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Found 14 papers, 9 papers with code
An Energy-based Model for Word-level AutoCompletion in Computer-aided Translation
Existing work addresses this task through a classification model based on a neural network that maps the hidden vector of the input context into its corresponding label (i. e., the candidate target word is treated as a label).
On the Transformations across Reward Model, Parameter Update, and In-Context Prompt
Despite the general capabilities of pre-trained large language models (LLMs), they still need further adaptation to better serve practical applications.
ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation
We introduce a new benchmark, ChartMimic, aimed at assessing the visually-grounded code generation capabilities of large multimodal models (LMMs).
Unchosen Experts Can Contribute Too: Unleashing MoE Models' Power by Self-Contrast
In MoE, each token in the input sequence activates a different subset of experts determined by a routing mechanism.
TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild
We release our dataset, model, and demo to foster future research in the area of multimodal instruction following.
Ranked #133 on
Visual Question Answering
on MM-Vet
AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models
Information-seeking conversation, which aims to help users gather information through conversation, has achieved great progress in recent years.
NewsDialogues: Towards Proactive News Grounded Conversation
In this paper, we propose a novel task, Proactive News Grounded Conversation, in which a dialogue system can proactively lead the conversation based on some key topics of the news.
WiCo: Win-win Cooperation of Bottom-up and Top-down Referring Image Segmentation
Bottom-up methods are mainly perturbed by Inferior Positive (IP) errors due to the lack of prior object information.
Question Answering as Programming for Solving Time-Sensitive Questions
Question answering plays a pivotal role in human daily life because it involves our acquisition of knowledge about the world.
Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation is typically inspired by class activation maps, which serve as pseudo masks with class-discriminative regions highlighted.
Optical Character Recognition (OCR)
Weakly supervised Semantic Segmentation
+1
Modeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity Retrieval
We extract the knowledge units from the corresponding context and then construct a mention/entity centralized graph.
MCSCSet: A Specialist-annotated Dataset for Medical-domain Chinese Spelling Correction
In this work, we define the task of Medical-domain Chinese Spelling Correction and propose MCSCSet, a large scale specialist-annotated dataset that contains about 200k samples.
Optical Character Recognition
Optical Character Recognition (OCR)
+1
A New Approach to Training Multiple Cooperative Agents for Autonomous Driving
Training multiple agents to perform safe and cooperative control in the complex scenarios of autonomous driving has been a challenge.
EmpHi: Generating Empathetic Responses with Human-like Intents
To address the bias of the empathetic intents distribution between empathetic dialogue models and humans, we propose a novel model to generate empathetic responses with human-consistent empathetic intents, EmpHi for short.
