18 citations · 41 across the 16 of their papers we have counts for
17 papers
EgoMem: Lifelong Memory Agent for Full-duplex Omnimodal Models
Yiqun Yao, Naitong Yu, Xiang Li +7
We introduce EgoMem, the first lifelong memory agent tailored for full-duplex models that process real-time omnimodal streams. EgoMem enables real-time models to recognize multiple…
GTool: Graph Enhanced Tool Planning with Large Language Model
Wenjie Chen, Wenbin Li, Di Yao +3
Tool planning with large language models (LLMs), referring to selecting, organizing, and preparing the tools necessary to complete a user request, bridges the gap between natural l…
Enhance Graph Alignment for Large Language Models
Haitong Luo, Xuying Meng, Suhang Wang +4
Graph-structured data is prevalent in the real world. Recently, due to the powerful emergent capabilities, Large Language Models (LLMs) have shown promising performance in modeling…
Sketch: A Toolkit for Streamlining LLM Operations
Xin Jiang, Xiang Li, Wenjia Ma +8
Large language models (LLMs) represented by GPT family have achieved remarkable success. The characteristics of LLMs lie in their ability to accommodate a wide range of tasks throu…
Open-domain Implicit Format Control for Large Language Model Generation
Yiqun Yao, Wenjia Ma, Xuezhi Fang +7
Controlling the format of outputs generated by large language models (LLMs) is a critical functionality in various applications. Current methods typically employ constrained decodi…
Not All Layers of LLMs Are Necessary During Inference
Siqi Fan, Xin Jiang, Xiang Li +6
Due to the large number of parameters, the inference phase of Large Language Models (LLMs) is resource-intensive. However, not all requests posed to LLMs are equally difficult to h…
Spectral-Based Graph Neural Networks for Complementary Item Recommendation
Haitong Luo, Xuying Meng, Suhang Wang +4
Modeling complementary relationships greatly helps recommender systems to accurately and promptly recommend the subsequent items when one item is purchased. Unlike traditional simi…
FLM-101B: An Open LLM and How to Train It with $100K Budget
Xiang Li, Yiqun Yao, Xin Jiang +10
Large language models (LLMs) are considered important approaches towards foundational machine intelligence, achieving remarkable success in Natural Language Processing and multimod…
FreeLM: Fine-Tuning-Free Language Model
Xiang Li, Xin Jiang, Xuying Meng +2
Pre-trained language models (PLMs) have achieved remarkable success in NLP tasks. Despite the great success, mainstream solutions largely follow the pre-training then finetuning pa…
NetGPT: Generative Pretrained Transformer for Network Traffic
Xuying Meng, Chungang Lin, Yequan Wang +1
All data on the Internet are transferred by network traffic, thus accurately modeling network traffic can help improve network services quality and protect data privacy. Pretrained…
GCRE-GPT: A Generative Model for Comparative Relation Extraction
Yequan Wang, Hengran Zhang, Aixin Sun +1
Given comparative text, comparative relation extraction aims to extract two targets (\eg two cameras) in comparison and the aspect they are compared for (\eg image quality). The ex…
PoKE: Prior Knowledge Enhanced Emotional Support Conversation with Latent Variable
Xiaohan Xu, Xuying Meng, Yequan Wang
Emotional support conversation (ESC) task can utilize various support strategies to help people relieve emotional distress and overcome the problem they face, which has attracted m…
Perplexity from PLM Is Unreliable for Evaluating Text Quality
Yequan Wang, Jiawen Deng, Aixin Sun +1
Recently, amounts of works utilize perplexity~(PPL) to evaluate the quality of the generated text. They suppose that if the value of PPL is smaller, the quality(i.e. fluency) of th…
CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction
Yequan Wang, Xiang Li, Aixin Sun +3
Quotation extraction aims to extract quotations from written text. There are three components in a quotation: source refers to the holder of the quotation, cue is the trigger word(…
Chat-Capsule: A Hierarchical Capsule for Dialog-level Emotion Analysis
Yequan Wang, Xuying Meng, Yiyi Liu +4
Many studies on dialog emotion analysis focus on utterance-level emotion only. These models hence are not optimized for dialog-level emotion detection, i.e. to predict the emotion…
A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict
Yiyi Liu, Yequan Wang, Aixin Sun +3
Sarcasm employs ambivalence, where one says something positive but actually means negative, and vice versa. The essence of sarcasm, which is also a sufficient and necessary conditi…
Peeking the Impact of Points of Interests on Didi
Yonghong Tian, Zeyu Li, Zhiwei Xu +2
Recently, the online car-hailing service, Didi, has emerged as a leader in the sharing economy. Used by passengers and drivers extensive, it becomes increasingly important for the…