21 citations · 39 across the 13 of their papers we have counts for
13 papers
LUT: Latent Utility Training for Visual Reasoning
Jiaxuan Kang, Siyu Chen, Mingda Li +6
Multimodal large language models have advanced visual understanding, yet perception-intensive reasoning remains challenging. Recent latent visual reasoning methods introduce hidden…
Gradients with Respect to Semantics Preserving Embeddings Tell the Uncertainty of Large Language Models
Mingda Li, Rundong Lv, Xinyu Li +2
Uncertainty quantification (UQ) is an important technique for ensuring the trustworthiness of LLMs, given their tendency to hallucinate. Existing state-of-the-art UQ approaches for…
Yunque DeepResearch Technical Report
Yuxuan Cai, Xinyi Lai, Peng Yuan +8
Deep research has emerged as a transformative capability for autonomous agents, empowering Large Language Models to navigate complex, open-ended tasks. However, realizing its full…
ESI: Epistemic Uncertainty Quantification via Semantic-preserving Intervention for Large Language Models
Mingda Li, Xinyu Li, Weinan Zhang +1
Uncertainty Quantification (UQ) is a promising approach to improve model reliability, yet quantifying the uncertainty of Large Language Models (LLMs) is non-trivial. In this work,…
Unstructured Text Enhanced Open-domain Dialogue System: A Systematic Survey
Longxuan Ma, Mingda Li, Weinan Zhang +2
Incorporating external knowledge into dialogue generation has been proven to benefit the performance of an open-domain Dialogue System (DS), such as generating informative or styli…
Policy-driven Knowledge Selection and Response Generation for Document-grounded Dialogue
Longxuan Ma, Jiapeng Li, Mingda Li +2
Document-grounded dialogue (DGD) uses documents as external knowledge for dialogue generation. Correctly understanding the dialogue context is crucial for selecting knowledge from…
Bridging the Language Gap: Enhancing Multilingual Prompt-Based Code Generation in LLMs via Zero-Shot Cross-Lingual Transfer
Mingda Li, Abhijit Mishra, Utkarsh Mujumdar
The use of Large Language Models (LLMs) for program code generation has gained substantial attention, but their biases and limitations with non-English prompts challenge global inc…
Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models
Mingda Li, Xinyu Li, Yifan Chen +2
Although Retrieval-Augmented Large Language Models (RALMs) demonstrate their superiority in terms of factuality, they do not consistently outperform the original retrieval-free Lan…
VTG-LLM: Integrating Timestamp Knowledge into Video LLMs for Enhanced Video Temporal Grounding
Yongxin Guo, Jingyu Liu, Mingda Li +6
Video Temporal Grounding (VTG) strives to accurately pinpoint event timestamps in a specific video using linguistic queries, significantly impacting downstream tasks like video bro…
SentinelLMs: Encrypted Input Adaptation and Fine-tuning of Language Models for Private and Secure Inference
Abhijit Mishra, Mingda Li, Soham Deo
This paper addresses the privacy and security concerns associated with deep neural language models, which serve as crucial components in various modern AI-based applications. These…
Through the Lens of Core Competency: Survey on Evaluation of Large Language Models
Ziyu Zhuang, Qiguang Chen, Longxuan Ma +7
From pre-trained language model (PLM) to large language model (LLM), the field of natural language processing (NLP) has witnessed steep performance gains and wide practical uses. T…
SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation
Longxuan Ma, Ziyu Zhuang, Weinan Zhang +2
This paper introduces a novel Self-supervised Fine-grained Dialogue Evaluation framework (SelF-Eval). The core idea is to model the correlation between turn quality and the entire…
Recent Progress in Conversational AI
Zijun Xue, Ruirui Li, Mingda Li
Conversational artificial intelligence (AI) is becoming an increasingly popular topic among industry and academia. With the fast development of neural network-based models, a lot o…