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An Yang, Baosong Yang, Binyuan Hui +55 authors
The Qwen2 series, comprising 0.5 to 72 billion parameter models, surpasses prior open models across language understanding, generation, multilingualism, coding, math, and reasoning, with exceptional performance in benchmarks like MMLU, GPQA, HumanEval, GSM8K, BBH, MT-Bench, Arena-Hard, and LiveCodeBench.
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Yuzhang Tian, Jianbo Zhao, Haoyu Dong +8 authors
SpreadsheetLLM introduces SheetCompressor and Chain of Spreadsheet to enhance LLMs' performance on spreadsheet tasks through efficient encoding and understanding.
Xin Chan, Xiaoyang Wang, Dian Yu +2 authors
Persona-driven synthetic data creation using a large language model and 1 billion diverse personas for various applications demonstrates versatility, scalability, and flexibility.
Pan Zhang, Xiaoyi Dong, Yuhang Zang +24 authors
A large-vision language model with long-contextual capabilities supports various comprehension and composition tasks, achieving GPT-4V level performance using 7B parameters.
Philippe Laban, Alexander R. Fabbri, Caiming Xiong +1 authors
The SummHay task evaluates LLMs and RAG systems on summarizing long-context documents by identifying relevant insights and citing sources, revealing challenges even for systems with document relevance signals.
Xingyao Wang, Boxuan Li, Yufan Song +21 authors
OpenDevin is a platform for developing AI agents that interact with the world by writing code, using command lines, and browsing the web, with support for multiple agents and evaluation benchmarks.
Pooyan Rahmanzadehgervi, Logan Bolton, Mohammad Reza Taesiri +1 authors
State-of-the-art large language models with vision capabilities perform poorly on simple visual tasks, indicating limitations in their visual understanding.
Runqi Qiao, Qiuna Tan, Guanting Dong +15 authors
WE-MATH benchmark evaluates Large Multimodal Models in visual mathematical reasoning, focusing on knowledge acquisition and generalization principles beyond end-to-end performance.
Ayush Kaushal, Tejas Pandey, Tejas Vaidhya +2 authors
The Spectra LLM suite provides insights into the performance and characteristics of low-bitwidth models through comprehensive training and evaluation of FloatLMs, QuantLMs, and TriLMs across various benchmarks and datasets.
Lucas Beyer, Andreas Steiner, André Susano Pinto +32 authors
PaliGemma, a versatile Vision-Language Model based on SigLIP-So400m and Gemma-2B, demonstrates strong performance across numerous open-world tasks, including specialized areas like remote sensing and segmentation.
Leon Voukoutis, Dimitris Roussis, Georgios Paraskevopoulos +6 authors
Developers created Meltemi 7B, a 7 billion parameter open-source large language model for Greek, trained on a 40 billion token corpus, and enhanced with instruction-tuning for chat applications.
Pierre Colombo, Telmo Pires, Malik Boudiaf +7 authors
Two large legal language models, SaulLM-54B and SaulLM-141B, based on the Mixtral architecture, are introduced for domain-specific adaptation in the legal sector using continued pretraining, specialized protocols, and preference alignment with synthetic data.
Chunqiu Steven Xia, Yinlin Deng, Soren Dunn +1 authors
Agentless, a two-phase approach to software development automation, achieves high performance and low cost compared to complex autonomous agents on the SWE-bench Lite benchmark.
Yunfei Chu, Jin Xu, Qian Yang +9 authors
Qwen2-Audio, a large-scale audio-language model, enhances instruction-following and audio analysis through natural language prompts and DPO optimization.
Junying Chen, Ruyi Ouyang, Anningzhe Gao +9 authors
The refinement of medical image-text pairs using multimodal large language models enhances medical multimodal capabilities and data quality, leading to superior performance in benchmarks.
Zafeirios Fountas, Martin A Benfeghoul, Adnan Oomerjee +4 authors
EM-LLM enhances LLMs by integrating human episodic memory principles, enabling efficient handling of extensive contexts through coherent event segmentation and retrieval.
Christopher E. Mower, Yuhui Wan, Hongzhan Yu +19 authors
A framework enables non-experts to program robots using natural language prompts and contextual information from the Robot Operating System (ROS), supported by large language models and offering various behavior modes, imitation learning, and feedback mechanisms.
Santosh V. Patapati
A BiLSTM-based multi-modal model combines audio, facial, and text data using a GPT-4 model to classify depression, achieving state-of-the-art performance in binary classification tasks.
Wenxuan Zhang, Hou Pong Chan, Yiran Zhao +9 authors
SeaLLMs 3 is a large language model optimized for Southeast Asian languages, offering state-of-the-art performance in various tasks while addressing safety and cultural considerations.
Chaofan Tao, Qian Liu, Longxu Dou +5 authors
Investigating the impact of vocabulary size on the scaling of large language models reveals that larger vocabularies improve performance when considering compute budgets, and current models often use suboptimal vocabulary sizes.
Daniel Goldstein, Fares Obeid, Eric Alcaide +2 authors
A hybrid Linear Attention/Transformer model, GoldFinch, efficiently generates a compressed KV-Cache for large context lengths with linear time and space complexity, outperforming previous models.
Kepan Nan, Rui Xie, Penghao Zhou +6 authors
The paper introduces OpenVid-1M and OpenVidHD-0.4M datasets, along with MVDiT, a Multi-modal Video Diffusion Transformer, to enhance text-to-video generation by leveraging high-quality data and comprehensive text information.
Junying Chen, Chi Gui, Anningzhe Gao +4 authors
Chain-of-Diagnosis (CoD) enhances interpretability in LLM-based medical diagnostics by providing a transparent reasoning pathway and developing DiagnosisGPT, which diagnoses a wide range of diseases with high accuracy and controllable rigor.
Zhaorun Chen, Yichao Du, Zichen Wen +16 authors
MJ-Bench evaluates multimodal judges across alignment, safety, image quality, and bias for text-to-image models and confirms close-source VLMs generally provide better feedback.
Haiwen Diao, Yufeng Cui, Xiaotong Li +3 authors
EVE is an encoder-free vision-language model that achieves competitive performance on multiple benchmarks using a unified decoder and extra supervision.
Pujiang He, Shan Zhou, Wenhuan Huang +7 authors
The paper presents a performance optimization solution for deploying large language models on CPUs by reducing KV cache size and implementing distributed inference, open-sourced for use.
Liang Zeng, Liangjun Zhong, Liang Zhao +9 authors
Skywork-Math model series achieves high performance in math reasoning by fine-tuning on a large and diverse dataset, demonstrating that data quantity significantly enhances LLM mathematical capabilities.
Yu Lu, Yuanzhi Liang, Linchao Zhu +1 authors
FreeLong enhances long video generation by balancing global and local video features during denoising, improving quality and consistency.
Zhaorun Chen, Zhen Xiang, Chaowei Xiao +2 authors
AgentPoison, a novel red teaming approach, introduces a backdoor attack targeting LLM agents by poisoning their memory or knowledge base, ensuring high attack success rates with minimal impact on normal performance.
Arindam Mitra, Luciano Del Corro, Guoqing Zheng +11 authors
AgentInstruct, an agentic framework, generates high-quality synthetic data for post-training language models, leading to significant performance improvements across various benchmarks.
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