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Assessing How Well Large Language Models Simulate Human Behavior with SIMBENCH

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PANER: Enhancing Entity Recognition in Data-Scarce Settings

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Unpacking Forgetting in Language Models: A Deep Dive into Post-Training Knowledge Retention

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MIT Pioneers Training Method for Personalized Object Localization in Generative AI

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Fine-Tuning LLMs: Detecting and Reducing Training Data Memorization

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Boosting Smaller Language Models for Business Conversations with DACIP-RC

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Pragyaan: Cultivating Culturally Aware AI for Indian Languages

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Boosting Luxembourgish Language Models with a New Cross-Lingual Dataset

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Rainbow Padding: A Simple Solution to Early Termination in Diffusion LLMs

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Optimizing Vision-Language Model Training with Capability-Aware Data Curation

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Efficient Data Selection for Model Fine-Tuning

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Decoding Human-Like Thinking in Large Language Models

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OpenCodeEdit: Crafting Superior Datasets for Instruction-Guided Code Editing with Open-Source LLMs

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Unpacking LLM Struggles with Causality: The Role of Uncertainty and Overconfidence

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AirQA: A New Benchmark and Data Synthesis Framework for AI Paper Question Answering

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Instruction Tuning Large Language Models for Better Coreference Resolution Across Languages

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Unlocking the Internal Mechanics of Language Model Fine-Tuning

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Adaptive Learning for LLMs: A New Approach to Instruction Tuning

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Advancing Arabic Language AI with HALA Models

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PDDL-INSTRUCT: Enhancing LLMs for Precise Symbolic Planning

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Optimizing LLM Training: The Power of Instruction Coverage and Depth

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Advancing LLM Training: A Deep Dive into Reinforcement Learning and the GRAPE Framework

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Decoding How AI Models Learn to Control Text Length

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Adaptive AI: How Instruction-Level Weight Shaping Creates Self-Improving Agents

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Unpacking User-Assistant Bias in Large Language Models: A New Framework for Understanding and Control

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Unlocking LLM Potential: A Seed-Free Approach to Instruction Tuning

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Robots Learn to Navigate Impossible Commands with New AI Framework

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LLaSO: An Open Standard for Large Speech-Language Models

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Optimizing LLM Instruction Tuning with Dynamic Dataset Mixtures

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Unlocking Graph Reasoning in Large Language Models

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