229 citations · 248 across the 9 of their papers we have counts for
10 papers
Mitigating Lost in Multi-turn Conversation via Curriculum RL with Verifiable Accuracy and Abstention Rewards
Ming Li, Pei Chen, Zhenhao Zhang +10
Large Language Models demonstrate strong capabilities in single-turn instruction following but suffer from Lost-in-Conversation (LiC), a degradation in performance as information i…
Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templates
Hy Dang, Tianyi Liu, Zhuofeng Wu +9
Large language models (LLMs) have demonstrated strong reasoning and tool-use capabilities, yet they often fail in real-world tool-interactions due to incorrect parameterization, po…
UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations
Fengran Mo, Yifan Gao, Chuan Meng +9
The rapid advancement of conversational search systems revolutionizes how information is accessed by enabling the multi-turn interaction between the user and the system. Existing c…
Divide-or-Conquer? Which Part Should You Distill Your LLM?
Zhuofeng Wu, He Bai, Aonan Zhang +4
Recent methods have demonstrated that Large Language Models (LLMs) can solve reasoning tasks better when they are encouraged to solve subtasks of the main task first. In this paper…
HiCL: Hierarchical Contrastive Learning of Unsupervised Sentence Embeddings
Zhuofeng Wu, Chaowei Xiao, VG Vinod Vydiswaran
In this paper, we propose a hierarchical contrastive learning framework, HiCL, which considers local segment-level and global sequence-level relationships to improve training effic…
PLANNER: Generating Diversified Paragraph via Latent Language Diffusion Model
Yizhe Zhang, Jiatao Gu, Zhuofeng Wu +3
Autoregressive models for text sometimes generate repetitive and low-quality output because errors accumulate during the steps of generation. This issue is often attributed to expo…
Defending against Insertion-based Textual Backdoor Attacks via Attribution
Jiazhao Li, Zhuofeng Wu, Wei Ping +2
Textual backdoor attack, as a novel attack model, has been shown to be effective in adding a backdoor to the model during training. Defending against such backdoor attacks has beco…
ChatGPT as an Attack Tool: Stealthy Textual Backdoor Attack via Blackbox Generative Model Trigger
Jiazhao Li, Yijin Yang, Zhuofeng Wu +2
Textual backdoor attacks pose a practical threat to existing systems, as they can compromise the model by inserting imperceptible triggers into inputs and manipulating labels in th…
IDPG: An Instance-Dependent Prompt Generation Method
Zhuofeng Wu, Sinong Wang, Jiatao Gu +4
Prompt tuning is a new, efficient NLP transfer learning paradigm that adds a task-specific prompt in each input instance during the model training stage. It freezes the pre-trained…
CLEAR: Contrastive Learning for Sentence Representation
Zhuofeng Wu, Sinong Wang, Jiatao Gu +3
Pre-trained language models have proven their unique powers in capturing implicit language features. However, most pre-training approaches focus on the word-level training objectiv…