数据来源：ArXiv Domain LLM Domain Papers1. Bridging the Editing Gap in LLMs: FineEdit for Precise and Targeted Text ModificationsLarge Language Models (LLMs) have significantly advanced natural language processing, demonstrating strong capabilities in tasks such as text generation, summarization, and reasoning. Recently, their potential for automating precise text editing tasks across specialized domains, such as programming code, LaTeX, and structured database languages, has gained attention. Howe ...

数据来源：ArXiv Domain LLM Domain Papers1. 11Plus-Bench: Demystifying Multimodal LLM Spatial Reasoning with Cognitive-Inspired AnalysisFor human cognitive process, spatial reasoning and perception are closely entangled, yet the nature of this interplay remains underexplored in the evaluation of multimodal large language models (MLLMs). While recent MLLM advancements show impressive performance on reasoning, their capacity for human-like spatial cognition remains an open question. In this work, we i ...

数据来源：ArXiv Domain LLM Domain Papers1. Bitune: Leveraging Bidirectional Attention to Improve Decoder-Only LLMsDecoder-only large language models typically rely solely on masked causal attention, which limits their expressiveness by restricting information flow to one direction. We propose Bitune, a method that enhances pretrained decoder-only LLMs by incorporating bidirectional attention into prompt processing. We evaluate Bitune in instruction-tuning and question-answering settings, showing si ...

数据来源：ArXiv Domain LLM Domain Papers1. Bitune: Leveraging Bidirectional Attention to Improve Decoder-Only LLMsDecoder-only large language models typically rely solely on masked causal attention, which limits their expressiveness by restricting information flow to one direction. We propose Bitune, a method that enhances pretrained decoder-only LLMs by incorporating bidirectional attention into prompt processing. We evaluate Bitune in instruction-tuning and question-answering settings, showing si ...

数据来源：ArXiv Domain LLM Domain Papers1. Bitune: Leveraging Bidirectional Attention to Improve Decoder-Only LLMsDecoder-only large language models typically rely solely on masked causal attention, which limits their expressiveness by restricting information flow to one direction. We propose Bitune, a method that enhances pretrained decoder-only LLMs by incorporating bidirectional attention into prompt processing. We evaluate Bitune in instruction-tuning and question-answering settings, showing si ...

数据来源：ArXiv Domain LLM Domain Papers1. ROSE: A Reward-Oriented Data Selection Framework for LLM Task-Specific Instruction TuningInstruction tuning has underscored the significant potential of large language models (LLMs) in producing more human controllable and effective outputs in various domains. In this work, we focus on the data selection problem for task-specific instruction tuning of LLMs. Prevailing methods primarily rely on the crafted similarity metrics to select training data that ali ...

数据来源：ArXiv Domain LLM Domain Papers1. MMReview: A Multidisciplinary and Multimodal Benchmark for LLM-Based Peer Review AutomationWith the rapid growth of academic publications, peer review has become an essential yet time-consuming responsibility within the research community. Large Language Models (LLMs) have increasingly been adopted to assist in the generation of review comments; however, current LLM-based review tasks lack a unified evaluation benchmark to rigorously assess the models’ abi ...

数据来源：ArXiv Domain LLM Domain Papers1. The Impact of Language Mixing on Bilingual LLM ReasoningProficient multilingual speakers often intentionally switch languages in the middle of a conversation. Similarly, recent reasoning-focused bilingual large language models (LLMs) with strong capabilities in both languages exhibit language mixing—alternating languages within their chain of thought. Discouraging this behavior in DeepSeek-R1 was found to degrade accuracy, suggesting that language mixing m ...

数据来源：ArXiv Domain LLM Domain Papers1. Jinx: Unlimited LLMs for Probing Alignment FailuresUnlimited, or so-called helpful-only language models are trained without safety alignment constraints and never refuse user queries. They are widely used by leading AI companies as internal tools for red teaming and alignment evaluation. For example, if a safety-aligned model produces harmful outputs similar to an unlimited model, this indicates alignment failures that require further attention. Despite th ...

数据来源：ArXiv Domain LLM Domain Papers1. ROSE: A Reward-Oriented Data Selection Framework for LLM Task-Specific Instruction TuningInstruction tuning has underscored the significant potential of large language models (LLMs) in producing more human controllable and effective outputs in various domains. In this work, we focus on the data selection problem for task-specific instruction tuning of LLMs. Prevailing methods primarily rely on the crafted similarity metrics to select training data that ali ...

数据来源：ArXiv Domain LLM Domain Papers1. Continuous Saudi Sign Language Recognition: A Vision Transformer ApproachSign language (SL) is an essential communication form for hearing-impaired and deaf people, enabling engagement within the broader society. Despite its significance, limited public awareness of SL often leads to inequitable access to educational and professional opportunities, thereby contributing to social exclusion, particularly in Saudi Arabia, where over 84,000 individuals depend ...

数据来源：ArXiv Domain LLM Domain Papers1. Delta Activations: A Representation for Finetuned Large Language ModelsThe success of powerful open source Large Language Models (LLMs) has enabled the community to create a vast collection of post-trained models adapted to specific tasks and domains. However, navigating and understanding these models remains challenging due to inconsistent metadata and unstructured repositories. We introduce Delta Activations, a method to represent finetuned models as vec ...

数据来源：ArXiv Domain LLM Domain Papers1. On sources to variabilities of simple cells in the primary visual cortex: A principled theory for the interaction between geometric image transformations and receptive field responsesThis paper gives an overview of a theory for modelling the interaction between geometric image transformations and receptive field responses for a visual observer that views objects and spatio-temporal events in the environment. This treatment is developed over combinations of ...

数据来源：ArXiv Domain LLM Domain Papers1. On sources to variabilities of simple cells in the primary visual cortex: A principled theory for the interaction between geometric image transformations and receptive field responsesThis paper gives an overview of a theory for modelling the interaction between geometric image transformations and receptive field responses for a visual observer that views objects and spatio-temporal events in the environment. This treatment is developed over combinations of ...

数据来源：ArXiv Domain LLM Domain Papers1. Scaling Environments for Organoid Intelligence with LLM-Automated Design and Plasticity-Based EvaluationAs the complexity of artificial agents increases, the design of environments that can effectively shape their behavior and capabilities has become a critical research frontier. We propose a framework that extends this principle to a novel class of agents: biological neural networks in the form of neural organoids. This paper introduces three scalable, cl ...