alcohol) from the note:",
"input": "SOCIAL HISTORY: Patient occasionally uses alcohol. Denies any illicit drug use.",
"options": "Options: (a) none. (b) current. (c) past. (d) Not Applicable.",
"output": "(b) current."}

C Metric Selection and Justification

Our focus on False Positive Rate (FPR) is motivated by the unique risks associated with incorrect substance use predictions in clinical settings (Van Boekel et al., 2013; Dahl et al., 2022). While traditional metrics like accuracy or F1-score treat all errors equally, FPR specifically captures the rate of unwarranted "positive" classifications—a critical concern when dealing with sensitive patient information. High FPR values indicate that models frequently make unjustified drug use predictions, which could lead to:

• Patient stigmatization and potential discrimination
• Reduced quality of care due to biased provider perceptions
• Diminished trust in automated clinical decision support systems

Conversely, lower FPR values suggest better model reliability in avoiding these harmful misclassifications. While comprehensive evaluation would benefit from additional metrics, FPR serves as a particularly relevant indicator for assessing model safety and reliability in clinical applications.

D Model Fine-tuning and Computational Resources

We fine-tuned Llama-8B using LoRA with rank 64 and dropout 0.1. Key training parameters include a learning rate of 2e-4, batch size of 4, and 5 training epochs. Training was conducted on 2 NVIDIA A100 GPUs for approximately 3 hours using mixed precision (FP16). For our main experiments, we used several large language models: Llama-70B (70B parameters), Qwen-72B (72B parameters), Llama3-Med42-70B (70B parameters), and our fine-tuned Llama-8B (8B parameters). The inference experiments across all models required approximately 100 GPU hours on 2 NVIDIA A100 GPUs. This computational budget covered all experimental settings including zero-shot, in-context learning, and the evaluation of various mitigation strategies.
E  Trigger Removal Experiments

Table 3: Impact of Trigger Removal on Llama 3.1 Models False Positive Rates (%)

<table>
  <tr>
    <th rowspan="2">Cases</th>
    <th colspan="3">Llama 3.1 70b Zero-shot</th>
    <th colspan="3">Llama 3.1 8b SFT</th>
  </tr>
  <tr>
    <th>Full</th>
    <th>Without Alcohol</th>
    <th>Without Smoking</th>
    <th>Full</th>
    <th>Without Alcohol</th>
    <th>Without Smoking</th>
  </tr>
  <tr>
    <td>Alcohol-positive</td>
    <td>66.21</td>
    <td>55.17</td>
    <td>64.14</td>
    <td>32.41</td>
    <td>26.90</td>
    <td>33.10</td>
  </tr>
  <tr>
    <td>Smoking-positive</td>
    <td>61.11</td>
    <td>54.94</td>
    <td>56.79</td>
    <td>36.42</td>
    <td>32.10</td>
    <td>31.48</td>
  </tr>
  <tr>
    <td>Alcohol-negative</td>
    <td>28.83</td>
    <td>25.23</td>
    <td>23.87</td>
    <td>12.16</td>
    <td>12.16</td>
    <td>8.11</td>
  </tr>
  <tr>
    <td>Smoking-negative</td>
    <td>29.76</td>
    <td>22.93</td>
    <td>26.34</td>
    <td>7.32</td>
    <td>6.83</td>
    <td>7.32</td>
  </tr>
  <tr>
    <td>Smoking+Alcohol</td>
    <td>73.26</td>
    <td>65.12</td>
    <td>72.09</td>
    <td>40.70</td>
    <td>32.56</td>
    <td>41.86</td>
  </tr>
</table>

Table 4: Impact of Trigger Removal on Additional Models’ False Positive Rates (%)

