7 citations · 16 across the 19 of their papers we have counts for
19 papers
DFlare: Scaling Up Draft Capacity for Block Diffusion Speculative Decoding
Jiebin Zhang, Zhenghan Yu, Song Liu +9
Block diffusion speculative decoding accelerates LLM inference by predicting all tokens within a block simultaneously for the target model to verify in parallel. Predicting an enti…
Video2GUI: Synthesizing Large-Scale Interaction Trajectories for Generalized GUI Agent Pretraining
Weimin Xiong, Shuhao Gu, Bowen Ye +5
Recent advances in multimodal large language models have driven growing interest in graphical user interface (GUI) agents, yet their generalization remains constrained by the scarc…
Learning to Draft: Adaptive Speculative Decoding with Reinforcement Learning
Jiebin Zhang, Zhenghan Yu, Liang Wang +8
Speculative decoding accelerates large language model (LLM) inference by using a small draft model to generate candidate tokens for a larger target model to verify. The efficacy of…
Hierarchical Memory Organization for Wikipedia Generation
Eugene J. Yu, Dawei Zhu, Yifan Song +6
Generating Wikipedia articles autonomously is a challenging task requiring the integration of accurate, comprehensive, and well-structured information from diverse sources. This pa…
More Vulnerable than You Think: On the Stability of Tool-Integrated LLM Agents
Weimin Xiong, Ke Wang, Yifan Song +4
Current evaluations of tool-integrated LLM agents typically focus on end-to-end tool-usage evaluation while neglecting their stability. This limits their real-world applicability,…
A Comprehensive Survey on Long Context Language Modeling
Jiaheng Liu, Dawei Zhu, Zhiqi Bai +34
Efficient processing of long contexts has been a persistent pursuit in Natural Language Processing. With the growing number of long documents, dialogues, and other textual data, it…
MPO: Boosting LLM Agents with Meta Plan Optimization
Weimin Xiong, Yifan Song, Qingxiu Dong +4
Recent advancements in large language models (LLMs) have enabled LLM-based agents to successfully tackle interactive planning tasks. However, despite their successes, existing appr…
More Tokens, Lower Precision: Towards the Optimal Token-Precision Trade-off in KV Cache Compression
Jiebin Zhang, Dawei Zhu, Yifan Song +6
As large language models (LLMs) process increasing context windows, the memory usage of KV cache has become a critical bottleneck during inference. The mainstream KV compression me…
VL-RewardBench: A Challenging Benchmark for Vision-Language Generative Reward Models
Lei Li, Yuancheng Wei, Zhihui Xie +9
Vision-language generative reward models (VL-GenRMs) play a crucial role in aligning and evaluating multimodal AI systems, yet their own evaluation remains under-explored. Current…
AgentBank: Towards Generalized LLM Agents via Fine-Tuning on 50000+ Interaction Trajectories
Yifan Song, Weimin Xiong, Xiutian Zhao +6
Fine-tuning on agent-environment interaction trajectory data holds significant promise for surfacing generalized agent capabilities in open-source large language models (LLMs). In…
The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinism
Yifan Song, Guoyin Wang, Sujian Li +1
Current evaluations of large language models (LLMs) often overlook non-determinism, typically focusing on a single output per example. This limits our understanding of LLM performa…
EERPD: Leveraging Emotion and Emotion Regulation for Improving Personality Detection
Zheng Li, Dawei Zhu, Qilong Ma +2
Personality is a fundamental construct in psychology, reflecting an individual's behavior, thinking, and emotional patterns. Previous researches have made some progress in personal…
Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement
Weimin Xiong, Yifan Song, Xiutian Zhao +6
Large language model agents have exhibited exceptional performance across a range of complex interactive tasks. Recent approaches have utilized tuning with expert trajectories to e…
Long Context Alignment with Short Instructions and Synthesized Positions
Wenhao Wu, Yizhong Wang, Yao Fu +3
Effectively handling instructions with extremely long context remains a challenge for Large Language Models (LLMs), typically necessitating high-quality long data and substantial c…
LongEmbed: Extending Embedding Models for Long Context Retrieval
Dawei Zhu, Liang Wang, Nan Yang +4
Embedding models play a pivot role in modern NLP applications such as IR and RAG. While the context limit of LLMs has been pushed beyond 1 million tokens, embedding models are stil…
CoUDA: Coherence Evaluation via Unified Data Augmentation
Dawei Zhu, Wenhao Wu, Yifan Song +3
Coherence evaluation aims to assess the organization and structure of a discourse, which remains challenging even in the era of large language models. Due to the scarcity of annota…
Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents
Yifan Song, Da Yin, Xiang Yue +3
Large Language Models (LLMs) have become integral components in various autonomous agent systems. In this study, we present an exploration-based trajectory optimization approach, r…
Rationale-Enhanced Language Models are Better Continual Relation Learners
Weimin Xiong, Yifan Song, Peiyi Wang +1
Continual relation extraction (CRE) aims to solve the problem of catastrophic forgetting when learning a sequence of newly emerging relations. Recent CRE studies have found that ca…
InfoCL: Alleviating Catastrophic Forgetting in Continual Text Classification from An Information Theoretic Perspective
Yifan Song, Peiyi Wang, Weimin Xiong +4
Continual learning (CL) aims to constantly learn new knowledge over time while avoiding catastrophic forgetting on old tasks. We focus on continual text classification under the cl…