25 citations · 118 across the 22 of their papers we have counts for
8 papers · 1 filter
ViStruct: Visual Structural Knowledge Extraction via Curriculum Guided Code-Vision Representation
Yangyi Chen, Xingyao Wang, Manling Li +2
State-of-the-art vision-language models (VLMs) still have limited performance in structural knowledge extraction, such as relations between objects. In this work, we present ViStru…
Examining LLMs' Uncertainty Expression Towards Questions Outside Parametric Knowledge
Genglin Liu, Xingyao Wang, Lifan Yuan +2
Can large language models (LLMs) express their uncertainty in situations where they lack sufficient parametric knowledge to generate reasonable responses? This work aims to systema…
R-Tuning: Instructing Large Language Models to Say `I Don't Know'
Hanning Zhang, Shizhe Diao, Yong Lin +6
Large language models (LLMs) have revolutionized numerous domains with their impressive performance but still face their challenges. A predominant issue is the propensity for these…
Defining a New NLP Playground
Sha Li, Chi Han, Pengfei Yu +8
The recent explosion of performance of large language models (LLMs) has changed the field of Natural Language Processing (NLP) more abruptly and seismically than any other shift in…
CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets
Lifan Yuan, Yangyi Chen, Xingyao Wang +3
Large language models (LLMs) are often augmented with tools to solve complex tasks. By generating code snippets and executing them through task-specific Application Programming Int…
MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback
Xingyao Wang, Zihan Wang, Jiateng Liu +4
To solve complex tasks, large language models (LLMs) often require multiple rounds of interactions with the user, sometimes assisted by external tools. However, current evaluation…
Making Pre-trained Language Models both Task-solvers and Self-calibrators
Yangyi Chen, Xingyao Wang, Heng Ji
Pre-trained language models (PLMs) serve as backbones for various real-world systems. For high-stake applications, it's equally essential to have reasonable confidence estimations…
LeTI: Learning to Generate from Textual Interactions
Xingyao Wang, Hao Peng, Reyhaneh Jabbarvand +1
Fine-tuning pre-trained language models (LMs) is essential for enhancing their capabilities. Existing techniques commonly fine-tune on input-output pairs (e.g., instruction tuning)…