Representing LLMs in Prompt Semantic Task Space

• URL: http://arxiv.org/abs/2509.22506v1
• Date: Fri, 26 Sep 2025 15:48:10 GMT
• Title: Representing LLMs in Prompt Semantic Task Space
• Authors: Idan Kashani, Avi Mendelson, Yaniv Nemcovsky,
• Abstract summary: Large language models (LLMs) achieve impressive results over various tasks.<n>Identifying the best-performing LLM for a given task is a significant challenge.<n>This work presents an efficient, training-free approach to representing LLMs as linear operators within the prompts' semantic task space.
• Score: 0.1784233255402269
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large language models (LLMs) achieve impressive results over various tasks, and ever-expanding public repositories contain an abundance of pre-trained models. Therefore, identifying the best-performing LLM for a given task is a significant challenge. Previous works have suggested learning LLM representations to address this. However, these approaches present limited scalability and require costly retraining to encompass additional models and datasets. Moreover, the produced representation utilizes distinct spaces that cannot be easily interpreted. This work presents an efficient, training-free approach to representing LLMs as linear operators within the prompts' semantic task space, thus providing a highly interpretable representation of the models' application. Our method utilizes closed-form computation of geometrical properties and ensures exceptional scalability and real-time adaptability to dynamically expanding repositories. We demonstrate our approach on success prediction and model selection tasks, achieving competitive or state-of-the-art results with notable performance in out-of-sample scenarios.

Related papers

• Language Models Struggle to Use Representations Learned In-Context [20.328647653504184]
  This study investigates whether large language models can use in-context representations to complete simple downstream tasks.<n>We find evidence that open-weights LLMs struggle to deploy representations of novel semantics that are defined in-context.<n>Overall, this work seeks to inspire novel methods for encouraging models to not only encode information presented in-context, but to do so in a manner that supports flexible deployment of this information.
  arXiv  Detail & Related papers  (2026-02-04T04:55:07Z)
• WeMMU: Enhanced Bridging of Vision-Language Models and Diffusion Models via Noisy Query Tokens [69.97021957331326]
  We propose Noisy Query Tokens, which learn a distributed representation space between the VLM and Diffusion Model via end-to-end optimization.<n>We also introduce a VAE branch with linear projection to recover fine-grained image details.
  arXiv  Detail & Related papers  (2025-12-02T09:02:20Z)
• Leveraging In-Context Learning for Language Model Agents [51.2996117207114]
  In-context learning (ICL) with dynamically selected demonstrations combines the flexibility of prompting large language models (LLMs) with the ability to leverage training data to improve performance.<n>We show that set-selection of trajectories of similar tasks as demonstrations significantly improves performance, reliability, robustness, and efficiency of LLM agents.<n>We find that demonstrations obtained from larger models (in the annotation phase) also improve smaller models, and that ICL agents can even rival costlier trained agents.
  arXiv  Detail & Related papers  (2025-06-16T05:37:49Z)
• Large Language Models as Attribution Regularizers for Efficient Model Training [0.0]
  Large Language Models (LLMs) have demonstrated remarkable performance across diverse domains.<n>We introduce a novel yet straightforward method for incorporating LLM-generated global task feature attributions into the training process of smaller networks.<n>Our approach yields superior performance in few-shot learning scenarios.
  arXiv  Detail & Related papers  (2025-02-27T16:55:18Z)
• Scaling Autonomous Agents via Automatic Reward Modeling And Planning [52.39395405893965]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of tasks.<n>However, they still struggle with problems requiring multi-step decision-making and environmental feedback.<n>We propose a framework that can automatically learn a reward model from the environment without human annotations.
  arXiv  Detail & Related papers  (2025-02-17T18:49:25Z)
• Improving Small-Scale Large Language Models Function Calling for Reasoning Tasks [0.8425561594225592]
  This study introduces a novel framework for training smaller language models in function calling. It focuses on specific logical and mathematical reasoning tasks. The approach aims to improve performances of small-scale models for these tasks using function calling.
  arXiv  Detail & Related papers  (2024-10-24T16:27:35Z)
• EVOLvE: Evaluating and Optimizing LLMs For In-Context Exploration [76.66831821738927]
  Large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty.<n>We measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications.<n>Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs.
  arXiv  Detail & Related papers  (2024-10-08T17:54:03Z)
• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• MetaGPT: Merging Large Language Models Using Model Exclusive Task Arithmetic [6.46176287368784]
  We propose textbfModel textbfExclusive textbfTask textbfArithmetic for merging textbfGPT-scale models. Our proposed MetaGPT is data-agnostic and bypasses the heavy search process, making it cost-effective and easy to implement for LLMs.
  arXiv  Detail & Related papers  (2024-06-17T10:12:45Z)
• Towards Modeling Learner Performance with Large Language Models [7.002923425715133]
  This paper investigates whether the pattern recognition and sequence modeling capabilities of LLMs can be extended to the domain of knowledge tracing. We compare two approaches to using LLMs for this task, zero-shot prompting and model fine-tuning, with existing, non-LLM approaches to knowledge tracing. While LLM-based approaches do not achieve state-of-the-art performance, fine-tuned LLMs surpass the performance of naive baseline models and perform on par with standard Bayesian Knowledge Tracing approaches.
  arXiv  Detail & Related papers  (2024-02-29T14:06:34Z)
• 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)

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.