Zobrazeno 1 - 10
of 2 354
pro vyhledávání: '"Yu MO"'
Large language models (LLMs) have achieved substantial progress in processing long contexts but still struggle with long-context reasoning. Existing approaches typically involve fine-tuning LLMs with synthetic data, which depends on annotations from
Externí odkaz:
http://arxiv.org/abs/2411.08147
Length extrapolation algorithms based on Rotary position embedding (RoPE) have shown promising results in extending the context length of language models. However, understanding how position embedding can capture longer-range contextual information r
Externí odkaz:
http://arxiv.org/abs/2410.08703
Autor:
Li, Siheng, Yang, Cheng, Wu, Taiqiang, Shi, Chufan, Zhang, Yuji, Zhu, Xinyu, Cheng, Zesen, Cai, Deng, Yu, Mo, Liu, Lemao, Zhou, Jie, Yang, Yujiu, Wong, Ngai, Wu, Xixin, Lam, Wai
Honesty is a fundamental principle for aligning large language models (LLMs) with human values, requiring these models to recognize what they know and don't know and be able to faithfully express their knowledge. Despite promising, current LLMs still
Externí odkaz:
http://arxiv.org/abs/2409.18786
Large language models (LLMs) have been garnering increasing attention in the recommendation community. Some studies have observed that LLMs, when fine-tuned by the cross-entropy (CE) loss with a full softmax, could achieve `state-of-the-art' performa
Externí odkaz:
http://arxiv.org/abs/2408.14238
Autor:
Yang, Cheng, Huang, Guoping, Yu, Mo, Zhang, Zhirui, Li, Siheng, Yang, Mingming, Shi, Shuming, Yang, Yujiu, Liu, Lemao
Word-level AutoCompletion(WLAC) is a rewarding yet challenging task in Computer-aided Translation. Existing work addresses this task through a classification model based on a neural network that maps the hidden vector of the input context into its co
Externí odkaz:
http://arxiv.org/abs/2407.20083
Autor:
Li, Jiangnan, Lin, Zheng, Wang, Lanrui, Si, Qingyi, Cao, Yanan, Yu, Mo, Fu, Peng, Wang, Weiping, Zhou, Jie
Humans convey emotions through daily dialogues, making emotion understanding a crucial step of affective intelligence. To understand emotions in dialogues, machines are asked to recognize the emotion for an utterance (Emotion Recognition in Dialogues
Externí odkaz:
http://arxiv.org/abs/2406.04758
Autor:
Jiang, Che, Qi, Biqing, Hong, Xiangyu, Fu, Dayuan, Cheng, Yang, Meng, Fandong, Yu, Mo, Zhou, Bowen, Zhou, Jie
Large language models are successful in answering factoid questions but are also prone to hallucination. We investigate the phenomenon of LLMs possessing correct answer knowledge yet still hallucinating from the perspective of inference dynamics, an
Externí odkaz:
http://arxiv.org/abs/2403.20009
Autor:
Ding, Peng, Fang, Jiading, Li, Peng, Wang, Kangrui, Zhou, Xiaochen, Yu, Mo, Li, Jing, Walter, Matthew R., Mei, Hongyuan
Large language models such as ChatGPT and GPT-4 have recently achieved astonishing performance on a variety of natural language processing tasks. In this paper, we propose MANGO, a benchmark to evaluate their capabilities to perform text-based mappin
Externí odkaz:
http://arxiv.org/abs/2403.19913
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating additional information from retrieval. However, studies have shown that LLMs still face challenges in effectively using the retrieved information, even ignori
Externí odkaz:
http://arxiv.org/abs/2402.18150
This work introduces an original and practical paradigm for narrative comprehension, stemming from the characteristics that individual passages within narratives tend to be more cohesively related than isolated. Complementary to the common end-to-end
Externí odkaz:
http://arxiv.org/abs/2402.13551