Zobrazeno 1 - 10
of 735
pro vyhledávání: '"Ye, Xi"'
Autor:
Sprague, Zayne, Yin, Fangcong, Rodriguez, Juan Diego, Jiang, Dongwei, Wadhwa, Manya, Singhal, Prasann, Zhao, Xinyu, Ye, Xi, Mahowald, Kyle, Durrett, Greg
Chain-of-thought (CoT) via prompting is the de facto method for eliciting reasoning capabilities from large language models (LLMs). But for what kinds of tasks is this extra ``thinking'' really helpful? To analyze this, we conducted a quantitative me
Externí odkaz:
http://arxiv.org/abs/2409.12183
Large language models (LLMs) are increasingly being used to synthesize and reason about source code. However, the static nature of these models' knowledge does not reflect the fact that libraries and API functions they invoke are continuously evolvin
Externí odkaz:
http://arxiv.org/abs/2407.06249
Recent work in interpretability shows that large language models (LLMs) can be adapted for new tasks in a learning-free way: it is possible to intervene on LLM representations to elicit desired behaviors for alignment. For instance, adding certain bi
Externí odkaz:
http://arxiv.org/abs/2406.01563
Autor:
Zhang, Xinlu, Chen, Zhiyu Zoey, Ye, Xi, Yang, Xianjun, Chen, Lichang, Wang, William Yang, Petzold, Linda Ruth
Instruction Fine-Tuning (IFT) significantly enhances the zero-shot capabilities of pretrained Large Language Models (LLMs). While coding data is known to boost reasoning abilities during LLM pretraining, its role in activating internal reasoning capa
Externí odkaz:
http://arxiv.org/abs/2405.20535
In the vast and dynamic landscape of urban settings, Traffic Safety Description and Analysis plays a pivotal role in applications ranging from insurance inspection to accident prevention. This paper introduces CityLLaVA, a novel fine-tuning framework
Externí odkaz:
http://arxiv.org/abs/2405.03194
Different entities with the same name can be difficult to distinguish. Handling confusing entity mentions is a crucial skill for language models (LMs). For example, given the question "Where was Michael Jordan educated?" and a set of documents discus
Externí odkaz:
http://arxiv.org/abs/2404.12447
Autor:
Ye, Xi, Bilodeau, Guillaume-Alexandre
Publikováno v:
AAAI2024
Predicting future frames of a video is challenging because it is difficult to learn the uncertainty of the underlying factors influencing their contents. In this paper, we propose a novel video prediction model, which has infinite-dimensional latent
Externí odkaz:
http://arxiv.org/abs/2312.06486
In-context learning can improve the performances of knowledge-rich tasks such as question answering. In such scenarios, in-context examples trigger a language model (LM) to surface information stored in its parametric knowledge. We study how to bette
Externí odkaz:
http://arxiv.org/abs/2311.09579
Large language models (LLMs) have achieved remarkable advancements in natural language understanding and generation. However, one major issue towards their widespread deployment in the real world is that they can generate "hallucinated" answers that
Externí odkaz:
http://arxiv.org/abs/2311.09533
Publikováno v:
ICLR 2024 (Spotlight)
While large language models (LLMs) equipped with techniques like chain-of-thought prompting have demonstrated impressive capabilities, they still fall short in their ability to reason robustly in complex settings. However, evaluating LLM reasoning is
Externí odkaz:
http://arxiv.org/abs/2310.16049