7 citations · 29 across the 54 of their papers we have counts for
13 papers · 1 filter
Plot Retrieval as an Assessment of Abstract Semantic Association
Shicheng Xu, Liang Pang, Jiangnan Li +5
Retrieving relevant plots from the book for a query is a critical task, which can improve the reading experience and efficiency of readers. Readers usually only give an abstract an…
Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images
Shicheng Xu, Danyang Hou, Liang Pang +4
With the advancement of generation models, AI-generated content (AIGC) is becoming more realistic, flooding the Internet. A recent study suggests that this phenomenon causes source…
Think Before You Speak: Cultivating Communication Skills of Large Language Models via Inner Monologue
Junkai Zhou, Liang Pang, Huawei Shen +1
The emergence of large language models (LLMs) further improves the capabilities of open-domain dialogue systems and can generate fluent, coherent, and diverse responses. However, L…
RegaVAE: A Retrieval-Augmented Gaussian Mixture Variational Auto-Encoder for Language Modeling
Jingcheng Deng, Liang Pang, Huawei Shen +1
Retrieval-augmented language models show promise in addressing issues like outdated information and hallucinations in language models (LMs). However, current research faces two mai…
SimOAP: Improve Coherence and Consistency in Persona-based Dialogue Generation via Over-sampling and Post-evaluation
Junkai Zhou, Liang Pang, Huawei Shen +1
Language models trained on large-scale corpora can generate remarkably fluent results in open-domain dialogue. However, for the persona-based dialogue generation task, consistency…
BERM: Training the Balanced and Extractable Representation for Matching to Improve Generalization Ability of Dense Retrieval
Shicheng Xu, Liang Pang, Huawei Shen +1
Dense retrieval has shown promise in the first-stage retrieval process when trained on in-domain labeled datasets. However, previous studies have found that dense retrieval is hard…
Visual Reasoning: from State to Transformation
Xin Hong, Yanyan Lan, Liang Pang +2
Most existing visual reasoning tasks, such as CLEVR in VQA, ignore an important factor, i.e.~transformation. They are solely defined to test how well machines understand concepts a…
LLMDet: A Third Party Large Language Models Generated Text Detection Tool
Kangxi Wu, Liang Pang, Huawei Shen +2
Generated texts from large language models (LLMs) are remarkably close to high-quality human-authored text, raising concerns about their potential misuse in spreading false informa…
MacLaSa: Multi-Aspect Controllable Text Generation via Efficient Sampling from Compact Latent Space
Hanxing Ding, Liang Pang, Zihao Wei +3
Multi-aspect controllable text generation aims to generate fluent sentences that possess multiple desired attributes simultaneously. Traditional methods either combine many operato…
Visual Transformation Telling
Wanqing Cui, Xin Hong, Yanyan Lan +3
Humans can naturally reason from superficial state differences (e.g. ground wetness) to transformations descriptions (e.g. raining) according to their life experience. In this pape…
Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks
Shicheng Xu, Liang Pang, Huawei Shen +2
Making the content generated by Large Language Model (LLM), accurate, credible and traceable is crucial, especially in complex knowledge-intensive tasks that require multi-step rea…
Multi-video Moment Ranking with Multimodal Clue
Danyang Hou, Liang Pang, Yanyan Lan +2
Video corpus moment retrieval~(VCMR) is the task of retrieving a relevant video moment from a large corpus of untrimmed videos via a natural language query. State-of-the-art work f…
Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language Understanding
Yunchang Zhu, Liang Pang, Kangxi Wu +3
Current natural language understanding (NLU) models have been continuously scaling up, both in terms of model size and input context, introducing more hidden and input neurons. Whi…