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Found 11 papers, 8 papers with code
MT$^{3}$: Scaling MLLM-based Text Image Machine Translation via Multi-Task Reinforcement Learning
Text Image Machine Translation (TIMT)-the task of translating textual content embedded in images-is critical for applications in accessibility, cross-lingual information access, and real-world document understanding.
CP-Router: An Uncertainty-Aware Router Between LLM and LRM
Recent advances in Large Reasoning Models (LRMs) have significantly improved long-chain reasoning capabilities over Large Language Models (LLMs).
CompBench: Benchmarking Complex Instruction-guided Image Editing
While real-world applications increasingly demand intricate scene manipulation, existing instruction-guided image editing benchmarks often oversimplify task complexity and lack comprehensive, fine-grained instructions.
MT-R1-Zero: Advancing LLM-based Machine Translation via R1-Zero-like Reinforcement Learning
We propose a rule-metric mixed reward mechanism to guide LLMs towards improved translation quality via emergent reasoning.
M-MAD: Multidimensional Multi-Agent Debate Framework for Fine-grained Machine Translation Evaluation
Recent advancements in large language models (LLMs) have given rise to the LLM-as-a-judge paradigm, showcasing their potential to deliver human-like judgments.
EC-Guide: A Comprehensive E-Commerce Guide for Instruction Tuning and Quantization
Large language models (LLMs) have attracted considerable attention in various fields for their cost-effective solutions to diverse challenges, especially with advancements in instruction tuning and quantization.
Ladder: A Model-Agnostic Framework Boosting LLM-based Machine Translation to the Next Level
By utilizing Gemma-2B/7B as the backbone, MT-Ladder-2B can elevate raw translations to the level of top-tier open-source models (e. g., refining BigTranslate-13B with +6. 91 BLEU and +3. 52 COMET for XX-En), and MT-Ladder-7B can further enhance model performance to be on par with the state-of-the-art GPT-4.
TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement
Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT).
DCR: Divide-and-Conquer Reasoning for Multi-choice Question Answering with LLMs
Subsequently, we propose Filter Choices based Reasoning (FCR) to improve model performance on MCQs with low ($\mathcal{CS}$).
How Well Do Text Embedding Models Understand Syntax?
Text embedding models have significantly contributed to advancements in natural language processing by adeptly capturing semantic properties of textual data.
Learning from Mixed Datasets: A Monotonic Image Quality Assessment Model
Deep learning based image quality assessment (IQA) models usually learn to predict image quality from a single dataset, leading the model to overfit specific scenes.
