262 citations · 1.1k across the 41 of their papers we have counts for
13 papers · 1 filter
Too Consistent to Detect: A Study of Self-Consistent Errors in LLMs
Hexiang Tan, Fei Sun, Sha Liu +8
As large language models (LLMs) often generate plausible but incorrect content, error detection has become increasingly critical to ensure truthfulness. However, existing detection…
The Mirage of Model Editing: Revisiting Evaluation in the Wild
Wanli Yang, Fei Sun, Jiajun Tan +5
Despite near-perfect results reported in the literature, the effectiveness of model editing in real-world applications remains unclear. To bridge this gap, we introduce QAEdit, a n…
Fact-Level Confidence Calibration and Self-Correction
Yige Yuan, Bingbing Xu, Hexiang Tan +5
Confidence calibration in LLMs, i.e., aligning their self-assessed confidence with the actual accuracy of their responses, enabling them to self-evaluate the correctness of their o…
Understanding the Collapse of LLMs in Model Editing
Wanli Yang, Fei Sun, Jiajun Tan +4
Despite significant progress in model editing methods, their application in real-world scenarios remains challenging as they often cause large language models (LLMs) to collapse. A…
When to Trust LLMs: Aligning Confidence with Response Quality
Shuchang Tao, Liuyi Yao, Hanxing Ding +6
Despite the success of large language models (LLMs) in natural language generation, much evidence shows that LLMs may produce incorrect or nonsensical text. This limitation highlig…
Blinded by Generated Contexts: How Language Models Merge Generated and Retrieved Contexts When Knowledge Conflicts?
Hexiang Tan, Fei Sun, Wanli Yang +3
While auxiliary information has become a key to enhancing Large Language Models (LLMs), relatively little is known about how LLMs merge these contexts, specifically contexts genera…
Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts
Ganesh Jawahar, Haichuan Yang, Yunyang Xiong +10
Weight-sharing supernets are crucial for performance estimation in cutting-edge neural architecture search (NAS) frameworks. Despite their ability to generate diverse subnetworks w…
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…
Ranking Enhanced Dialogue Generation
Changying Hao, Liang Pang, Yanyan Lan +3
How to effectively utilize the dialogue history is a crucial problem in multi-turn dialogue generation. Previous works usually employ various neural network architectures (e.g., re…
Improving Multi-turn Dialogue Modelling with Utterance ReWriter
Hui Su, Xiaoyu Shen, Rongzhi Zhang +4
Recent research has made impressive progress in single-turn dialogue modelling. In the multi-turn setting, however, current models are still far from satisfactory. One major challe…
Multi-Source Pointer Network for Product Title Summarization
Fei Sun, Peng Jiang, Hanxiao Sun +3
In this paper, we study the product title summarization problem in E-commerce applications for display on mobile devices. Comparing with conventional sentence summarization, produc…
Semantic Regularities in Document Representations
Fei Sun, Jiafeng Guo, Yanyan Lan +2
Recent work exhibited that distributed word representations are good at capturing linguistic regularities in language. This allows vector-oriented reasoning based on simple linear…
Component-Enhanced Chinese Character Embeddings
Yanran Li, Wenjie Li, Fei Sun +1
Distributed word representations are very useful for capturing semantic information and have been successfully applied in a variety of NLP tasks, especially on English. In this wor…