58 citations · 70 across the 13 of their papers we have counts for
13 papers
IBCircuit: Towards Holistic Circuit Discovery with Information Bottleneck
Tian Bian, Yifan Niu, Chaohao Yuan +7
Circuit discovery has recently attracted attention as a potential research direction to explain the non-trivial behaviors of language models. It aims to find the computational subg…
Fine-grained Semantics Integration for Large Language Model-based Recommendation
Jiawei Feng, Xiaoyu Kong, Leheng Sheng +8
Recent advances in Large Language Models (LLMs) have driven a shift in recommender systems from the discriminative paradigm to the LLM-based generative paradigm, where the recommen…
NTPP: Generative Speech Language Modeling for Dual-Channel Spoken Dialogue via Next-Token-Pair Prediction
Qichao Wang, Ziqiao Meng, Wenqian Cui +6
Inspired by the impressive capabilities of GPT-4o, there is growing interest in enabling speech language models (SLMs) to engage in natural, fluid spoken interactions with humans.…
Measuring Diversity in Synthetic Datasets
Yuchang Zhu, Huizhe Zhang, Bingzhe Wu +5
Large language models (LLMs) are widely adopted to generate synthetic datasets for various natural language processing (NLP) tasks, such as text classification and summarization. H…
Probing the Safety Response Boundary of Large Language Models via Unsafe Decoding Path Generation
Haoyu Wang, Bingzhe Wu, Yatao Bian +3
Large Language Models (LLMs) are implicit troublemakers. While they provide valuable insights and assist in problem-solving, they can also potentially serve as a resource for malic…
Parameter-Efficient Fine-Tuning with Discrete Fourier Transform
Ziqi Gao, Qichao Wang, Aochuan Chen +4
Low-rank adaptation~(LoRA) has recently gained much interest in fine-tuning foundation models. It effectively reduces the number of trainable parameters by incorporating low-rank m…
Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping
Haoyu Wang, Guozheng Ma, Ziqiao Meng +9
Self-alignment is an effective way to reduce the cost of human annotation while ensuring promising model capability. However, most current methods complete the data collection and…
Rethinking and Simplifying Bootstrapped Graph Latents
Wangbin Sun, Jintang Li, Liang Chen +3
Graph contrastive learning (GCL) has emerged as a representative paradigm in graph self-supervised learning, where negative samples are commonly regarded as the key to preventing m…
PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection
Tao Yang, Tianyuan Shi, Fanqi Wan +4
Recent advances in large language models (LLMs), such as ChatGPT, have showcased remarkable zero-shot performance across various NLP tasks. However, the potential of LLMs in person…
Language Agents for Detecting Implicit Stereotypes in Text-to-image Models at Scale
Qichao Wang, Tian Bian, Yian Yin +6
The recent surge in the research of diffusion models has accelerated the adoption of text-to-image models in various Artificial Intelligence Generated Content (AIGC) commercial pro…
Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators
Liang Chen, Yang Deng, Yatao Bian +4
Large language models (LLMs) outperform information retrieval techniques for downstream knowledge-intensive tasks when being prompted to generate world knowledge. However, communit…
Is GPT4 a Good Trader?
Bingzhe Wu
Recently, large language models (LLMs), particularly GPT-4, have demonstrated significant capabilities in various planning and reasoning tasks \cite{cheng2023gpt4,bubeck2023sparks}…
A Survey of Adversarial Learning on Graphs
Liang Chen, Jintang Li, Jiaying Peng +6
Deep learning models on graphs have achieved remarkable performance in various graph analysis tasks, e.g., node classification, link prediction, and graph clustering. However, they…