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Large Language Models

9,067 papers

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Papers

Why We Feel What We Feel: Joint Detection of Emotions and Their Opinion Triggers in E-commerce EMNLP 2025
BanglaByT5: Byte-Level Modelling for Bangla EMNLP 2025
Long-context Language Models Fail in Basic Retrieval Tasks Without Sufficient Reasoning Steps EMNLP 2025
Retrieval-Augmented Machine Translation with Unstructured Knowledge EMNLP 2025
UTMath: A Benchmark for Math Evaluation with Unit Test EMNLP 2025
Retrieval-Augmented Generation with Hierarchical Knowledge EMNLP 2025
Drift: Decoding-time Personalized Alignments with Implicit User Preferences EMNLP 2025
KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering EMNLP 2025
OSC: Cognitive Orchestration through Dynamic Knowledge Alignment in Multi-Agent LLM Collaboration EMNLP 2025
When Inverse Data Outperforms: Exploring the Pitfalls of Mixed Data in Multi-Stage Fine-Tuning EMNLP 2025
A Knapsack by Any Other Name: Presentation impacts LLM performance on NP-hard problems EMNLP 2025
Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of Large Language Models EMNLP 2025
A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcare EMNLP 2025
Self-Correction Makes LLMs Better Parsers EMNLP 2025
Explaining Length Bias in LLM-Based Preference Evaluations EMNLP 2025
Turning the Tide: Repository-based Code Reflection EMNLP 2025
Federated Retrieval-Augmented Generation: A Systematic Mapping Study EMNLP 2025
Offloaded Reasoning: Efficient Inference for Large Language Models via Modular Reasoning and Refinement EMNLP 2025
Learning to Instruct: Fine-Tuning a Task-Aware Instruction Optimizer for Black-Box LLMs EMNLP 2025
Token Knowledge: A New Perspective For Knowledge in Large Language Models EMNLP 2025
Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation EMNLP 2025
Enhancing Attributed Question Answering using Tailored Progressive Curriculum Learning EMNLP 2025
Nine Ways to Break Copyright Law and Why Our LLM Won’t: A Fair Use Aligned Generation Framework EMNLP 2025
Low-Confidence Gold: Refining Low-Confidence Samples for Efficient Instruction Tuning EMNLP 2025
DLPO: Towards a Robust, Efficient, and Generalizable Prompt Optimization Framework from a Deep-Learning Perspective EMNLP 2025