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pro vyhledávání: '"Wang., Lu"'
Language models (LMs) have demonstrated an improved capacity to handle long-context information, yet existing long-context benchmarks primarily measure LMs' retrieval abilities with extended inputs, e.g., pinpointing a short phrase from long-form tex
Externí odkaz:
http://arxiv.org/abs/2411.07130
Autor:
Zhang, Yudi, Xiao, Pei, Wang, Lu, Zhang, Chaoyun, Fang, Meng, Du, Yali, Puzyrev, Yevgeniy, Yao, Randolph, Qin, Si, Lin, Qingwei, Pechenizkiy, Mykola, Zhang, Dongmei, Rajmohan, Saravan, Zhang, Qi
In-context learning (ICL) and Retrieval-Augmented Generation (RAG) have gained attention for their ability to enhance LLMs' reasoning by incorporating external knowledge but suffer from limited contextual window size, leading to insufficient informat
Externí odkaz:
http://arxiv.org/abs/2411.03349
Autor:
Ouyang, Yichen, Wang, Lu, Yang, Fangkai, Zhao, Pu, Huang, Chenghua, Liu, Jianfeng, Pang, Bochen, Yang, Yaming, Zhan, Yuefeng, Sun, Hao, Lin, Qingwei, Rajmohan, Saravan, Deng, Weiwei, Zhang, Dongmei, Sun, Feng, Zhang, Qi
Query generation is a critical task for web search engines (e.g. Google, Bing) and recommendation systems. Recently, state-of-the-art query generation methods leverage Large Language Models (LLMs) for their strong capabilities in context understandin
Externí odkaz:
http://arxiv.org/abs/2411.00722
Autor:
Huang, Chenghua, Fan, Zhizhen, Wang, Lu, Yang, Fangkai, Zhao, Pu, Lin, Zeqi, Lin, Qingwei, Zhang, Dongmei, Rajmohan, Saravan, Zhang, Qi
Reinforcement Learning from Human Feedback (RLHF) is a crucial technique for aligning language models with human preferences, playing a pivotal role in the success of conversational models like GPT-4, ChatGPT, and Llama 2. A core challenge in employi
Externí odkaz:
http://arxiv.org/abs/2411.00418
Language models (LMs) are widely used by an increasing number of users, underscoring the challenge of maintaining factuality across a broad range of topics. We first present VERIFY (Verification and Evidence RetrIeval for FactualitY evaluation), a pi
Externí odkaz:
http://arxiv.org/abs/2410.22257
Deep Learning-Driven Microstructure Characterization and Vickers Hardness Prediction of Mg-Gd Alloys
In the field of materials science, exploring the relationship between composition, microstructure, and properties has long been a critical research focus. The mechanical performance of solid-solution Mg-Gd alloys is significantly influenced by Gd con
Externí odkaz:
http://arxiv.org/abs/2410.20402
Reliable responses of service chatbots are often achieved by employing retrieval-based methods that restrict answers to a knowledge base comprising predefined question-answer pairs (QA pairs). To accommodate potential variations in how a customer's q
Externí odkaz:
http://arxiv.org/abs/2410.12444
Providing feedback is widely recognized as crucial for refining students' writing skills. Recent advances in language models (LMs) have made it possible to automatically generate feedback that is actionable and well-aligned with human-specified attri
Externí odkaz:
http://arxiv.org/abs/2410.08058
Narrative-of-Thought: Improving Temporal Reasoning of Large Language Models via Recounted Narratives
Reasoning about time and temporal relations is an integral aspect of human cognition, essential for perceiving the world and navigating our experiences. Though large language models (LLMs) have demonstrated impressive performance in many reasoning ta
Externí odkaz:
http://arxiv.org/abs/2410.05558
We study the problem of fine-tuning a language model (LM) for a target task by optimally using the information from $n$ auxiliary tasks. This problem has broad applications in NLP, such as targeted instruction tuning and data selection in chain-of-th
Externí odkaz:
http://arxiv.org/abs/2409.19458