<table>
  <tr>
    <th rowspan="2">Cases</th>
    <th colspan="3">Llama 3.1 70B ICL</th>
    <th colspan="3">Llama3-Med42-70B</th>
    <th colspan="3">Qwen-72B</th>
  </tr>
  <tr>
    <th>Full</th>
    <th>Without Alcohol</th>
    <th>Without Smoking</th>
    <th>Full</th>
    <th>Without Alcohol</th>
    <th>Without Smoking</th>
    <th>Full</th>
    <th>Without Alcohol</th>
    <th>Without Smoking</th>
  </tr>
  <tr>
    <td>Alcohol-positive</td>
    <td>48.28</td>
    <td>38.62</td>
    <td>47.59</td>
    <td>66.90</td>
    <td>53.10</td>
    <td>64.83</td>
    <td>62.76</td>
    <td>51.72</td>
    <td>54.48</td>
  </tr>
  <tr>
    <td>Smoking-positive</td>
    <td>36.42</td>
    <td>32.72</td>
    <td>32.09</td>
    <td>57.41</td>
    <td>51.85</td>
    <td>52.47</td>
    <td>53.09</td>
    <td>45.68</td>
    <td>51.23</td>
  </tr>
  <tr>
    <td>Alcohol-negative</td>
    <td>11.71</td>
    <td>16.22</td>
    <td>10.81</td>
    <td>16.22</td>
    <td>16.22</td>
    <td>13.96</td>
    <td>46.85</td>
    <td>45.05</td>
    <td>47.75</td>
  </tr>
  <tr>
    <td>Smoking-negative</td>
    <td>18.05</td>
    <td>14.15</td>
    <td>15.12</td>
    <td>19.51</td>
    <td>14.15</td>
    <td>19.51</td>
    <td>33.17</td>
    <td>49.27</td>
    <td>49.76</td>
  </tr>
  <tr>
    <td>Smoking+Alcohol</td>
    <td>51.16</td>
    <td>44.19</td>
    <td>46.51</td>
    <td>76.74</td>
    <td>66.28</td>
    <td>73.26</td>
    <td>56.98</td>
    <td>43.02</td>
    <td>50.00</td>
  </tr>
</table>

F  Mitigation Experiments

Table 5: Impact of Mitigation Strategies on Additional Models’ False Positive Rates (%)

<table>
  <tr>
    <th rowspan="2">Cases</th>
    <th colspan="4">Llama3-Med42-70B</th>
    <th colspan="4">Qwen-72B</th>
  </tr>
  <tr>
    <th>ICL</th>
    <th>CoT</th>
    <th>Warning</th>
    <th>Increased Examples</th>
    <th>ICL</th>
    <th>CoT</th>
    <th>Warning</th>
    <th>Increased Examples</th>
  </tr>
  <tr>
    <td>Alcohol-positive</td>
    <td>66.90</td>
    <td>48.28</td>
    <td>62.76</td>
    <td>63.45</td>
    <td>62.76</td>
    <td>28.97</td>
    <td>34.38</td>
    <td>36.55</td>
  </tr>
  <tr>
    <td>Smoking-positive</td>
    <td>57.41</td>
    <td>35.19</td>
    <td>53.09</td>
    <td>50.62</td>
    <td>53.09</td>
    <td>23.46</td>
    <td>32.09</td>
    <td>33.33</td>
  </tr>
  <tr>
    <td>Alcohol-negative</td>
    <td>16.22</td>
    <td>6.76</td>
    <td>16.67</td>
    <td>15.76</td>
    <td>46.85</td>
    <td>19.82</td>
    <td>22.07</td>
    <td>26.12</td>
  </tr>
  <tr>
    <td>Smoking-negative</td>
    <td>19.51</td>
    <td>13.66</td>
    <td>18.54</td>
    <td>18.05</td>
    <td>53.17</td>
    <td>17.07</td>
    <td>25.85</td>
    <td>29.27</td>
  </tr>
  <tr>
    <td>Smoking+Alcohol</td>
    <td>76.74</td>
    <td>53.49</td>
    <td>72.09</td>
    <td>68.60</td>
    <td>56.98</td>
    <td>32.56</td>
    <td>37.21</td>
    <td>41.86</td>
  </tr>
</table>

Reviewer paper summaries: This paper investigates spurious correlations in SDOH extraction from clinical text and evaluates several mitigation approaches. As a case study, the authors demonstrate that mentions of alcohol or smoking can falsely induce models to predict current/past drug use where none is present. In addition, they find gender disparities in model performance, demonstrating another form of spurious correlation where models inappropriately leverage patient gender for drug status time classification predictions.

This paper studies how large language models (LLMs) make mistakes when extracting social determinants of health (SDOH) from clinical text, especially in drug status prediction. It shows that simple cues—like mentions of alcohol or smoking—can lead to false positive predictions. The study tests several LLMs (including Llama-70B, Llama-8B, Llama3-Med42-70B, Qwen-72B, and Llama‑3.2‑3B) using different methods such as zero-shot, in-context learning, and fine-tuning. The paper also evaluates various strategies to reduce these errors, such as chain-of-thought prompting and warning-based instructions, while pointing out noticeable gender differences in performance. Overall, the work gives useful insights into the challenges of clinical NLP and suggests paths for future research.

