49 citations · 104 across the 32 of their papers we have counts for
32 papers
RS-Gen: A Multi-Stage Agentic Framework for Reasoning and Search-Augmented Image Generation
Feifei Bian, Zhimin Zheng, Wei Deng +2
Recent years have witnessed remarkable progress in image generation and editing, particularly regarding instruction following and visual fidelity. However, when handling ambiguous…
Group-Graph Policy Optimization for Long-Horizon Agentic Reinforcement Learning
Yunan Wang, Minghui Song, Zihan Zhang +6
Group-based Reinforcement Learning (RL) has significantly enhanced Large Language Models (LLMs) in agentic scenarios. To achieve finer-grained policy updates, recent agentic RL fra…
STAR: SpatioTemporal Adaptive Reward Allocation for Text-to-Image RL Post-Training
Jinjie Shen, Wei Deng, Xian Hu +2
Existing RL post-training methods for text-to-image generation usually convert the final-image reward into a single scalar advantage and apply it with the same strength to the enti…
Beyond Static Dialogues: Benchmarking Realistic, Heterogeneous, and Evolving Long-Term Memory
Han Zhang, Zihao Tang, Xin Yu +8
In existing memory benchmarks for Large Language Models (LLMs), the evaluated dialogue sessions often lack long-term semantic consistency, and the underlying personas tend to be fl…
DUET: Joint Exploration of User Item Profiles in Recommendation System
Yue Chen, Yifei Sun, Lu Wang +17
Traditional recommendation systems represent users and items as dense vectors and learn to align them in a shared latent space for relevance estimation. Recent LLM-based recommende…
LettinGo: Explore User Profile Generation for Recommendation System
Lu Wang, Di Zhang, Fangkai Yang +9
User profiling is pivotal for recommendation systems, as it transforms raw user interaction data into concise and structured representations that drive personalized recommendations…
RePrompt: Reasoning-Augmented Reprompting for Text-to-Image Generation via Reinforcement Learning
Mingrui Wu, Lu Wang, Pu Zhao +14
Despite recent progress in text-to-image (T2I) generation, existing models often struggle to faithfully capture user intentions from short and under-specified prompts. While prior…
MAIN: Mutual Alignment Is Necessary for instruction tuning
Fanyi Yang, Jianfeng Liu, Xin Zhang +7
Instruction tuning has empowered large language models (LLMs) to achieve remarkable performance, yet its success heavily depends on the availability of large-scale, high-quality in…
GeAR: Generation Augmented Retrieval
Haoyu Liu, Shaohan Huang, Jianfeng Liu +6
Document retrieval techniques are essential for developing large-scale information systems. The common approach involves using a bi-encoder to compute the semantic similarity betwe…
Context-DPO: Aligning Language Models for Context-Faithfulness
Baolong Bi, Shaohan Huang, Yiwei Wang +11
Reliable responses from large language models (LLMs) require adherence to user instructions and retrieved information. While alignment techniques help LLMs align with human intenti…
StreamAdapter: Efficient Test Time Adaptation from Contextual Streams
Dilxat Muhtar, Yelong Shen, Yaming Yang +11
In-context learning (ICL) allows large language models (LLMs) to adapt to new tasks directly from the given demonstrations without requiring gradient updates. While recent advances…
Token-level Proximal Policy Optimization for Query Generation
Yichen Ouyang, Lu Wang, Fangkai Yang +13
Query generation is a critical task for web search engines (e.g. Google, Bing) and recommendation systems. Recently, state-of-the-art query generation methods leverage Large Langua…
E5-V: Universal Embeddings with Multimodal Large Language Models
Ting Jiang, Minghui Song, Zihan Zhang +6
Multimodal large language models (MLLMs) have shown promising advancements in general visual and language understanding. However, the representation of multimodal information using…
ASI++: Towards Distributionally Balanced End-to-End Generative Retrieval
Yuxuan Liu, Tianchi Yang, Zihan Zhang +5
Generative retrieval, a promising new paradigm in information retrieval, employs a seq2seq model to encode document features into parameters and decode relevant document identifier…
MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning
Ting Jiang, Shaohan Huang, Shengyue Luo +8
Low-rank adaptation is a popular parameter-efficient fine-tuning method for large language models. In this paper, we analyze the impact of low-rank updating, as implemented in LoRA…
ResLoRA: Identity Residual Mapping in Low-Rank Adaption
Shuhua Shi, Shaohan Huang, Minghui Song +7
As one of the most popular parameter-efficient fine-tuning (PEFT) methods, low-rank adaptation (LoRA) is commonly applied to fine-tune large language models (LLMs). However, updati…
HD-Eval: Aligning Large Language Model Evaluators Through Hierarchical Criteria Decomposition
Yuxuan Liu, Tianchi Yang, Shaohan Huang +6
Large language models (LLMs) have emerged as a promising alternative to expensive human evaluations. However, the alignment and coverage of LLM-based evaluations are often limited…
Text Diffusion with Reinforced Conditioning
Yuxuan Liu, Tianchi Yang, Shaohan Huang +6
Diffusion models have demonstrated exceptional capability in generating high-quality images, videos, and audio. Due to their adaptiveness in iterative refinement, they provide a st…
: Sequential Example Selection for In-Context Learning
Haoyu Liu, Jianfeng Liu, Shaohan Huang +5
The remarkable capability of large language models (LLMs) for in-context learning (ICL) needs to be activated by demonstration examples. Prior work has extensively explored the sel…
Improving Domain Adaptation through Extended-Text Reading Comprehension
Ting Jiang, Shaohan Huang, Shengyue Luo +8
To enhance the domain-specific capabilities of large language models, continued pre-training on a domain-specific corpus is a prevalent method. Recent work demonstrates that adapti…
Auto Search Indexer for End-to-End Document Retrieval
Tianchi Yang, Minghui Song, Zihan Zhang +4
Generative retrieval, which is a new advanced paradigm for document retrieval, has recently attracted research interests, since it encodes all documents into the model and directly…
Democratizing Reasoning Ability: Tailored Learning from Large Language Model
Zhaoyang Wang, Shaohan Huang, Yuxuan Liu +8
Large language models (LLMs) exhibit impressive emergent abilities in natural language processing, but their democratization is hindered due to huge computation requirements and cl…
Calibrating LLM-Based Evaluator
Yuxuan Liu, Tianchi Yang, Shaohan Huang +6
Recent advancements in large language models (LLMs) on language modeling and emergent capabilities make them a promising reference-free evaluator of natural language generation qua…
Model-enhanced Vector Index
Hailin Zhang, Yujing Wang, Qi Chen +16
Embedding-based retrieval methods construct vector indices to search for document representations that are most similar to the query representations. They are widely used in docume…