Title: Boosting Resilience of Large Language Models through Causality-Driven Robust Optimization
Venue: NeurIPS 2025
Accepted: yes
Keywords: Large language models

Spurious correlations

Hallucination

Knowledge localization

Logistic regression model

Policy optimization
Abstract: Large language models (LLMs) have achieved remarkable achievements across diverse applications; however, they remain plagued by spurious correlations and the generation of hallucinated content. Despite extensive efforts to enhance the resilience of LLMs, existing approaches either rely on indiscriminate fine-tuning of all parameters, resulting in parameter inefficiency and lack of specificity, or depend on post-processing techniques that offer limited adaptability and flexibility. This study introduces a novel Causality-driven Robust Optimization (CdRO) approach that selectively updates model components sensitive to causal reasoning, enhancing model causality while preserving valuable pretrained knowledge to mitigate overfitting. Our method begins by identifying the parameter components within LLMs that capture causal relationships, achieved through comparing the training dynamics of parameter matrices associated with the original samples, as well as augmented counterfactual and paraphrased variants. These comparisons are then fed into a lightweight logistic regression model, optimized in real time to dynamically identify and adapt the causal components within LLMs. The identified parameters are subsequently optimized using an enhanced policy optimization algorithm, where the reward function is designed to jointly promote both model generalization and robustness. Extensive experiments across various tasks using twelve different LLMs demonstrate the superior performance of our framework, underscoring its significant effectiveness in reducing the model’s dependence on spurious associations and mitigating hallucinations.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe manuscript presents a novel Causality-driven Robust Optimization (CDRO) approach that selectively updates model components sensitive to causal reasoning. Extensive experiments across various tasks and LLMs architectures demonstrate the superior performance of the proposed method.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. CdRO addresses a critical challenge in LLM development: enhancing causal reasoning without extensive retraining or parameter inefficiency.\n2. There is a clear problem formulation that effectively motivates the need for parameter-efficient causal reasoning enhancement.\n3. The manuscript has a well-structured presentation with logical flow from theoretical foundations to implementation details and experimental results.\n\nWeaknesses:\n1. The explanation of how loss gradients capture model sensitivity needs more intuitive examples or visualizations.\n2. The computational overhead introduced by the knowledge localization process could present scalability challenges for very large models, though this is partially mitigated by the parameter efficiency.\n3. There is limited discussion of potential negative effects or failure cases, in which the approach might underperform relative to baselines.\n4. How stable is the identification of causal components across different runs or different datasets? Is there consistency in which components are identified as causality-sensitive?\n\nQUESTIONS:\n1. How was the threshold determined for selecting causal components? Specifically, the appendix mentions selecting matrices with \"predicted probabilities exceeding the average predicted probability,\" but is this optimal? Were other selection strategies considered?\n2. How do you verify that the identified components truly encode causal relationships rather than other statistical patterns? Could you provide ablation studies or visualization evidence showing that these components specifically capture causal rather than correlational information?\n3. How do the training time and computational requirements compare between CDRO and other methods? The dual optimization process (updating both the logistic regression model and the LLM parameters) may introduce computational complexity.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-26"}

comments: SUMMARY:
This paper focuses on addressing the hallucination issue in LLMs by optimizing causal relations. The approach first identifies causal knowledge and then optimizes it using reinforcement learning. Experiments show that it performs well on several NLU tasks.

STRENGTHS AND WEAKNESSES:
**Strengths:**
1. It is an important problem to solve: avoid the hallucination where the output is based on spurious relations
2. The writing is generally clear and the figure is informative

**Weaknesses:**

1. The "locate and optimize" strategy has been widely used in the continual learning community [1, 2, 3]. It would be helpful if the authors could compare their approach with these existing methods to better situate their contribution.
2. The authors should consider applying their method to LLMs and evaluating it on more complex tasks. The current models and datasets listed in Table 1 are too simple (i.e., SoTA LLM can easily achieve 90%+ acc.), which limits the informativeness of the evaluation.

[1]: Continual Pre-training of Language Models, ICLR 2023.    
[2]: Locating and Editing Factual Associations in GPT, NeurIPS 2022.    
[3]: Continual learning of natural language processing tasks: A survey， 2022

QUESTIONS:
1. I think "enhancing model causality while preserving valuable pretrained knoweldge" is closedly related to continual leanring. I strongly recomment the author to add a related work section on continual leanrning (see above W1 and potential reference)

2. Could you expand the evaluation? The current evaluation is relatively simple and could benefit from broader or more challenging benchmarks (see W2 above).

