arXiv subjects

Mansi

Publications and source records attributed to Mansi.

6 recordsLinked to original sources

arXiv2026

Efficient bias mitigation in T2I diffusion models using Concept Graphs

Text-to-Image diffusion models often propagate harmful bias inherited from the training data. Existing bias mitigation techniques typically intervene only at the text encoder or provide inference-time guidance, often leading to generations that collapse into semantically incoherent outputs. To address these limitations, we introduce CO-ALIGN (Concept Ontology Alignment), a novel bias mitigation approach based on concept-graph alignment that operates on the model's internal concept ontology. By aligning concepts within the text encoder and denoiser, CO-ALIGN achieves substantial bias reduction while preserving generative integrity. We demonstrate the effectiveness of concept-graph alignment across three paradigms: text-encoders, denoisers and joint text-denoiser ontology alignment. CO-ALIGN outperforms the state of the art, improving fairness by $30\%$, $\Delta FID=11.4$ in image quality, $2.8\%$ in image fidelity, all while reducing semantically incoherent outputs by $88\%$. Beyond bias mitigation, we show that CO-ALIGN benefits other downstream tasks as well. In particular, our experiments demonstrate that better-aligned internal ontologies enhance concept unlearning robustness across multiple unlearning techniques.

cs.AI↗

arXiv2026

Neurosymbolic Learning for Inference-Time Argumentation

Claim verification is an important problem in high-stakes settings, including health and finance. When information underpinning claims is incomplete or conflicting, uncertain answers may be more appropriate than binary true or false classifications. In all cases, faithful explanations of the considerations determining the final verdict are crucial. We introduce inference-time argumentation (ITA), a trainable neurosymbolic framework for ternary claim verification in which a formal argumentation semantics giving the strength of claims is used both (i) to guide LLM training as models learn to generate arguments and assign them base scores (representing intrinsic strengths) and (ii) to compute ternary (true/false/uncertain) predictions from generated, scored arguments. As a result, at training time, argument generation and scoring can be optimised according to the quality of the induced argumentative predictions. Moreover, at inference time, the final prediction is faithful, by construction, to the arguments and scores determining the verdict, rather than being justified by a potentially unfaithful post-hoc reasoning trace as in conventional reasoning models. We finally show that, on two datasets for ternary claim verification, ITA improves upon argumentative baselines and can perform competitively against non-argumentative direct-prediction baselines, while providing verdicts that are computed deterministically from explicit, inspectable argumentative structures.

cs.AI↗

arXiv2026

Selective Fine-Tuning for Targeted and Robust Concept Unlearning

Text guided diffusion models are used by millions of users, but can be easily exploited to produce harmful content. Concept unlearning methods aim at reducing the models' likelihood of generating harmful content. Traditionally, this has been tackled at an individual concept level, with only a handful of recent works considering more realistic concept combinations. However, state of the art methods depend on full finetuning, which is computationally expensive. Concept localisation methods can facilitate selective finetuning, but existing techniques are static, resulting in suboptimal utility. In order to tackle these challenges, we propose TRUST (Targeted Robust Selective fine Tuning), a novel approach for dynamically estimating target concept neurons and unlearning them through selective finetuning, empowered by a Hessian based regularization. We show experimentally, against a number of SOTA baselines, that TRUST is robust against adversarial prompts, preserves generation quality to a significant degree, and is also significantly faster than the SOTA. Our method achieves unlearning of not only individual concepts but also combinations of concepts and conditional concepts, without any specific regularization.

cs.AI↗

arXiv2025

Understanding Dementia Speech Alignment with Diffusion-Based Image Generation

Text-to-image models generate highly realistic images based on natural language descriptions and millions of users use them to create and share images online. While it is expected that such models can align input text and generated image in the same latent space little has been done to understand whether this alignment is possible between pathological speech and generated images. In this work, we examine the ability of such models to align dementia-related speech information with the generated images and develop methods to explain this alignment. Surprisingly, we found that dementia detection is possible from generated images alone achieving 75% accuracy on the ADReSS dataset. We then leverage explainability methods to show which parts of the language contribute to the detection.

cs.LG↗

arXiv2024

AmalREC: A Dataset for Relation Extraction and Classification Leveraging Amalgamation of Large Language Models

Existing datasets for relation classification and extraction often exhibit limitations such as restricted relation types and domain-specific biases. This work presents a generic framework to generate well-structured sentences from given tuples with the help of Large Language Models (LLMs). This study has focused on the following major questions: (i) how to generate sentences from relation tuples, (ii) how to compare and rank them, (iii) can we combine strengths of individual methods and amalgamate them to generate an even bette quality of sentences, and (iv) how to evaluate the final dataset? For the first question, we employ a multifaceted 5-stage pipeline approach, leveraging LLMs in conjunction with template-guided generation. We introduce Sentence Evaluation Index(SEI) that prioritizes factors like grammatical correctness, fluency, human-aligned sentiment, accuracy, and complexity to answer the first part of the second question. To answer the second part of the second question, this work introduces a SEI-Ranker module that leverages SEI to select top candidate generations. The top sentences are then strategically amalgamated to produce the final, high-quality sentence. Finally, we evaluate our dataset on LLM-based and SOTA baselines for relation classification. The proposed dataset features 255 relation types, with 15K sentences in the test set and around 150k in the train set organized in, significantly enhancing relational diversity and complexity. This work not only presents a new comprehensive benchmark dataset for RE/RC task, but also compare different LLMs for generation of quality sentences from relational tuples.

cs.IR↗

arXiv2023

Rayleigh quotients of Dillon's functions

The Walsh--Hadamard spectrum of a bent function uniquely determines a dual function. The dual of a bent function is also bent. A bent function that is equal to its dual is called a self-dual function. The Hamming distance between a bent function and its dual is related to its Rayleigh quotient. Carlet, Danielsen, Parker, and Sole studied Rayleigh quotients of bent functions in ${\mathcal PS}_{ap}$, and obtained an expression in terms of a character sum. We use another approach comprising of Desarguesian spreads to obtain the complete spectrum of Rayleigh quotients of bent functions in $\mathcal{PS}_{ap}$.

cs.DM↗