most citedAutoHint: Automatic Prompt Optimization with Hint Generation

4 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL20234 cited

AutoHint: Automatic Prompt Optimization with Hint Generation

Hong Sun, Xue Li, Yinchuan Xu +5

This paper presents AutoHint, a novel framework for automatic prompt engineering and optimization for Large Language Models (LLM). While LLMs have demonstrated remarkable ability i…

cs.CL20231 cited

Dual-Alignment Pre-training for Cross-lingual Sentence Embedding

Ziheng Li, Shaohan Huang, Zihan Zhang +7

Recent studies have shown that dual encoder models trained with the sentence-level translation ranking task are effective methods for cross-lingual sentence embedding. However, our…

cs.CL20232 cited

Pre-training Transformers for Knowledge Graph Completion

Sanxing Chen, Hao Cheng, Xiaodong Liu +3

Learning transferable representation of knowledge graphs (KGs) is challenging due to the heterogeneous, multi-relational nature of graph structures. Inspired by Transformer-based p…

cs.LG2022

The Counterfactual-Shapley Value: Attributing Change in System Metrics

Amit Sharma, Hua Li, Jian Jiao

Given an unexpected change in the output metric of a large-scale system, it is important to answer why the change occurred: which inputs caused the change in metric? A key componen…

cs.LG20221 cited

NGAME: Negative Mining-aware Mini-batching for Extreme Classification

Kunal Dahiya, Nilesh Gupta, Deepak Saini +16

Extreme Classification (XC) seeks to tag data points with the most relevant subset of labels from an extremely large label set. Performing deep XC with dense, learnt representation…