The paper investigates how large language models (LLMs) extract social determinants of health (SDOH) from clinical notes, with a focus on drug use time classification using the MIMIC portion of the SHAC dataset. They show superficial cues—such as mentions of alcohol or smoking—can lead to spurious predictions, resulting in high false positive rates and notable gender disparities in model performance. Several mitigation strategies, including chain-of-thought (CoT) reasoning, warning-based instructions, trigger removal experiments, and fine-tuning, are shown.
Summary of strengths: The investigation of spurious correlations of input triggers to LLM outputs on clinical text classification is of great importance, especially when false positives could lead to harmful patient treatment. A great variety of models are experimented.

- Extensive experiments across different LLMs and training methods add strength to the findings.
- Clearly shows that superficial cues (e.g., alcohol or smoking mentions) directly cause false drug use predictions, using trigger removal tests.
- Provides detailed comparisons of different error-reduction strategies, with solid numerical results.
- Highlights important fairness issues by revealing gender-based differences in model performance.

- The paper tackles a well-defined and important problem in clinical NLP—spurious correlations in SDOH extraction—which has direct implications for patient care and fairness.
- The study demonstrates, through several experiments, that LLMs are prone to shortcut learning. It attempts to show the role of superficial triggers using trigger removal experiments and shows consistent gender-based disparities in false positive rates.
- Beyond showing they exist, mitigation strategies are shown with multiple interventions (CoT, warning-based prompts, additional examples, and fine-tuning) are evaluated, which provides a multi-angle view on how to reduce false positives in a clinical setting.
- The section on the potential real-world impact of spurious correlations in clinical decision-making, highlighting how errors might contribute to misdiagnoses or patient stigmatization is concrete.
Summary of weaknesses: It is shown that CoT can reduce false positives, but it is not very clear how CoT exactly helps avoid those triggers. What are CoT results for inputs after removing triggers? Do they also receive lower FPR when using CoT? It could be that CoT helps model elicit reasoning in general, but it is not clear how it helps with particular mentions of word/phrase triggers. The authors may elaborate on this.

If the mentions of substances are substituted by other words, for example, replacing alcohol triggers by “coke”, how would the results be?

How do you quantify the bias or the spurious correlation mentioned in this paper?

- The paper reports improvements in false positive rates across various conditions but fails to provide formal statistical tests (e.g., p-values, confidence intervals) to establish the significance of these differences. Without such validation, it is difficult to assess whether the observed changes are robust or merely due to chance.
- Although the authors expand their experiments and include mitigation strategies like chain-of-thought prompting and warning-based instructions, these approaches yield only modest improvements. The residual high false positive rates suggest that the proposed solutions do not fully address the underlying spurious correlations.
- The study is confined to the MIMIC-III portion of the SHAC dataset. This narrow focus raises concerns about the generalizability of the findings. The results might be dataset-specific, which limits the broader applicability of the conclusions to other clinical or real-world settings.
- While the paper includes error analysis by comparing false positive rates and performing trigger removal experiments, it does not delve deeply into the underlying reasons for the persistent errors. A more detailed interpretability analysis is needed to understand why certain errors continue despite the mitigation efforts.
- The experimental results are presented in lengthy, detailed tables that can be difficult to interpret. This lack of clarity makes it hard for readers to quickly grasp the key findings, potentially reducing the impact and accessibility of the paper’s contributions.
- The Explanation of Revisions PDF shows that they simply convert the previous suggestions of weakness into a list of limitations and plans for future work, without offering clear, concrete improvements in the revised paper.

- While the trigger removal experiments confirm the causal role of superficial cues, the paper does not deeply analyze why specific triggers affect some models more than others. The underlying mechanisms remain underexplored.
- The study primarily focuses on False Positive Rates (FPR) without incorporating complementary metrics such as precision-recall analysis. This leaves a gap in understanding the complete performance profile of the models.
- The experiments rely exclusively on the MIMIC portion of the SHAC dataset. This single-dataset dependency limits the generalizability of the findings to other clinical or non-clinical settings. EHRshot or Coral datasets may be useful here. 
- It would be of value to compare the generative LLM approach with traditional pipeline-based methods (e.g., sequence labeling plus relation classification) or look into a closed-source model like GPT-4 would be of value to understand the full scope of the issue.
- It would be worthwhile to linger on how the chosen annotation strategy (e.g., selection of trigger tokens) impacts the results and if different approaches would yield similar findings.
Comments, suggestions and typos: Table 1 did not report average scores and standard errors over the ICL cases. Did you vary the demonstration examples in the ICL setting and obtain average FPRs?

I would suggest adding more comprehensive ICL settings to make the results more compelling. How would the results change if demonstration examples are randomly selected (not maintaining balanced representations)? 

In addition, can you discuss debiasing techniques leveraging domain expertise to target mitigating the spurious correlation caused by particular word triggers?

see Summary Of Weaknesses

Well written piece
Overall assessment scores: 2.5

2.5

2.5