1 citations · 1 across the 6 of their papers we have counts for
6 papers
PEAR: Position-Embedding-Agnostic Attention Re-weighting Enhances Retrieval-Augmented Generation with Zero Inference Overhead
Tao Tan, Yining Qian, Ang Lv +7
Large language models (LLMs) enhanced with retrieval-augmented generation (RAG) have introduced a new paradigm for web search. However, the limited context awareness of LLMs degrad…
AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code Generation
Ziyang Luo, Xin Li, Hongzhan Lin +2
The impressive performance of proprietary LLMs like GPT4 in code generation has led to a trend to replicate these capabilities in open-source models through knowledge distillation…
CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding?
Yuwei Zhao, Ziyang Luo, Yuchen Tian +4
Recent advancements in large language models (LLMs) have showcased impressive code generation capabilities, primarily evaluated through language-to-code benchmarks. However, these…
Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Models
Hongzhan Lin, Ziyang Luo, Wei Gao +3
The age of social media is flooded with Internet memes, necessitating a clear grasp and effective identification of harmful ones. This task presents a significant challenge due to…
WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom
Ruichao Yang, Wei Gao, Jing Ma +2
In recent years, we witness the explosion of false and unconfirmed information (i.e., rumors) that went viral on social media and shocked the public. Rumors can trigger versatile,…
Dual-Scale Interest Extraction Framework with Self-Supervision for Sequential Recommendation
Liangliang Chen, Hongzhan Lin, Jinshan Ma +1
In the sequential recommendation task, the recommender generally learns multiple embeddings from a user's historical behaviors, to catch the diverse interests of the user. Neverthe…