146 citations · 332 across the 18 of their papers we have counts for
18 papers
ToolTree: Efficient LLM Agent Tool Planning via Dual-Feedback Monte Carlo Tree Search and Bidirectional Pruning
Shuo Yang, Soyeon Caren Han, Yihao Ding +2
Large Language Model (LLM) agents are increasingly applied to complex, multi-step tasks that require interaction with diverse external tools across various domains. However, curren…
Spider-Sense: Intrinsic Risk Sensing for Efficient Agent Defense with Hierarchical Adaptive Screening
Zhenxiong Yu, Zhi Yang, Zhiheng Jin +19
As large language models (LLMs) evolve into autonomous agents, their real-world applicability has expanded significantly, accompanied by new security challenges. Most existing agen…
KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions
Tingyu Wu, Zhisheng Chen, Ziyan Weng +8
Existing long-horizon memory benchmarks mostly use multi-turn dialogues or synthetic user histories, which makes retrieval performance an imperfect proxy for person understanding.…
FaceID-6M: A Large-Scale, Open-Source FaceID Customization Dataset
Shuhe Wang, Xiaoya Li, Jiwei Li +8
Due to the data-driven nature of current face identity (FaceID) customization methods, all state-of-the-art models rely on large-scale datasets containing millions of high-quality…
Turn That Frown Upside Down: FaceID Customization via Cross-Training Data
Shuhe Wang, Xiaoya Li, Xiaofei Sun +4
Existing face identity (FaceID) customization methods perform well but are limited to generating identical faces as the input, while in real-world applications, users often desire…
Reinforcement Learning Enhanced LLMs: A Survey
Shuhe Wang, Shengyu Zhang, Jie Zhang +7
Reinforcement learning (RL) enhanced large language models (LLMs), particularly exemplified by DeepSeek-R1, have exhibited outstanding performance. Despite the effectiveness in imp…
Packing Analysis: Packing Is More Appropriate for Large Models or Datasets in Supervised Fine-tuning
Shuhe Wang, Guoyin Wang, Yizhong Wang +3
Packing, initially utilized in the pre-training phase, is an optimization technique designed to maximize hardware resource efficiency by combining different training sequences to f…
Sim-GPT: Text Similarity via GPT Annotated Data
Shuhe Wang, Beiming Cao, Shengyu Zhang +5
Due to the lack of a large collection of high-quality labeled sentence pairs with textual similarity scores, existing approaches for Semantic Textual Similarity (STS) mostly rely o…
Sentiment Analysis through LLM Negotiations
Xiaofei Sun, Xiaoya Li, Shengyu Zhang +5
A standard paradigm for sentiment analysis is to rely on a singular LLM and makes the decision in a single round under the framework of in-context learning. This framework suffers…
Instruction Tuning for Large Language Models: A Survey
Shengyu Zhang, Linfeng Dong, Xiaoya Li +8
This paper surveys research works in the quickly advancing field of instruction tuning (IT), which can also be referred to as supervised fine-tuning (SFT)\footnote{In this paper, u…
Pushing the Limits of ChatGPT on NLP Tasks
Xiaofei Sun, Linfeng Dong, Xiaoya Li +8
Despite the success of ChatGPT, its performances on most NLP tasks are still well below the supervised baselines. In this work, we looked into the causes, and discovered that its s…
GPT-NER: Named Entity Recognition via Large Language Models
Shuhe Wang, Xiaofei Sun, Xiaoya Li +5
Despite the fact that large-scale Language Models (LLM) have achieved SOTA performances on a variety of NLP tasks, its performance on NER is still significantly below supervised ba…
GNN-SL: Sequence Labeling Based on Nearest Examples via GNN
Shuhe Wang, Yuxian Meng, Rongbin Ouyang +4
To better handle long-tail cases in the sequence labeling (SL) task, in this work, we introduce graph neural networks sequence labeling (GNN-SL), which augments the vanilla SL mode…
NN-NER: Named Entity Recognition with Nearest Neighbor Search
Shuhe Wang, Xiaoya Li, Yuxian Meng +4
Inspired by recent advances in retrieval augmented methods in NLP~\citep{khandelwal2019generalization,khandelwal2020nearest,meng2021gnn}, in this paper, we introduce a nearest…
Faster Nearest Neighbor Machine Translation
Shuhe Wang, Jiwei Li, Yuxian Meng +5
NN based neural machine translation (NN-MT) has achieved state-of-the-art results in a variety of MT tasks. One significant shortcoming of NN-MT lies in its inefficiency i…
OpenViDial 2.0: A Larger-Scale, Open-Domain Dialogue Generation Dataset with Visual Contexts
Shuhe Wang, Yuxian Meng, Xiaoya Li +3
In order to better simulate the real human conversation process, models need to generate dialogue utterances based on not only preceding textual contexts but also visual contexts.…
Modeling Text-visual Mutual Dependency for Multi-modal Dialog Generation
Shuhe Wang, Yuxian Meng, Xiaofei Sun +5
Multi-modal dialog modeling is of growing interest. In this work, we propose frameworks to resolve a specific case of multi-modal dialog generation that better mimics multi-modal d…
OpenViDial: A Large-Scale, Open-Domain Dialogue Dataset with Visual Contexts
Yuxian Meng, Shuhe Wang, Qinghong Han +4
When humans converse, what a speaker will say next significantly depends on what he sees. Unfortunately, existing dialogue models generate dialogue utterances only based on precedi…