2 citations · 6 across the 39 of their papers we have counts for
10 papers · 1 filter
CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense
Mingkun Zhang, Keping Bi, Wei Chen +3
Despite ongoing efforts to defend neural classifiers from adversarial attacks, they remain vulnerable, especially to unseen attacks. In contrast, humans are difficult to be cheated…
LINKAGE: Listwise Ranking among Varied-Quality References for Non-Factoid QA Evaluation via LLMs
Sihui Yang, Keping Bi, Wanqing Cui +2
Non-Factoid (NF) Question Answering (QA) is challenging to evaluate due to diverse potential answers and no objective criterion. The commonly used automatic evaluation metrics like…
Contextual Dual Learning Algorithm with Listwise Distillation for Unbiased Learning to Rank
Lulu Yu, Keping Bi, Shiyu Ni +1
Unbiased Learning to Rank (ULTR) aims to leverage biased implicit user feedback (e.g., click) to optimize an unbiased ranking model. The effectiveness of the existing ULTR methods…
Are Large Language Models More Honest in Their Probabilistic or Verbalized Confidence?
Shiyu Ni, Keping Bi, Lulu Yu +1
Large language models (LLMs) have been found to produce hallucinations when the question exceeds their internal knowledge boundaries. A reliable model should have a clear perceptio…
Evaluating Implicit Bias in Large Language Models by Attacking From a Psychometric Perspective
Yuchen Wen, Keping Bi, Wei Chen +2
As large language models (LLMs) become an important way of information access, there have been increasing concerns that LLMs may intensify the spread of unethical content, includin…
An Iterative Utility Judgment Framework Inspired by Philosophical Relevance via LLMs
Hengran Zhang, Keping Bi, Jiafeng Guo +1
Relevance and utility are two frequently used measures to evaluate the effectiveness of an information retrieval (IR) system. Relevance emphasizes the aboutness of a result to a qu…
MORE: Multi-mOdal REtrieval Augmented Generative Commonsense Reasoning
Wanqing Cui, Keping Bi, Jiafeng Guo +1
Since commonsense information has been recorded significantly less frequently than its existence, language models pre-trained by text generation have difficulty to learn sufficient…
When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentation
Shiyu Ni, Keping Bi, Jiafeng Guo +1
Large Language Models (LLMs) have been found to have difficulty knowing they do not possess certain knowledge and tend to provide specious answers in such cases. Retrieval Augmenta…
Reproducibility Analysis and Enhancements for Multi-Aspect Dense Retriever with Aspect Learning
Keping Bi, Xiaojie Sun, Jiafeng Guo +1
Multi-aspect dense retrieval aims to incorporate aspect information (e.g., brand and category) into dual encoders to facilitate relevance matching. As an early and representative m…
A Multi-Granularity-Aware Aspect Learning Model for Multi-Aspect Dense Retrieval
Xiaojie Sun, Keping Bi, Jiafeng Guo +5
Dense retrieval methods have been mostly focused on unstructured text and less attention has been drawn to structured data with various aspects, e.g., products with aspects such as…