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As pre-trained language models grow in size, full fine-tuning their parameters on task adaptation data becomes increasingly impractical. To … (voir plus)address this challenge, some methods for low-rank adaptation of language models have been proposed, e.g. LoRA, which incorporates trainable low-rank decomposition matrices into only some parameters of the pre-trained model, called adapters. This approach significantly reduces the number of trainable parameters compared to fine-tuning all parameters or adapters. In this work, we look at low-rank adaptation method from the lens of data privacy. We show theoretically that the low-rank adaptation used in LoRA is equivalent to fine-tuning adapters with noisy batch gradients - just like what DPSGD algorithm does. We also quantify the variance of the injected noise as a decreasing function of adaptation rank. By establishing a Berry-Esseen type bound on the total variation distance between the injected noise distribution and a Gaussian noise distribution with the same variance, we show that the dynamics of low-rank adaptation is very close to when DPSGD is performed w.r.t the adapters. Following our theoretical findings and approved by our experimental results, we show that low-rank adaptation provides robustness to membership inference attacks w.r.t the fine-tuning data.
Large Language models (LLMs) have demonstrated impressive performance on a wide range of tasks, including in multimodal settings such as spe… (voir plus)ech. However, their evaluation is often limited to English and a few high-resource languages. For low-resource languages, there is no standardized evaluation benchmark. In this paper, we address this gap by introducing mSTEB, a new benchmark to evaluate the performance of LLMs on a wide range of tasks covering language identification, text classification, question answering, and translation tasks on both speech and text modalities. We evaluated the performance of leading LLMs such as Gemini 2.0 Flash and GPT-4o (Audio) and state-of-the-art open models such as Qwen 2 Audio and Gemma 3 27B. Our evaluation shows a wide gap in performance between high-resource and low-resource languages, especially for languages spoken in Africa and Americas/Oceania. Our findings show that more investment is needed to address their under-representation in LLMs coverage.
Large Language models (LLMs) have demonstrated impressive performance on a wide range of tasks, including in multimodal settings such as spe… (voir plus)ech. However, their evaluation is often limited to English and a few high-resource languages. For low-resource languages, there is no standardized evaluation benchmark. In this paper, we address this gap by introducing mSTEB, a new benchmark to evaluate the performance of LLMs on a wide range of tasks covering language identification, text classification, question answering, and translation tasks on both speech and text modalities. We evaluated the performance of leading LLMs such as Gemini 2.0 Flash and GPT-4o (Audio) and state-of-the-art open models such as Qwen 2 Audio and Gemma 3 27B. Our evaluation shows a wide gap in performance between high-resource and low-resource languages, especially for languages spoken in Africa and Americas/Oceania. Our findings show that more investment is needed to address their under-representation in LLMs coverage.
As AI systems become increasingly embedded in human decision-making process, aligning their behavior with human values is critical to ensuri… (voir plus)ng safe and trustworthy deployment. A central approach to AI Alignment called Imitation Learning (IL), trains a learner to directly mimic desirable human behaviors from expert demonstrations. However, standard IL methods assume that (1) experts act to optimize expected returns; (2) expert policies are Markovian. Both assumptions are inconsistent with empirical findings from behavioral economics, according to which humans are (1) risk-sensitive; and (2) make decisions based on past experience. In this work, we examine the implications of risk sensitivity for IL and show that standard approaches do not capture all optimal policies under risk-sensitive decision criteria. By characterizing these expert policies, we identify key limitations of existing IL algorithms in replicating expert performance in risk-sensitive settings. Our findings underscore the need for new IL frameworks that account for both risk-aware preferences and temporal dependencies to faithfully align AI behavior with human experts.
Large Language Models (LLMs) have become vital tools in software development tasks such as code generation, completion, and analysis. As the… (voir plus)ir integration into workflows deepens, ensuring robustness against vulnerabilities especially those triggered by diverse or adversarial inputs becomes increasingly important. Such vulnerabilities may lead to incorrect or insecure code generation when models encounter perturbed task descriptions, code, or comments. Prior research often overlooks the role of natural language in guiding code tasks. This study investigates how adversarial perturbations in natural language inputs including prompts, comments, and descriptions affect LLMs for Code (LLM4Code). It examines the effects of perturbations at the character, word, and sentence levels to identify the most impactful vulnerabilities. We analyzed multiple projects (e.g., ReCode, OpenAttack) and datasets (e.g., HumanEval, MBPP), establishing a taxonomy of adversarial attacks. The first dimension classifies the input type code, prompts, or comments while the second dimension focuses on granularity: character, word, or sentence-level changes. We adopted a mixed-methods approach, combining quantitative performance metrics with qualitative vulnerability analysis. LLM4Code models show varying robustness across perturbation types. Sentence-level attacks were least effective, suggesting models are resilient to broader contextual changes. In contrast, word-level perturbations posed serious challenges, exposing semantic vulnerabilities. Character-level effects varied, showing model sensitivity to subtle syntactic deviations.Our study offers a structured framework for testing LLM4Code robustness and emphasizes the critical role of natural language in adversarial evaluation. Improving model resilience to semantic-level disruptions is essential for secure and reliable code-generation systems.
Large language models (LLMs) often struggle with context fidelity, producing inconsistent answers when responding to questions based on prov… (voir plus)ided information. Existing approaches either rely on expensive supervised fine-tuning to generate evidence post-answer or train models to perform web searches without necessarily improving utilization of the given context. We propose CARE, a novel native retrieval-augmented reasoning framework that teaches LLMs to explicitly integrate in-context evidence within their reasoning process with the model's own retrieval capabilities. Our method requires minimal labeled evidence data while significantly enhancing both retrieval accuracy and answer generation performance through strategically retrieved in-context tokens in the reasoning chain. Extensive experiments on multiple real-world and counterfactual QA benchmarks demonstrate that our approach substantially outperforms supervised fine-tuning, traditional retrieval-augmented generation methods, and external retrieval solutions. This work represents a fundamental advancement in making LLMs more accurate, reliable, and efficient for knowledge-intensive tasks.
