Related papers: Implicit Bias-Like Patterns in Reasoning Models

Related papers

Is Reasoning All You Need? Probing Bias in the Age of Reasoning Language Models [0.0]
Reasoning Language Models (RLMs) have gained traction for their ability to perform complex, multi-step reasoning tasks.<n>While these capabilities promise improved reliability, their impact on robustness to social biases remains unclear.<n>We leverage the CLEAR-Bias benchmark to investigate the adversarial robustness of RLMs to bias elicitation.
arXiv Detail & Related papers (2025-07-03T17:01:53Z)
Biases Propagate in Encoder-based Vision-Language Models: A Systematic Analysis From Intrinsic Measures to Zero-shot Retrieval Outcomes [14.331322509462419]
Social-group biases intrinsic to foundational encoder-based vision-language models (VLMs) manifest in biases in downstream tasks.<n>We introduce a controlled framework to measure this propagation by correlating intrinsic measures of bias in the representational space with measures of bias in zero-shot text-to-image (TTI) and image-to-text (ITT) retrieval.<n>Results show substantial correlations between intrinsic and extrinsic bias, with an average $rho$ = 0.83 $pm$ 0.10.<n> Notably, we find that larger/better-performing models exhibit greater bias propagation, a finding that raises concerns
arXiv Detail & Related papers (2025-06-06T20:01:32Z)
BiasConnect: Investigating Bias Interactions in Text-to-Image Models [73.76853483463836]
We introduce BiasConnect, a novel tool designed to analyze and quantify bias interactions in Text-to-Image models.<n>Our method provides empirical estimates that indicate how other bias dimensions shift toward or away from an ideal distribution when a given bias is modified.<n>We demonstrate the utility of BiasConnect for selecting optimal bias mitigation axes, comparing different TTI models on the dependencies they learn, and understanding the amplification of intersectional societal biases in TTI models.
arXiv Detail & Related papers (2025-03-12T19:01:41Z)
Does Reasoning Introduce Bias? A Study of Social Bias Evaluation and Mitigation in LLM Reasoning [17.86131226260848]
We present the first systematic evaluation of social bias within large language models (LLMs)<n>We quantify how biased reasoning steps correlate with incorrect predictions and often lead to stereotype expression.<n>We propose Answer Distribution as Bias Proxy (ADBP), a lightweight mitigation method that detects bias by tracking how model predictions change.
arXiv Detail & Related papers (2025-02-21T10:16:07Z)
Self-supervised Analogical Learning using Language Models [59.64260218737556]
We propose SAL, a self-supervised analogical learning framework.<n> SAL mimics the human analogy process and trains models to explicitly transfer high-quality symbolic solutions.<n>We show that the resulting models outperform base language models on a wide range of reasoning benchmarks.
arXiv Detail & Related papers (2025-02-03T02:31:26Z)
No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs [56.78271181959529]
This paper proposes a conceptual shift to modeling low-dimensional dynamical systems by departing from the traditional two-step modeling process. Instead of first discovering a closed-form equation and then analyzing it, our approach, direct semantic modeling, predicts the semantic representation of the dynamical system. Our approach not only simplifies the modeling pipeline but also enhances the transparency and flexibility of the resulting models.
arXiv Detail & Related papers (2025-01-30T18:36:48Z)
On the Reasoning Capacity of AI Models and How to Quantify It [0.0]
Large Language Models (LLMs) have intensified the debate surrounding the fundamental nature of their reasoning capabilities.<n>While achieving high performance on benchmarks such as GPQA and MMLU, these models exhibit limitations in more complex reasoning tasks.<n>We propose a novel phenomenological approach that goes beyond traditional accuracy metrics to probe the underlying mechanisms of model behavior.
arXiv Detail & Related papers (2025-01-23T16:58:18Z)
How far can bias go? -- Tracing bias from pretraining data to alignment [54.51310112013655]
This study examines the correlation between gender-occupation bias in pre-training data and their manifestation in LLMs.<n>Our findings reveal that biases present in pre-training data are amplified in model outputs.
arXiv Detail & Related papers (2024-11-28T16:20:25Z)
Investigating Implicit Bias in Large Language Models: A Large-Scale Study of Over 50 LLMs [0.0]
Large Language Models (LLMs) are being adopted across a wide range of tasks. Recent research indicates that LLMs can harbor implicit biases even when they pass explicit bias evaluations. This study highlights that newer or larger language models do not automatically exhibit reduced bias.
