Zobrazeno 1 - 10
of 657
pro vyhledávání: '"Golovneva, A."'
Autor:
Wang, Tianlu, Kulikov, Ilia, Golovneva, Olga, Yu, Ping, Yuan, Weizhe, Dwivedi-Yu, Jane, Pang, Richard Yuanzhe, Fazel-Zarandi, Maryam, Weston, Jason, Li, Xian
Model-based evaluation is at the heart of successful model development -- as a reward model for training, and as a replacement for human evaluation. To train such evaluators, the standard approach is to collect a large amount of human preference judg
Externí odkaz:
http://arxiv.org/abs/2408.02666
Autor:
Wu, Tianhao, Yuan, Weizhe, Golovneva, Olga, Xu, Jing, Tian, Yuandong, Jiao, Jiantao, Weston, Jason, Sukhbaatar, Sainbayar
Large Language Models (LLMs) are rapidly surpassing human knowledge in many domains. While improving these models traditionally relies on costly human data, recent self-rewarding mechanisms (Yuan et al., 2024) have shown that LLMs can improve by judg
Externí odkaz:
http://arxiv.org/abs/2407.19594
The attention mechanism is a critical component of Large Language Models (LLMs) that allows tokens in a sequence to interact with each other, but is order-invariant. Incorporating position encoding (PE) makes it possible to address by position, such
Externí odkaz:
http://arxiv.org/abs/2405.18719
Large language models (LLMs) have a surprising failure: when trained on "A has a feature B", they do not generalize to "B is a feature of A", which is termed the Reversal Curse. Even when training with trillions of tokens this issue still appears due
Externí odkaz:
http://arxiv.org/abs/2403.13799
Autor:
Sukhbaatar, Sainbayar, Golovneva, Olga, Sharma, Vasu, Xu, Hu, Lin, Xi Victoria, Rozière, Baptiste, Kahn, Jacob, Li, Daniel, Yih, Wen-tau, Weston, Jason, Li, Xian
We investigate efficient methods for training Large Language Models (LLMs) to possess capabilities in multiple specialized domains, such as coding, math reasoning and world knowledge. Our method, named Branch-Train-MiX (BTX), starts from a seed model
Externí odkaz:
http://arxiv.org/abs/2403.07816
Autor:
Gao, Silin, Dwivedi-Yu, Jane, Yu, Ping, Tan, Xiaoqing Ellen, Pasunuru, Ramakanth, Golovneva, Olga, Sinha, Koustuv, Celikyilmaz, Asli, Bosselut, Antoine, Wang, Tianlu
To achieve faithful reasoning that aligns with human expectations, large language models (LLMs) need to ground their reasoning to real-world knowledge (e.g., web facts, math and physical rules). Tools help LLMs access this external knowledge, but the
Externí odkaz:
http://arxiv.org/abs/2401.17464
Autor:
Golovneva, Olga, O'Brien, Sean, Pasunuru, Ramakanth, Wang, Tianlu, Zettlemoyer, Luke, Fazel-Zarandi, Maryam, Celikyilmaz, Asli
With recent advancements in large language models, methods like chain-of-thought prompting to elicit reasoning chains have been shown to improve results on reasoning tasks. However, tasks that require multiple steps of reasoning still pose significan
Externí odkaz:
http://arxiv.org/abs/2312.05180
Autor:
Wang, Peifang, Golovneva, Olga, Aghajanyan, Armen, Ren, Xiang, Chen, Muhao, Celikyilmaz, Asli, Fazel-Zarandi, Maryam
Visual language reasoning requires a system to extract text or numbers from information-dense images like charts or plots and perform logical or arithmetic reasoning to arrive at an answer. To tackle this task, existing work relies on either (1) an e
Externí odkaz:
http://arxiv.org/abs/2310.02804
Autor:
Yu, Lili, Shi, Bowen, Pasunuru, Ramakanth, Muller, Benjamin, Golovneva, Olga, Wang, Tianlu, Babu, Arun, Tang, Binh, Karrer, Brian, Sheynin, Shelly, Ross, Candace, Polyak, Adam, Howes, Russell, Sharma, Vasu, Xu, Puxin, Tamoyan, Hovhannes, Ashual, Oron, Singer, Uriel, Li, Shang-Wen, Zhang, Susan, James, Richard, Ghosh, Gargi, Taigman, Yaniv, Fazel-Zarandi, Maryam, Celikyilmaz, Asli, Zettlemoyer, Luke, Aghajanyan, Armen
We present CM3Leon (pronounced "Chameleon"), a retrieval-augmented, token-based, decoder-only multi-modal language model capable of generating and infilling both text and images. CM3Leon uses the CM3 multi-modal architecture but additionally shows th
Externí odkaz:
http://arxiv.org/abs/2309.02591
Autor:
Wang, Tianlu, Yu, Ping, Tan, Xiaoqing Ellen, O'Brien, Sean, Pasunuru, Ramakanth, Dwivedi-Yu, Jane, Golovneva, Olga, Zettlemoyer, Luke, Fazel-Zarandi, Maryam, Celikyilmaz, Asli
As large language models improve, there is increasing interest in techniques that leverage these models' capabilities to refine their own outputs. In this work, we introduce Shepherd, a language model specifically tuned to critique responses and sugg
Externí odkaz:
http://arxiv.org/abs/2308.04592