18 citations · 31 across the 25 of their papers we have counts for
26 papers
KARL: Mitigating Hallucinations in LLMs via Knowledge-Boundary-Aware Reinforcement Learning
Cheng Gao, Cheng Huang, Kangyang Luo +5
Enabling large language models (LLMs) to appropriately abstain from answering questions beyond their knowledge is crucial for mitigating hallucinations. While existing reinforcemen…
From Context to Skills: Can Language Models Learn from Context Skillfully?
Shuzheng Si, Haozhe Zhao, Yu Lei +10
Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge. This calls for context learning, where LMs directly lear…
From Context to EDUs: Faithful and Structured Context Compression via Elementary Discourse Unit Decomposition
Yiqing Zhou, Yu Lei, Shuzheng Si +7
Managing extensive context remains a critical bottleneck for Large Language Models (LLMs), particularly in applications like long-document question answering and autonomous agents…
BabyVision: Visual Reasoning Beyond Language
Liang Chen, Weichu Xie, Yiyan Liang +27
While humans develop core visual skills long before acquiring language, contemporary Multimodal LLMs (MLLMs) still rely heavily on linguistic priors to compensate for their fragile…
FaithLens: Detecting and Explaining Faithfulness Hallucination
Shuzheng Si, Qingyi Wang, Haozhe Zhao +8
Recognizing whether outputs from large language models (LLMs) contain faithfulness hallucination is crucial for real-world applications, e.g., retrieval-augmented generation and su…
RhinoInsight: Improving Deep Research through Control Mechanisms for Model Behavior and Context
Yu Lei, Shuzheng Si, Wei Wang +4
Large language models are evolving from single-turn responders into tool-using agents capable of sustained reasoning and decision-making for deep research. Prevailing systems adopt…
A Goal Without a Plan Is Just a Wish: Efficient and Effective Global Planner Training for Long-Horizon Agent Tasks
Shuzheng Si, Haozhe Zhao, Kangyang Luo +5
Agents based on large language models (LLMs) struggle with brainless trial-and-error and generating hallucinatory actions due to a lack of global planning in long-horizon tasks. In…
MENTOR: Efficient Multimodal-Conditioned Tuning for Autoregressive Vision Generation Models
Haozhe Zhao, Zefan Cai, Shuzheng Si +5
Recent text-to-image models produce high-quality results but still struggle with precise visual control, balancing multimodal inputs, and requiring extensive training for complex m…
Teaching Large Language Models to Maintain Contextual Faithfulness via Synthetic Tasks and Reinforcement Learning
Shuzheng Si, Haozhe Zhao, Cheng Gao +11
Teaching large language models (LLMs) to be faithful in the provided context is crucial for building reliable information-seeking systems. Therefore, we propose a systematic framew…
GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion
Kangyang Luo, Yuzhuo Bai, Cheng Gao +11
Knowledge Graph Completion (KGC), which aims to infer missing or incomplete facts, is a crucial task for KGs. However, integrating the vital structural information of KGs into Larg…
Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering
Shuzheng Si, Haozhe Zhao, Gang Chen +9
Training LLMs on data containing unfamiliar knowledge during the instruction tuning stage can encourage hallucinations. To address this challenge, we introduce NOVA, a novel framew…
UltraIF: Advancing Instruction Following from the Wild
Kaikai An, Li Sheng, Ganqu Cui +4
Instruction-following made modern large language models (LLMs) helpful assistants. However, the key to taming LLMs on complex instructions remains mysterious, for that there are hu…
Looking Beyond Text: Reducing Language bias in Large Vision-Language Models via Multimodal Dual-Attention and Soft-Image Guidance
Haozhe Zhao, Shuzheng Si, Liang Chen +4
Large vision-language models (LVLMs) have achieved impressive results in various vision-language tasks. However, despite showing promising performance, LVLMs suffer from hallucinat…
GATEAU: Selecting Influential Samples for Long Context Alignment
Shuzheng Si, Haozhe Zhao, Gang Chen +7
Aligning large language models to handle instructions with extremely long contexts has yet to be fully investigated. Previous studies have attempted to scale up the available data…
Rethinking Semantic Parsing for Large Language Models: Enhancing LLM Performance with Semantic Hints
Kaikai An, Shuzheng Si, Helan Hu +4
Semantic Parsing aims to capture the meaning of a sentence and convert it into a logical, structured form. Previous studies show that semantic parsing enhances the performance of s…
UltraEdit: Instruction-based Fine-Grained Image Editing at Scale
Haozhe Zhao, Xiaojian Ma, Liang Chen +7
This paper presents UltraEdit, a large-scale (approximately 4 million editing samples), automatically generated dataset for instruction-based image editing. Our key idea is to addr…
Mitigating Language-Level Performance Disparity in mPLMs via Teacher Language Selection and Cross-lingual Self-Distillation
Haozhe Zhao, Zefan Cai, Shuzheng Si +4
Large-scale multilingual Pretrained Language Models (mPLMs) yield impressive performance on cross-language tasks, yet significant performance disparities exist across different lan…
One-Shot Learning as Instruction Data Prospector for Large Language Models
Yunshui Li, Binyuan Hui, Xiaobo Xia +9
Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality, inadvertently introducing noise that may comp…
ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code
Xiangru Tang, Yuliang Liu, Zefan Cai +21
Despite Large Language Models (LLMs) like GPT-4 achieving impressive results in function-level code generation, they struggle with repository-scale code understanding (e.g., coming…
Improving the Robustness of Distantly-Supervised Named Entity Recognition via Uncertainty-Aware Teacher Learning and Student-Student Collaborative Learning
Shuzheng Si, Helan Hu, Haozhe Zhao +4
Distantly-Supervised Named Entity Recognition (DS-NER) is widely used in real-world scenarios. It can effectively alleviate the burden of annotation by matching entities in existin…
UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Prompt
Yucheng Cai, Wentao Ma, Yuchuan Wu +4
Recent research has shown that multi-task pre-training greatly improves the model's robustness and transfer ability, which is crucial for building a high-quality dialog system. How…
MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning
Haozhe Zhao, Zefan Cai, Shuzheng Si +7
Since the resurgence of deep learning, vision-language models (VLMs) enhanced by large language models (LLMs) have grown exponentially in popularity. However, while LLMs can utiliz…
Mining Clues from Incomplete Utterance: A Query-enhanced Network for Incomplete Utterance Rewriting
Shuzheng Si, Shuang Zeng, Baobao Chang
Incomplete utterance rewriting has recently raised wide attention. However, previous works do not consider the semantic structural information between incomplete utterance and rewr…
SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented Dialogue Agents
Shuzheng Si, Wentao Ma, Haoyu Gao +7
Task-oriented dialogue (TOD) models have made significant progress in recent years. However, previous studies primarily focus on datasets written by annotators, which has resulted…