24 citations · 51 across the 27 of their papers we have counts for
8 papers · 1 filter
Pooling And Attention: What Are Effective Designs For LLM-Based Embedding Models?
Yixuan Tang, Yi Yang
The significant advancements of Large Language Models (LLMs) in generative tasks have led to a growing body of work exploring LLM-based embedding models. While these models, employ…
Do We Need Domain-Specific Embedding Models? An Empirical Investigation
Yixuan Tang, Yi Yang
Embedding models play a crucial role in representing and retrieving information across various NLP applications. Recent advancements in Large Language Models (LLMs) have further en…
Know the Unknown: An Uncertainty-Sensitive Method for LLM Instruction Tuning
Jiaqi Li, Yixuan Tang, Yi Yang
Large language models (LLMs) demonstrate remarkable capabilities but face challenges from hallucinations, which typically arise from insufficient knowledge or context. While instru…
Revealing the Two Sides of Data Augmentation: An Asymmetric Distillation-based Win-Win Solution for Open-Set Recognition
Yunbing Jia, Xiaoyu Kong, Fan Tang +3
In this paper, we reveal the two sides of data augmentation: enhancements in closed-set recognition correlate with a significant decrease in open-set recognition. Through empirical…
CQIL: Inference Latency Optimization with Concurrent Computation of Quasi-Independent Layers
Longwei Zou, Qingyang Wang, Han Zhao +3
The fast-growing large scale language models are delivering unprecedented performance on almost all natural language processing tasks. However, the effectiveness of large language…
Beyond Surface Similarity: Detecting Subtle Semantic Shifts in Financial Narratives
Jiaxin Liu, Yi Yang, Kar Yan Tam
In this paper, we introduce the Financial-STS task, a financial domain-specific NLP task designed to measure the nuanced semantic similarity between pairs of financial narratives.…
Do LLMs Know about Hallucination? An Empirical Investigation of LLM's Hidden States
Hanyu Duan, Yi Yang, Kar Yan Tam
Large Language Models (LLMs) can make up answers that are not real, and this is known as hallucination. This research aims to see if, how, and to what extent LLMs are aware of hall…
MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries
Yixuan Tang, Yi Yang
Retrieval-augmented generation (RAG) augments large language models (LLM) by retrieving relevant knowledge, showing promising potential in mitigating LLM hallucinations and enhanci…