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cs.CL2024

Findings of the WMT 2024 Shared Task on Discourse-Level Literary Translation

Longyue Wang, Siyou Liu, Chenyang Lyu +11

Following last year, we have continued to host the WMT translation shared task this year, the second edition of the Discourse-Level Literary Translation. We focus on three language…

cs.CL2024

Marco-LLM: Bridging Languages via Massive Multilingual Training for Cross-Lingual Enhancement

Lingfeng Ming, Bo Zeng, Chenyang Lyu +17

Large Language Models (LLMs) have achieved remarkable progress in recent years; however, their excellent performance is still largely limited to major world languages, primarily En…

cs.CL2024

Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions

Yu Zhao, Huifeng Yin, Bo Zeng +6

Currently OpenAI o1 sparks a surge of interest in the study of large reasoning models (LRM). Building on this momentum, Marco-o1 not only focuses on disciplines with standard answe…

cs.CL2024

Reference-free Hallucination Detection for Large Vision-Language Models

Qing Li, Jiahui Geng, Chenyang Lyu +3

Large vision-language models (LVLMs) have made significant progress in recent years. While LVLMs exhibit excellent ability in language understanding, question answering, and conver…

cs.CV2024

CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark

David Romero, Chenyang Lyu, Haryo Akbarianto Wibowo +73

Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge pres…

cs.CV2024

GPT4Video: A Unified Multimodal Large Language Model for lnstruction-Followed Understanding and Safety-Aware Generation

Zhanyu Wang, Longyue Wang, Zhen Zhao +7

While the recent advances in Multimodal Large Language Models (MLLMs) constitute a significant leap forward in the field, these models are predominantly confined to the realm of in…