38 citations · 155 across the 22 of their papers we have counts for
22 papers
Scaling Long-Horizon LLM Agent via Context-Folding
Weiwei Sun, Miao Lu, Zhan Ling +4
Large language model (LLM) agents are fundamentally constrained by context length on long-horizon tasks. We introduce Context-Folding, a framework that empowers agents to actively…
ZeroGR: A Generalizable and Scalable Framework for Zero-Shot Generative Retrieval
Weiwei Sun, Keyi Kong, Xinyu Ma +5
Generative retrieval (GR) reformulates information retrieval (IR) by framing it as the generation of document identifiers (docids), thereby enabling end-to-end optimization and sea…
Direct Retrieval-augmented Optimization: Synergizing Knowledge Selection and Language Models
Zhengliang Shi, Lingyong Yan, Weiwei Sun +7
Retrieval-augmented generation (RAG) integrates large language models ( LLM s) with retrievers to access external knowledge, improving the factuality of LLM generation in knowledge…
Replication and Exploration of Generative Retrieval over Dynamic Corpora
Zhen Zhang, Xinyu Ma, Weiwei Sun +6
Generative retrieval (GR) has emerged as a promising paradigm in information retrieval (IR). However, most existing GR models are developed and evaluated using a static document co…
MAIR: A Massive Benchmark for Evaluating Instructed Retrieval
Weiwei Sun, Zhengliang Shi, Jiulong Wu +6
Recent information retrieval (IR) models are pre-trained and instruction-tuned on massive datasets and tasks, enabling them to perform well on a wide range of tasks and potentially…
MEFT: Memory-Efficient Fine-Tuning through Sparse Adapter
Jitai Hao, WeiWei Sun, Xin Xin +4
Parameter-Efficient Fine-tuning (PEFT) facilitates the fine-tuning of Large Language Models (LLMs) under limited resources. However, the fine-tuning performance with PEFT on comple…
Generate-then-Ground in Retrieval-Augmented Generation for Multi-hop Question Answering
Zhengliang Shi, Weiwei Sun, Shen Gao +3
Multi-Hop Question Answering (MHQA) tasks present a significant challenge for large language models (LLMs) due to the intensive knowledge required. Current solutions, like Retrieva…
Improving the Robustness of Large Language Models via Consistency Alignment
Yukun Zhao, Lingyong Yan, Weiwei Sun +6
Large language models (LLMs) have shown tremendous success in following user instructions and generating helpful responses. Nevertheless, their robustness is still far from optimal…
How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study
Tianjie Ju, Weiwei Sun, Wei Du +3
Previous work has showcased the intriguing capability of large language models (LLMs) in retrieving facts and processing context knowledge. However, only limited research exists on…
Content-Based Collaborative Generation for Recommender Systems
Yidan Wang, Zhaochun Ren, Weiwei Sun +9
Generative models have emerged as a promising utility to enhance recommender systems. It is essential to model both item content and user-item collaborative interactions in a unifi…
Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers
Weiwei Sun, Zheng Chen, Xinyu Ma +6
Recent studies have demonstrated the great potential of Large Language Models (LLMs) serving as zero-shot relevance rankers. The typical approach involves making comparisons betwee…
DiQAD: A Benchmark Dataset for End-to-End Open-domain Dialogue Assessment
Yukun Zhao, Lingyong Yan, Weiwei Sun +5
Dialogue assessment plays a critical role in the development of open-domain dialogue systems. Existing work are uncapable of providing an end-to-end and human-epistemic assessment…
Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method
Yukun Zhao, Lingyong Yan, Weiwei Sun +6
Large Language Models (LLMs) have shown great potential in Natural Language Processing (NLP) tasks. However, recent literature reveals that LLMs generate nonfactual responses inter…
RADE: Reference-Assisted Dialogue Evaluation for Open-Domain Dialogue
Zhengliang Shi, Weiwei Sun, Shuo Zhang +3
Evaluating open-domain dialogue systems is challenging for reasons such as the one-to-many problem, i.e., many appropriate responses other than just the golden response. As of now,…
Answering Ambiguous Questions via Iterative Prompting
Weiwei Sun, Hengyi Cai, Hongshen Chen +4
In open-domain question answering, due to the ambiguity of questions, multiple plausible answers may exist. To provide feasible answers to an ambiguous question, one approach is to…
Towards Explainable Conversational Recommender Systems
Shuyu Guo, Shuo Zhang, Weiwei Sun +3
Explanations in conventional recommender systems have demonstrated benefits in helping the user understand the rationality of the recommendations and improving the system's efficie…
Generative Knowledge Selection for Knowledge-Grounded Dialogues
Weiwei Sun, Pengjie Ren, Zhaochun Ren
Knowledge selection is the key in knowledge-grounded dialogues (KGD), which aims to select an appropriate knowledge snippet to be used in the utterance based on dialogue history. P…
Learning to Tokenize for Generative Retrieval
Weiwei Sun, Lingyong Yan, Zheng Chen +7
Conventional document retrieval techniques are mainly based on the index-retrieve paradigm. It is challenging to optimize pipelines based on this paradigm in an end-to-end manner.…
Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents
Weiwei Sun, Lingyong Yan, Xinyu Ma +5
Large Language Models (LLMs) have demonstrated remarkable zero-shot generalization across various language-related tasks, including search engines. However, existing work utilizes…
Contrastive Learning Reduces Hallucination in Conversations
Weiwei Sun, Zhengliang Shi, Shen Gao +3
Pre-trained language models (LMs) store knowledge in their parameters and can generate informative responses when used in conversational systems. However, LMs suffer from the probl…
Metaphorical User Simulators for Evaluating Task-oriented Dialogue Systems
Weiwei Sun, Shuyu Guo, Shuo Zhang +4
Task-oriented dialogue systems (TDSs) are assessed mainly in an offline setting or through human evaluation. The evaluation is often limited to single-turn or is very time-intensiv…
Simulating User Satisfaction for the Evaluation of Task-oriented Dialogue Systems
Weiwei Sun, Shuo Zhang, Krisztian Balog +4
Evaluation is crucial in the development process of task-oriented dialogue systems. As an evaluation method, user simulation allows us to tackle issues such as scalability and cost…