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Identifying the Achilles' Heel: An Iterative Method for Dynamically Uncovering Factual Errors in Large Language Models  ⚙️MLOps
arxiv.org·20h
AsishKumarDalal/memoryllm: using differntiable neural computer architecture with GPT2 to provide memory  🔧Hardware
github.com·5d·DEV
Select to Think: Unlocking SLM Potential with Local Sufficiency  📡Edge Computing
arxiv.org·20h
LLM Psychosis: A Theoretical and Diagnostic Framework for Reality-Boundary Failures in Large Language Models  ⚙️MLOps
arxiv.org·20h
Shorthand for Thought: Compressing LLM Reasoning via Entropy-Guided Supertokens  ⚙️MLOps
arxiv.org·20h
Language Anchoring: A Systematic Method for LLM Multilingual Adaptation  ⚙️MLOps
github.com·3d·Hacker News
What Kind of Language is Easy to Language-Model Under Curriculum Learning?  🗄️Vector Databases
arxiv.org·20h
From Similarity to Structure: Training-free LLM Context Compression with Hybrid Graph Priors  🗄️Vector Databases
arxiv.org·2d
CoQuant: Joint Weight-Activation Subspace Projection for Mixed-Precision LLMs  🗄️Vector Databases
arxiv.org·20h
MoRFI: Monotonic Sparse Autoencoder Feature Identification  🗄️Vector Databases
arxiv.org·20h
Doing More With Less: Revisiting the Effectiveness of LLM Pruning for Test-Time Scaling  ⚙️MLOps
arxiv.org·1d
Information Extraction from Electricity Invoices with General-Purpose Large Language Models  ⚙️MLOps
arxiv.org·20h
A Systematic Approach for Large Language Models Debugging  ⚙️MLOps
arxiv.org·2d
Delineating Knowledge Boundaries for Honest Large Vision-Language Models  👁️Computer Vision
arxiv.org·20h
Evaluating Large Language Models on Computer Science University Exams in Data Structures  🗄️Vector Databases
arxiv.org·2d
Small Language Model Helps Resolve Semantic Ambiguity of LLM Prompt  ⚙️MLOps
arxiv.org·2d
Self Knowledge Re-expression: A Fully Local Method for Adapting LLMs to Tasks Using Intrinsic Knowledge  🤖AI
arxiv.org·2d
The Structured Output Benchmark: A Multi-Source Benchmark for Evaluating Structured Output Quality in Large Language Models  ⚙️MLOps
arxiv.org·1d
Programming with Data: Test-Driven Data Engineering for Self-Improving LLMs from Raw Corpora  ⚙️MLOps
arxiv.org·1d
Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities  📡Edge Computing
arxiv.org·2d

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