LIMITATIONS:
No discussion in the main content. Perhaps the author can add some cost analysis
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis study introduces a novel Causality-driven Robust Optimization (CDRO) approach to enhance the causal reasoning abilities of Large Language Models (LLMs), reducing their reliance on spurious correlations and mitigating hallucinations. CDRO first identifies causality-sensitive parameters in LLMs by comparing training dynamics across original samples, augmented counterfactual samples, and paraphrased variants, using a lightweight logistic regression model. These identified parameters are then optimized with an enhanced policy optimization algorithm, whose reward function promotes both generalization and robustness. Extensive experiments with twelve different LLMs across various tasks demonstrate CDRO's superiority in reducing spurious associations, mitigating hallucinations, alleviating biases, and improving out-of-distribution generalization. The key contributions include a new method for localizing causal knowledge in LLMs, a collaborative optimization framework, and validation of effectiveness through comprehensive experiments.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**:\n- Effectively enhances LLMs' causal reasoning abilities, reducing reliance on spurious correlations and mitigating hallucinations across various tasks .\n- Outperforms baselines in debiasing (gender and race) and improving OOD generalization, with superior performance on both NLU and NLG tasks.\n- Adopts a parameter-efficient optimization strategy, preserving pretrained knowledge while enhancing downstream task performance .\n- The collaborative optimization framework (logistic regression + enhanced REINFORCE++) enables dynamic localization of causal components, ensuring adaptability .\n- Comprehensive experiments with 12 LLMs validate its robustness and generalizability .\n\n**Weaknesses**:\n- Introduces additional time complexity due to the adaptive knowledge localization module, though the trade-off is justified by performance gains .\n- The effectiveness of causal component localization heavily depends on the quality of counterfactual and paraphrased samples, posing challenges in data augmentation. Moreover, there is a lack of ablation experiments on counterfactual and paraphrased samples, making the significance of paraphrased samples questionable.\n- The layout of tables and figures is suboptimal. In the appendix, the places where figures and tables are cited are far from their actual positions, which may cause confusion for readers.\n- The description of parameter updates for the selected model components is not clear enough, with only limited information provided in the flowcharts.\n\nQUESTIONS:\n1. Given that the study uses prompted LLMs to score constructed counterfactual and paraphrased samples, could this LLM-based evaluation method inherit the inherent biases or limitations of the evaluating models, leading to inconsistent or inaccurate assessments of sample quality? And might the reliance on such model-in-the-loop scoring—especially when using the same or similar LLMs for both generation and evaluation—create closed-loop biases that fail to align with human judgment or real-world task requirements, thereby undermining the effectiveness of the augmented data?\t\n2. Given that the study claims to selectively update Causality-Sensitive Parameters rather than all model parameters, several questions arise regarding the clarity of this process: How exactly are the \"causality-sensitive\" characteristics of these parameters operationally defined beyond the mentioned gradient differences and cosine similarities of hidden states? Is there empirical evidence to confirm that the localized parameters indeed encode causal relationships rather than other types of task-relevant information? Additionally, how is the threshold for selecting these parameters (e.g., \"matrices with predicted probabilities exceeding the average\") determined, and does this threshold remain robust across different model architectures or task domains? Finally, since only specific parameter components are updated, is there a risk of creating imbalances in the model's internal representations, and how does the framework mitigate such potential side effects?\n3. Given that CDRO aims to enhance causal reasoning by selectively optimizing parameters identified through comparisons of training dynamics across original, counterfactual, and paraphrased samples, how does the framework account for the possibility that causal relationships in real-world scenarios are often complex, multi-faceted, or context-dependent—far more intricate than the binary or simplified causal signals captured by the generated counterfactuals? Moreover, could the reliance on predefined types of augmented samples (counterfactuals and paraphrases) limit the model’s ability to learn causal structures that cannot be easily approximated by such perturbations, and if so, how might the framework be adapted to capture more nuanced causal dependencies?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new robust optimization framework based on causality, called CDRO, aiming to reduce LLMS 'reliance on false correlations and enhance their resilience across various tasks.   Specifically, the method proposed in this work first identifies the parameter components that capture causal relationships by analyzing the training dynamics in the weight matrices of the original samples, counterfactual samples, and paraphrase samples.   These dynamics are modeled through the logistic regression mechanism, making it possible to automatically and adaptively locate the parameters related to causal relationships.   Meanwhile, to further improve and optimize the process, the author introduced a collaborative reinforcement learning strategy, alternately updating the identified causal parameters and the logistic regression model.   A large number of experiments on various NLU and NLG tasks have shown that CDRO consistently outperforms the comparison baseline in reducing false correlations, suppressing knowledge illusions, and improving overall model performance.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths The motivation of this paper is clear and the writing is concise and of high quality.   The experimental section is relatively comprehensive, with extensive evaluations conducted on the proposed method.   Additionally, the method has been compared with various baseline approaches, further highlighting its effectiveness.\n\nWeaknesses 1.  The author mentioned that LLMS have counterfactual issues.   However, during the data construction stage, prompt LLMS are used for data generation.   Does this mean that the data quality depends on the LLM itself?   In other words, how can we ensure that the generated data is trustworthy?\n \n2.  The author mentioned that other methods have fine-tuned all the parameters of the model, but the method proposed by the author only fine-tunes some parameters.   Efficient parameter fine-tuning methods are common.   What are the general implementation methods of other baselines like?   Is there really no method that only fine-tunes some parameters?\n\n3.  In the experimental section, why weren't all the baselines compared on some datasets or some models?\n\n4.   Has the author conducted experiments on datasets for hallucinations?   Hallucination evaluation on the NLG task may not well reflect the hallucination problem of the model.\n\nQUESTIONS:\nMy question is recorded in the weakness section\n\nLIMITATIONS:\nRefer to the weakness.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}