Generalizing Large Language Model Usability Across Resource-Constrained

• URL: http://arxiv.org/abs/2505.17040v1
• Date: Tue, 13 May 2025 01:00:12 GMT
• Title: Generalizing Large Language Model Usability Across Resource-Constrained
• Authors: Yun-Da Tsai,
• Abstract summary: dissertation presents a systematic study toward generalizing Large Language Models under real-world constraints.<n>First, it introduces a robust text-centric alignment framework that enables LLMs to seamlessly integrate diverse modalities.<n>Beyond multimodal setting, the dissertation investigates inference-time optimization strategies for LLMs.
• Score: 0.43512163406552007
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have achieved remarkable success across a wide range of natural language tasks, and recent efforts have sought to extend their capabilities to multimodal domains and resource-constrained environments. However, existing approaches often rely on costly supervised fine-tuning or assume fixed training conditions, limiting their generalization when facing unseen modalities, limited data, or restricted compute resources. This dissertation presents a systematic study toward generalizing LLM usability under real-world constraints. First, it introduces a robust text-centric alignment framework that enables LLMs to seamlessly integrate diverse modalities-including text, images, tables, and any modalities - via natural language interfaces. This approach supports in-context adaptation to unseen or dynamically changing modalities without requiring retraining. To enhance robustness against noisy and missing modalities, an adversarial prompting technique is proposed, generating semantically challenging perturbations at the prompt level to stress-test model reliability. Beyond multimodal setting, the dissertation investigates inference-time optimization strategies for LLMs, leveraging prompt search and uncertainty quantification to improve performance without additional model training. This perspective offers an efficient alternative to scaling model parameters or retraining from scratch. Additionally, the work addresses low-resource domains such as Verilog code generation by designing correct-by-construction synthetic data pipelines and logic-enhanced reasoning models, achieving state-of-the-art performance with minimal data. Together, these contributions form a unified effort to enhance the adaptability, scalability, and efficiency of large language models under practical constraints.

