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2401.04088
Mixtral of Experts
We introduce Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model. Mixtral has the same architecture as Mistral 7B, with the difference that each layer is composed of 8 feedforward blocks (i.e. experts). For every token, at each layer, a router network selects two experts to process the current state and com...
http://arxiv.org/pdf/2401.04088
Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Sandeep Subramanian...
cs.LG, cs.CL
See more details at https://mistral.ai/news/mixtral-of-experts/
null
cs.LG
20240108
20240108
4 2 0 2 n a J 8 ] G L . s c [ 1 v 8 8 0 4 0 . 1 0 4 2 : v i X r a # Mixtral of Experts Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lél...
{ "id": "1905.07830" }
2312.17238
Fast Inference of Mixture-of-Experts Language Models with Offloading
With the widespread adoption of Large Language Models (LLMs), many deep learning practitioners are looking for strategies of running these models more efficiently. One such strategy is to use sparse Mixture-of-Experts (MoE) - a type of model architectures where only a fraction of model layers are active for any given i...
http://arxiv.org/pdf/2312.17238
Artyom Eliseev, Denis Mazur
cs.LG, cs.AI, cs.DC
Technical report
null
cs.LG
20231228
20231228
3 2 0 2 c e D 8 2 ] G L . s c [ 1 v 8 3 2 7 1 . 2 1 3 2 : v i X r a # Fast Inference of Mixture-of-Experts Language Models with Offloading Artyom Eliseev Moscow Institute of Physics and Technology Yandex School of Data Analysis lavawolfiee@gmail.com # Denis Mazur Moscow Institute of Physics and Technology Yandex Resear...
{ "id": "2302.13971" }
2312.11111
The Good, The Bad, and Why: Unveiling Emotions in Generative AI
"Emotion significantly impacts our daily behaviors and interactions. While\nrecent generative AI mod(...TRUNCATED)
http://arxiv.org/pdf/2312.11111
"Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Xinyi Wang, Wenxin Hou, Jianxun Lian, Fang Luo, Q(...TRUNCATED)
cs.AI, cs.CL, cs.HC
Technical report; an extension to EmotionPrompt (arXiv:2307.11760); 34 pages
null
cs.AI
20231218
20231219
"3 2 0 2 c e D 9 1\n] I A . s c [\n2 v 1 1 1 1 1 . 2 1 3 2 : v i X r a\n# The Good, The Bad, and Why(...TRUNCATED)
{ "id": "2210.09261" }
2312.00752
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
"Foundation models, now powering most of the exciting applications in deep\nlearning, are almost uni(...TRUNCATED)
http://arxiv.org/pdf/2312.00752
Albert Gu, Tri Dao
cs.LG, cs.AI
null
null
cs.LG
20231201
20231201
"# Mamba: Linear-Time Sequence Modeling with Selective State Spaces\n# Albert Gu*1 and Tri Dao*2\n1M(...TRUNCATED)
{ "id": "2302.13971" }
2311.15296
"UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generati(...TRUNCATED)
"Large language models (LLMs) have emerged as pivotal contributors in\ncontemporary natural language(...TRUNCATED)
http://arxiv.org/pdf/2311.15296
"Xun Liang, Shichao Song, Simin Niu, Zhiyu Li, Feiyu Xiong, Bo Tang, Zhaohui Wy, Dawei He, Peng Chen(...TRUNCATED)
cs.CL
13 Pages, submitted to ICDE2024
null
cs.CL
20231126
20231126
"3 2 0 2\nv o N 6 2 ] L C . s c [ 1 v 6 9 2 5 1 . 1 1 3 2 : v i X r a\n# UHGEval: Benchmarking the H(...TRUNCATED)
{ "id": "2307.03109" }
2311.04254
Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation
"Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breakin(...TRUNCATED)
http://arxiv.org/pdf/2311.04254
"Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qin(...TRUNCATED)
cs.AI, cs.LG
17 pages, 5 figures
null
cs.AI
20231107
20231112
"3 2 0 2\nv o N 2 1 ] I A . s c [\n2 v 4 5 2 4 0 . 1 1 3 2 : v i X r a\n\nEVERYTHING OF THOUGHTS : D(...TRUNCATED)
{ "id": "1706.06708" }
2311.04072
Beyond Imitation: Leveraging Fine-grained Quality Signals for Alignment
"Alignment with human preference is a desired property of large language\nmodels (LLMs). Currently, (...TRUNCATED)
http://arxiv.org/pdf/2311.04072
Geyang Guo, Ranchi Zhao, Tianyi Tang, Wayne Xin Zhao, Ji-Rong Wen
cs.CL
null
null
cs.CL
20231107
20231107
"3 2 0 2\n# v o N 7\n# ] L C . s c [\n1 v 2 7 0 4 0 . 1 1 3 2 : v i X r a\nPreprint.\n# BEYOND IMITA(...TRUNCATED)
{ "id": "2309.00267" }
2311.01964
Don't Make Your LLM an Evaluation Benchmark Cheater
"Large language models~(LLMs) have greatly advanced the frontiers of\nartificial intelligence, attai(...TRUNCATED)
http://arxiv.org/pdf/2311.01964
"Kun Zhou, Yutao Zhu, Zhipeng Chen, Wentong Chen, Wayne Xin Zhao, Xu Chen, Yankai Lin, Ji-Rong Wen, (...TRUNCATED)
cs.CL, cs.AI
11 pages
null
cs.CL
20231103
20231103
"3 2 0 2\nv o N 3 ] L C . s c [\n1 v 4 6 9 1 0 . 1 1 3 2 : v i X r a\n# Don’t Make Your LLM an (...TRUNCATED)
{ "id": "2310.18018" }
2311.04915
"Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Mod(...TRUNCATED)
"We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psych(...TRUNCATED)
http://arxiv.org/pdf/2311.04915
Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn
cs.CL, cs.AI, cs.HC
null
null
cs.CL
20231102
20231214
"# Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy M(...TRUNCATED)
{ "id": "2302.13971" }
2311.01555
Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers
"Recent studies have demonstrated the great potential of Large Language Models\n(LLMs) serving as ze(...TRUNCATED)
http://arxiv.org/pdf/2311.01555
"Weiwei Sun, Zheng Chen, Xinyu Ma, Lingyong Yan, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yi(...TRUNCATED)
cs.IR, cs.CL
null
null
cs.IR
20231102
20231102
"3 2 0 2 v o N 2 ] R I . s c [\n1 v 5 5 5 1 0 . 1 1 3 2 : v i X r a\n# Instruction Distillation Make(...TRUNCATED)
{ "id": "2210.11416" }
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