Zobrazeno 1 - 10
of 196
pro vyhledávání: '"Lu, Wenpeng"'
Reinforcement Learning from Human Feedback (RLHF) has been proven to be an effective method for preference alignment of large language models (LLMs) and is widely used in the post-training process of LLMs. However, RLHF struggles with handling multip
Externí odkaz:
http://arxiv.org/abs/2411.01245
Autor:
Zhang, Yongheng, Chen, Qiguang, Zhou, Jingxuan, Wang, Peng, Si, Jiasheng, Wang, Jin, Lu, Wenpeng, Qin, Libo
Chain-of-Thought (CoT) has become a vital technique for enhancing the performance of Large Language Models (LLMs), attracting increasing attention from researchers. One stream of approaches focuses on the iterative enhancement of LLMs by continuously
Externí odkaz:
http://arxiv.org/abs/2410.04463
With the growing complexity of fact verification tasks, the concern with "thoughtful" reasoning capabilities is increasing. However, recent fact verification benchmarks mainly focus on checking a narrow scope of semantic factoids within claims and la
Externí odkaz:
http://arxiv.org/abs/2408.10918
Autor:
Qin, Libo, Wei, Fuxuan, Chen, Qiguang, Zhou, Jingxuan, Huang, Shijue, Si, Jiasheng, Lu, Wenpeng, Che, Wanxiang
Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Neverthe
Externí odkaz:
http://arxiv.org/abs/2406.10505
The proliferation of open-source Large Language Models (LLMs) underscores the pressing need for evaluation methods. Existing works primarily rely on external evaluators, focusing on training and prompting strategies. However, a crucial aspect, model-
Externí odkaz:
http://arxiv.org/abs/2403.04222
Next Basket Recommender Systems (NBRs) function to recommend the subsequent shopping baskets for users through the modeling of their preferences derived from purchase history, typically manifested as a sequence of historical baskets. Given their wide
Externí odkaz:
http://arxiv.org/abs/2312.02550
Pre-trained vision-language models, e.g., CLIP, working with manually designed prompts have demonstrated great capacity of transfer learning. Recently, learnable prompts achieve state-of-the-art performance, which however are prone to overfit to seen
Externí odkaz:
http://arxiv.org/abs/2308.11186
By summarizing longer consumer health questions into shorter and essential ones, medical question answering (MQA) systems can more accurately understand consumer intentions and retrieve suitable answers. However, medical question summarization is ver
Externí odkaz:
http://arxiv.org/abs/2304.07437
In natural language processing (NLP), the context of a word or sentence plays an essential role. Contextual information such as the semantic representation of a passage or historical dialogue forms an essential part of a conversation and a precise un
Externí odkaz:
http://arxiv.org/abs/2211.02899
Next basket recommender systems (NBRs) aim to recommend a user's next (shopping) basket of items via modeling the user's preferences towards items based on the user's purchase history, usually a sequence of historical baskets. Due to its wide applica
Externí odkaz:
http://arxiv.org/abs/2209.02892