Related papers

• Transferable Modeling Strategies for Low-Resource LLM Tasks: A Prompt and Alignment-Based Approach [1.3286097954612326]
  This paper addresses the limited transfer and adaptation capabilities of large language models in low-resource language scenarios.<n>It proposes a unified framework that combines a knowledge transfer module with parameter-efficient fine-tuning strategies.<n>It enhances task-specific adaptability while preserving the general capabilities of large language models.
  arXiv  Detail & Related papers  (2025-07-01T09:34:49Z)
• Large Language Models Post-training: Surveying Techniques from Alignment to Reasoning [185.51013463503946]
  Large Language Models (LLMs) have fundamentally transformed natural language processing, making them indispensable across domains ranging from conversational systems to scientific exploration.<n>These challenges necessitate advanced post-training language models (PoLMs) to address shortcomings, such as restricted reasoning capacities, ethical uncertainties, and suboptimal domain-specific performance.<n>This paper presents the first comprehensive survey of PoLMs, systematically tracing their evolution across five core paradigms: Fine-tuning, which enhances task-specific accuracy; Alignment, which ensures ethical coherence and alignment with human preferences; Reasoning, which advances multi-step inference despite challenges in reward design; Integration and Adaptation, which
  arXiv  Detail & Related papers  (2025-03-08T05:41:42Z)
• Adaptive Resource Allocation Optimization Using Large Language Models in Dynamic Wireless Environments [25.866960634041092]
  Current solutions rely on domain-specific architectures or techniques, and a general DL approach for constrained optimization remains undeveloped.<n>We propose a large language model for resource allocation (LLM-RAO) to address the complex resource allocation problem while adhering to constraints.<n>LLM-RAO achieves up to a 40% performance enhancement compared to conventional DL methods and up to an $80$% improvement over analytical approaches.
  arXiv  Detail & Related papers  (2025-02-04T12:56:59Z)
• Efficient Domain Adaptation of Multimodal Embeddings using Constrastive Learning [0.08192907805418582]
  Current approaches either yield subpar results when using pretrained models without task-specific adaptation, or require substantial computational resources for fine-tuning.<n>We propose a novel method for adapting foundational, multimodal embeddings to downstream tasks, without the need of expensive fine-tuning processes.
  arXiv  Detail & Related papers  (2025-02-04T06:30:12Z)
• Optimal Query Allocation in Extractive QA with LLMs: A Learning-to-Defer Framework with Theoretical Guarantees [3.4289478404209826]
  Large Language Models excel in generative tasks but exhibit inefficiencies in structured text selection.<n>We propose a Learning-to-Defer framework that allocates queries to specialized experts, ensuring high-confidence predictions.
  arXiv  Detail & Related papers  (2024-10-21T08:21:00Z)
• Unlocking the Potential of Model Merging for Low-Resource Languages [66.7716891808697]
  Adapting large language models to new languages typically involves continual pre-training (CT) followed by supervised fine-tuning (SFT) We propose model merging as an alternative for low-resource languages, combining models with distinct capabilities into a single model without additional training. Experiments based on Llama-2-7B demonstrate that model merging effectively endows LLMs for low-resource languages with task-solving abilities, outperforming CT-then-SFT in scenarios with extremely scarce data.
  arXiv  Detail & Related papers  (2024-07-04T15:14:17Z)
• Scalable Language Model with Generalized Continual Learning [58.700439919096155]
  The Joint Adaptive Re-ization (JARe) is integrated with Dynamic Task-related Knowledge Retrieval (DTKR) to enable adaptive adjustment of language models based on specific downstream tasks. Our method demonstrates state-of-the-art performance on diverse backbones and benchmarks, achieving effective continual learning in both full-set and few-shot scenarios with minimal forgetting.
  arXiv  Detail & Related papers  (2024-04-11T04:22:15Z)
• Text-centric Alignment for Multi-Modality Learning [3.6961400222746748]
  We propose the Text-centric Alignment for Multi-Modality Learning (TAMML) approach. By leveraging the unique properties of text as a unified semantic space, TAMML demonstrates significant improvements in handling unseen, diverse, and unpredictable modality combinations. This study contributes to the field by offering a flexible, effective solution for real-world applications where modality availability is dynamic and uncertain.
  arXiv  Detail & Related papers  (2024-02-12T22:07:43Z)
• Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement [67.1393112206885]
  Large Language Models (LLMs) have shown promise as intelligent agents in interactive decision-making tasks. We introduce Entropy-Regularized Token-level Policy Optimization (ETPO), an entropy-augmented RL method tailored for optimizing LLMs at the token level. We assess the effectiveness of ETPO within a simulated environment that models data science code generation as a series of multi-step interactive tasks.
  arXiv  Detail & Related papers  (2024-02-09T07:45:26Z)
• Solution-oriented Agent-based Models Generation with Verifier-assisted Iterative In-context Learning [10.67134969207797]
  Agent-based models (ABMs) stand as an essential paradigm for proposing and validating hypothetical solutions or policies. Large language models (LLMs) encapsulating cross-domain knowledge and programming proficiency could potentially alleviate the difficulty of this process. We present SAGE, a general solution-oriented ABM generation framework designed for automatic modeling and generating solutions for targeted problems.
  arXiv  Detail & Related papers  (2024-02-04T07:59:06Z)
• Amortizing intractable inference in large language models [56.92471123778389]
  We use amortized Bayesian inference to sample from intractable posterior distributions. We empirically demonstrate that this distribution-matching paradigm of LLM fine-tuning can serve as an effective alternative to maximum-likelihood training. As an important application, we interpret chain-of-thought reasoning as a latent variable modeling problem.
  arXiv  Detail & Related papers  (2023-10-06T16:36:08Z)
• Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation [87.98063273826702]
  We propose a memory imitation meta-learning (MemIML) method that enhances the model's reliance on support sets for task adaptation. A theoretical analysis is provided to prove the effectiveness of our method.
  arXiv  Detail & Related papers  (2022-03-22T12:41:55Z)

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

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.