Search Results for author:
Found 9 papers, 2 papers with code
TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities
The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework.
Truth Forest: Toward Multi-Scale Truthfulness in Large Language Models through Intervention without Tuning
We introduce Truth Forest, a method that enhances truthfulness in LLMs by uncovering hidden truth representations using multi-dimensional orthogonal probes.
Answer-focused and Position-aware Neural Question Generation
(2) The model copies the context words that are far from and irrelevant to the answer, instead of the words that are close and relevant to the answer.
Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval
Recently, the retrieval models based on dense representations have been gradually applied in the first stage of the document retrieval tasks, showing better performance than traditional sparse vector space models.
TITA: A Two-stage Interaction and Topic-Aware Text Matching Model
To tackle this problem, we define a three-level relevance in keyword-document matching task: topic-aware relevance, partially-relevance and irrelevance.
Enhancing Document Ranking with Task-adaptive Training and Segmented Token Recovery Mechanism
In this paper, we propose a new ranking model DR-BERT, which improves the Document Retrieval (DR) task by a task-adaptive training process and a Segmented Token Recovery Mechanism (STRM).
Inflected Forms Are Redundant in Question Generation Models
Secondly, we propose to adapt QG as a combination of the following actions in the encode-decoder framework: generating a question word, copying a word from the source sequence or generating a word transformation type.
PhD: A Prompted Visual Hallucination Evaluation Dataset
The rapid growth of Large Language Models (LLMs) has driven the development of Large Vision-Language Models (LVLMs).
The Elephant in the Room: Rethinking the Usage of Pre-trained Language Model in Sequential Recommendation
Sequential recommendation (SR) has seen significant advancements with the help of Pre-trained Language Models (PLMs).