arXiv Detail & Related papers (2024-10-13T03:43:18Z)
Large Language Models as an Indirect Reasoner: Contrapositive and Contradiction for Automated Reasoning [74.90592233107712]
We propose a Direct-Indirect Reasoning (DIR) method, which considers Direct Reasoning (DR) and Indirect Reasoning (IR) as multiple parallel reasoning paths that are merged to derive the final answer.<n>Our DIR method is simple yet effective and can be straightforwardly integrated with existing variants of CoT methods.
arXiv Detail & Related papers (2024-02-06T03:41:12Z)
Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes [73.12947922129261]
We leverage the zero-shot capabilities of large language models to reduce stereotyping. We show that self-debiasing can significantly reduce the degree of stereotyping across nine different social groups. We hope this work opens inquiry into other zero-shot techniques for bias mitigation.
arXiv Detail & Related papers (2024-02-03T01:40:11Z)
Investigating Bias Representations in Llama 2 Chat via Activation Steering [0.0]
We use activation steering to probe for and mitigate biases related to gender, race, and religion. Our findings reveal inherent gender bias in Llama 2 7B Chat, persisting even after Reinforcement Learning from Human Feedback. This work also provides valuable insights into effective red-teaming strategies for Large Language Models.
arXiv Detail & Related papers (2024-02-01T07:48:50Z)
Exploring the Jungle of Bias: Political Bias Attribution in Language Models via Dependency Analysis [86.49858739347412]
Large Language Models (LLMs) have sparked intense debate regarding the prevalence of bias in these models and its mitigation. We propose a prompt-based method for the extraction of confounding and mediating attributes which contribute to the decision process. We find that the observed disparate treatment can at least in part be attributed to confounding and mitigating attributes and model misalignment.
arXiv Detail & Related papers (2023-11-15T00:02:25Z)
Are Neural Topic Models Broken? [81.15470302729638]
We study the relationship between automated and human evaluation of topic models. We find that neural topic models fare worse in both respects compared to an established classical method.
arXiv Detail & Related papers (2022-10-28T14:38:50Z)
General Greedy De-bias Learning [163.65789778416172]
We propose a General Greedy De-bias learning framework (GGD), which greedily trains the biased models and the base model like gradient descent in functional space. GGD can learn a more robust base model under the settings of both task-specific biased models with prior knowledge and self-ensemble biased model without prior knowledge.
arXiv Detail & Related papers (2021-12-20T14:47:32Z)
Interpretable Data-Based Explanations for Fairness Debugging [7.266116143672294]
Gopher is a system that produces compact, interpretable, and causal explanations for bias or unexpected model behavior. We introduce the concept of causal responsibility that quantifies the extent to which intervening on training data by removing or updating subsets of it can resolve the bias. Building on this concept, we develop an efficient approach for generating the top-k patterns that explain model bias.
arXiv Detail & Related papers (2021-12-17T20:10:00Z)
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization [93.8373619657239]
Neural networks trained with SGD were recently shown to rely preferentially on linearly-predictive features. This simplicity bias can explain their lack of robustness out of distribution (OOD) We demonstrate that the simplicity bias can be mitigated and OOD generalization improved.
arXiv Detail & Related papers (2021-05-12T12:12:24Z)
Learning from others' mistakes: Avoiding dataset biases without modeling them [111.17078939377313]
State-of-the-art natural language processing (NLP) models often learn to model dataset biases and surface form correlations instead of features that target the intended task. Previous work has demonstrated effective methods to circumvent these issues when knowledge of the bias is available. We show a method for training models that learn to ignore these problematic correlations.
arXiv Detail & Related papers (2020-12-02T16:10:54Z)
Debiasing Skin Lesion Datasets and Models? Not So Fast [17.668005682385175]
Models learned from data risk learning biases from that same data. When models learn spurious correlations not found in real-world situations, their deployment for critical tasks, such as medical decisions, can be catastrophic. We find out that, despite interesting results that point to promising future research, current debiasing methods are not ready to solve the bias issue for skin-lesion models.
arXiv Detail & Related papers (2020-04-23T21:07:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.