Search Results for author:
Found 7 papers, 2 papers with code
Rejection Improves Reliability: Training LLMs to Refuse Unknown Questions Using RL from Knowledge Feedback
Large Language Models (LLMs) often generate erroneous outputs, known as hallucinations, due to their limitations in discerning questions beyond their knowledge scope.
Hierarchical Multimodal Pre-training for Visually Rich Webpage Understanding
Additionally, we propose several pre-training tasks to model the interaction among text, structure, and image modalities effectively.
MULTI: Multimodal Understanding Leaderboard with Text and Images
Rapid progress in multimodal large language models (MLLMs) highlights the need to introduce challenging yet realistic benchmarks to the academic community, while existing benchmarks primarily focus on understanding simple natural images and short context.
ChemDFM: Dialogue Foundation Model for Chemistry
To this end, we develop ChemDFM, the first LLM towards CGI.
META-GUI: Towards Multi-modal Conversational Agents on Mobile GUI
However, this API-based architecture greatly limits the information-searching capability of intelligent assistants and may even lead to task failure if TOD-specific APIs are not available or the task is too complicated to be executed by the provided APIs.
Rb-PaStaNet: A Few-Shot Human-Object Interaction Detection Based on Rules and Part States
Existing Human-Object Interaction (HOI) Detection approaches have achieved great progress on nonrare classes while rare HOI classes are still not well-detected.
Learning Key-Value Store Design
The critical insight and potential long-term impact is that such unifying models 1) render what we consider up to now as fundamentally different data structures to be seen as views of the very same overall design space, and 2) allow seeing new data structure designs with performance properties that are not feasible by existing designs.
