Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Xiao, Ziang"'
While finetuning language models from pairwise preferences has proven remarkably effective, the underspecified nature of natural language presents critical challenges. Direct preference feedback is uninterpretable, difficult to provide where multidim
Externí odkaz:
http://arxiv.org/abs/2407.14916
With Retrieval Augmented Generation (RAG), Large Language Models (LLMs) are playing a pivotal role in information search and are being adopted globally. Although the multilingual capability of LLMs offers new opportunities to bridge the language barr
Externí odkaz:
http://arxiv.org/abs/2407.05502
Autor:
Liu, Yu Lu, Blodgett, Su Lin, Cheung, Jackie Chi Kit, Liao, Q. Vera, Olteanu, Alexandra, Xiao, Ziang
Benchmarking is seen as critical to assessing progress in NLP. However, creating a benchmark involves many design decisions (e.g., which datasets to include, which metrics to use) that often rely on tacit, untested assumptions about what the benchmar
Externí odkaz:
http://arxiv.org/abs/2406.08723
Autor:
Wang, Ruoyao, Todd, Graham, Xiao, Ziang, Yuan, Xingdi, Côté, Marc-Alexandre, Clark, Peter, Jansen, Peter
Virtual environments play a key role in benchmarking advances in complex planning and decision-making tasks but are expensive and complicated to build by hand. Can current language models themselves serve as world simulators, correctly predicting how
Externí odkaz:
http://arxiv.org/abs/2406.06485
Large language models (LLMs) powered conversational search systems have already been used by hundreds of millions of people, and are believed to bring many benefits over conventional search. However, while decades of research and public discourse int
Externí odkaz:
http://arxiv.org/abs/2402.05880
Autor:
Sordoni, Alessandro, Yuan, Xingdi, Côté, Marc-Alexandre, Pereira, Matheus, Trischler, Adam, Xiao, Ziang, Hosseini, Arian, Niedtner, Friederike, Roux, Nicolas Le
Large language models (LLMs) can be seen as atomic units of computation mapping sequences to a distribution over sequences. Thus, they can be seen as stochastic language layers in a language network, where the learnable parameters are the natural lan
Externí odkaz:
http://arxiv.org/abs/2306.12509
Autor:
Liao, Q. Vera, Xiao, Ziang
The recent development of generative and large language models (LLMs) poses new challenges for model evaluation that the research community and industry are grappling with. While the versatile capabilities of these models ignite excitement, they also
Externí odkaz:
http://arxiv.org/abs/2306.03100
We address a fundamental challenge in Natural Language Generation (NLG) model evaluation -- the design and evaluation of evaluation metrics. Recognizing the limitations of existing automatic metrics and noises from how current human evaluation was co
Externí odkaz:
http://arxiv.org/abs/2305.14889
In this work, we investigate the capacity of language models to generate explicit, interpretable, and interactive world models of scientific and common-sense reasoning tasks. We operationalize this as a task of generating text games, expressed as hun
Externí odkaz:
http://arxiv.org/abs/2305.14879
Qualitative analysis of textual contents unpacks rich and valuable information by assigning labels to the data. However, this process is often labor-intensive, particularly when working with large datasets. While recent AI-based tools demonstrate uti
Externí odkaz:
http://arxiv.org/abs/2304.10548