1.6k citations · 6.6k across the 108 of their papers we have counts for
108 papers
BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models
Zican Dong, Tianyi Tang, Junyi Li +2
Large language models (LLMs) have achieved dramatic proficiency over NLP tasks with normal length. Recently, multiple studies have committed to extending the context length and enh…
Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark]
Jiawei Jiang, Chengkai Han, Wayne Xin Zhao +1
The field of urban spatial-temporal prediction is advancing rapidly with the development of deep learning techniques and the availability of large-scale datasets. However, challeng…
A Survey on Large Language Model based Autonomous Agents
Lei Wang, Chen Ma, Xueyang Feng +10
Autonomous agents have long been a prominent research focus in both academic and industry communities. Previous research in this field often focuses on training agents with limited…
Towards Effective Ancient Chinese Translation: Dataset, Model, and Evaluation
Geyang Guo, Jiarong Yang, Fengyuan Lu +3
Interpreting ancient Chinese has been the key to comprehending vast Chinese literature, tradition, and civilization. In this paper, we propose Erya for ancient Chinese translation.…
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study
Peiyu Liu, Zikang Liu, Ze-Feng Gao +5
Despite the superior performance, Large Language Models~(LLMs) require significant computational resources for deployment and use. To overcome this issue, quantization methods have…
Alleviating the Long-Tail Problem in Conversational Recommender Systems
Zhipeng Zhao, Kun Zhou, Xiaolei Wang +4
Conversational recommender systems (CRS) aim to provide the recommendation service via natural language conversations. To develop an effective CRS, high-quality CRS datasets are ve…
Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation
Ruiyang Ren, Yuhao Wang, Yingqi Qu +6
Large language models (LLMs) have shown impressive prowess in solving a wide range of tasks with world knowledge. However, it remains unclear how well LLMs are able to perceive the…
Reciprocal Sequential Recommendation
Bowen Zheng, Yupeng Hou, Wayne Xin Zhao +2
Reciprocal recommender system (RRS), considering a two-way matching between two parties, has been widely applied in online platforms like online dating and recruitment. Existing RR…
JiuZhang 2.0: A Unified Chinese Pre-trained Language Model for Multi-task Mathematical Problem Solving
Wayne Xin Zhao, Kun Zhou, Beichen Zhang +8
Although pre-trained language models~(PLMs) have recently advanced the research progress in mathematical reasoning, they are not specially designed as a capable multi-task solver,…
Learning to Imagine: Visually-Augmented Natural Language Generation
Tianyi Tang, Yushuo Chen, Yifan Du +3
People often imagine relevant scenes to aid in the writing process. In this work, we aim to utilize visual information for composition in the same manner as humans. We propose a me…
Improving Conversational Recommendation Systems via Counterfactual Data Simulation
Xiaolei Wang, Kun Zhou, Xinyu Tang +4
Conversational recommender systems (CRSs) aim to provide recommendation services via natural language conversations. Although a number of approaches have been proposed for developi…
Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoning
Beichen Zhang, Kun Zhou, Xilin Wei +4
Chain-of-thought prompting~(CoT) and tool augmentation have been validated in recent work as effective practices for improving large language models~(LLMs) to perform step-by-step…
User Behavior Simulation with Large Language Model based Agents
Lei Wang, Jingsen Zhang, Hao Yang +11
Simulating high quality user behavior data has always been a fundamental problem in human-centered applications, where the major difficulty originates from the intricate mechanism…
Zero-shot Visual Question Answering with Language Model Feedback
Yifan Du, Junyi Li, Tianyi Tang +2
In this paper, we propose a novel language model guided captioning approach, LAMOC, for knowledge-based visual question answering (VQA). Our approach employs the generated captions…
The Web Can Be Your Oyster for Improving Large Language Models
Junyi Li, Tianyi Tang, Wayne Xin Zhao +3
Large language models (LLMs) encode a large amount of world knowledge. However, as such knowledge is frozen at the time of model training, the models become static and limited by t…
TOME: A Two-stage Approach for Model-based Retrieval
Ruiyang Ren, Wayne Xin Zhao, Jing Liu +3
Recently, model-based retrieval has emerged as a new paradigm in text retrieval that discards the index in the traditional retrieval model and instead memorizes the candidate corpo…
Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach
Junjie Zhang, Ruobing Xie, Yupeng Hou +3
In the past decades, recommender systems have attracted much attention in both research and industry communities, and a large number of studies have been devoted to developing effe…
GlyphDiffusion: Text Generation as Image Generation
Junyi Li, Wayne Xin Zhao, Jian-Yun Nie +1
Diffusion models have become a new generative paradigm for text generation. Considering the discrete categorical nature of text, in this paper, we propose GlyphDiffusion, a novel d…
Diffusion-NAT: Self-Prompting Discrete Diffusion for Non-Autoregressive Text Generation
Kun Zhou, Yifan Li, Wayne Xin Zhao +1
Recently, continuous diffusion models (CDM) have been introduced into non-autoregressive (NAR) text-to-text generation. However, the discrete nature of text increases the difficult…
ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models
Zhipeng Chen, Kun Zhou, Beichen Zhang +3
Although large language models (LLMs) have achieved excellent performance in a variety of evaluation benchmarks, they still struggle in complex reasoning tasks which require specif…
Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models
Xiaolei Wang, Xinyu Tang, Wayne Xin Zhao +2
The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs), which rely on natural language conv…
HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models
Junyi Li, Xiaoxue Cheng, Wayne Xin Zhao +2
Large language models (LLMs), such as ChatGPT, are prone to generate hallucinations, i.e., content that conflicts with the source or cannot be verified by the factual knowledge. To…
Evaluating Object Hallucination in Large Vision-Language Models
Yifan Li, Yifan Du, Kun Zhou +3
Inspired by the superior language abilities of large language models (LLM), large vision-language models (LVLM) have been recently explored by integrating powerful LLMs for improvi…
StructGPT: A General Framework for Large Language Model to Reason over Structured Data
Jinhao Jiang, Kun Zhou, Zican Dong +3
In this paper, we study how to improve the zero-shot reasoning ability of large language models~(LLMs) over structured data in a unified way. Inspired by the study on tool augmenta…