Zobrazeno 1 - 10
of 274
pro vyhledávání: '"HUANG Yuheng"'
Given the ubiquity of multi-task in practical systems, Multi-Task Learning (MTL) has found widespread application across diverse domains. In real-world scenarios, these tasks often have different priorities. For instance, In web search, relevance is
Externí odkaz:
http://arxiv.org/abs/2412.12092
Retrieval-Augmented Generation (RAG) is a pivotal technique for enhancing the capability of large language models (LLMs) and has demonstrated promising efficacy across a diverse spectrum of tasks. While LLM-driven RAG systems show superior performanc
Externí odkaz:
http://arxiv.org/abs/2411.19463
Building on the advancements of Large Language Models (LLMs) and Vision Language Models (VLMs), recent research has introduced Vision-Language-Action (VLA) models as an integrated solution for robotic manipulation tasks. These models take camera imag
Externí odkaz:
http://arxiv.org/abs/2410.05191
Multi-modal foundation models and generative AI have demonstrated promising capabilities in applications across various domains. Recently, Vision-language-action (VLA) models have attracted much attention regarding their potential to advance robotic
Externí odkaz:
http://arxiv.org/abs/2409.12894
Large Language Models (LLMs) are widely used in many different domains, but because of their limited interpretability, there are questions about how trustworthy they are in various perspectives, e.g., truthfulness and toxicity. Recent research has st
Externí odkaz:
http://arxiv.org/abs/2408.10474
Performance evaluation plays a crucial role in the development life cycle of large language models (LLMs). It estimates the model's capability, elucidates behavior characteristics, and facilitates the identification of potential issues and limitation
Externí odkaz:
http://arxiv.org/abs/2408.03573
As safety remains a crucial concern throughout the development lifecycle of Large Language Models (LLMs), researchers and industrial practitioners have increasingly focused on safeguarding and aligning LLM behaviors with human preferences and ethical
Externí odkaz:
http://arxiv.org/abs/2407.07342
Large language models (LLMs) enhanced by retrieval-augmented generation (RAG) provide effective solutions in various application scenarios. However, developers face challenges in integrating RAG-enhanced LLMs into software systems, due to lack of int
Externí odkaz:
http://arxiv.org/abs/2407.05138
Large Language Models (LLMs) have shown great potential in code generation. However, current LLMs still cannot reliably generate correct code. Moreover, it is unclear what kinds of code generation errors LLMs can make. To address this, we conducted a
Externí odkaz:
http://arxiv.org/abs/2406.08731
Autor:
Wang, Wenhan, Yang, Chenyuan, Wang, Zhijie, Huang, Yuheng, Chu, Zhaoyang, Song, Da, Zhang, Lingming, Chen, An Ran, Ma, Lei
Testing plays a crucial role in the software development cycle, enabling the detection of bugs, vulnerabilities, and other undesirable behaviors. To perform software testing, testers need to write code snippets that execute the program under test. Re
Externí odkaz:
http://arxiv.org/abs/2406.04531