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Found 34 papers, 22 papers with code
基于双星型自注意力网络的搜索结果多样化方法(Search Result Diversification Framework Based on Dual Star-shaped Self-Attention Network)
“相关研究指出, 用户提交给搜索引擎的查询通常为短查询。由于自然语言本身的特点, 短查询通常具有歧义性, 同一个查询可以指代不同的事物, 或同一事物的不同方面。为了让搜索结果尽可能满足用户多样化的信息需求, 搜索引擎需要对返回的结果进行多样化排序, 搜索结果多样化技术应运而生。目前已有的基于全局交互的多样化方法通过全连接的自注意力网络捕获全体候选文档间的交互关系, 取得了较好的效果。但由于此类方法只考虑文档间的相关关系, 并没有考虑到文档是否具有跟查询相关的有效信息, 在训练数据有限的条件下效率相对较低。该文提出了一种基于双星型自注意力网络的搜索结果多样化方法, 将全连接结构改为星型拓扑结构, 并嵌入查询信息以高效率地提取文档跟查询相关的全局交互特征。相关实验结果显示, 该模型相对于基于全连接自注意力网络的多样化方法, 具备显著的性能优势。”
From Matching to Generation: A Survey on Generative Information Retrieval
We will summarize the advancements in GR regarding model training, document identifier, incremental learning, downstream tasks adaptation, multi-modal GR and generative recommendation, as well as progress in reliable response generation in aspects of internal knowledge memorization, external knowledge augmentation, generating response with citations and personal information assistant.
An Integrated Data Processing Framework for Pretraining Foundation Models
The ability of the foundation models heavily relies on large-scale, diverse, and high-quality pretraining data.
UFO: a Unified and Flexible Framework for Evaluating Factuality of Large Language Models
To address these challenges, we categorize four available fact sources: human-written evidence, reference documents, search engine results, and LLM knowledge, along with five text generation tasks containing six representative datasets.
Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs
The integration of large language models (LLMs) and search engines represents a significant evolution in knowledge acquisition methodologies.
BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidence
Retrieval-augmented large language models (LLMs) have demonstrated efficacy in knowledge-intensive tasks such as open-domain QA, addressing inherent challenges in knowledge update and factual inadequacy.
INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning
Despite this, their application to information retrieval (IR) tasks is still challenging due to the infrequent occurrence of many IR-specific concepts in natural language.
Don't Make Your LLM an Evaluation Benchmark Cheater
Large language models~(LLMs) have greatly advanced the frontiers of artificial intelligence, attaining remarkable improvement in model capacity.
Large Language Models for Information Retrieval: A Survey
This evolution requires a combination of both traditional methods (such as term-based sparse retrieval methods with rapid response) and modern neural architectures (such as language models with powerful language understanding capacity).
Learning to Relate to Previous Turns in Conversational Search
An effective way to improve retrieval effectiveness is to expand the current query with historical queries.
ConvGQR: Generative Query Reformulation for Conversational Search
In this paper, we propose ConvGQR, a new framework to reformulate conversational queries based on generative pre-trained language models (PLMs), one for query rewriting and another for generating potential answers.
WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpus
In this paper, we introduce a new NLP task -- generating short factual articles with references for queries by mining supporting evidence from the Web.
A Text-guided Protein Design Framework
Current AI-assisted protein design mainly utilizes protein sequential and structural information.
An Empirical Study of Uniform-Architecture Knowledge Distillation in Document Ranking
Specifically, when the student models are in cross-encoder architecture, a pairwise loss of hard labels is critical for training student models, whereas the distillation objectives of intermediate Transformer layers may hurt performance.
MCP: Self-supervised Pre-training for Personalized Chatbots with Multi-level Contrastive Sampling
To tackle these problems, we propose a self-supervised learning framework MCP for capturing better representations from users' dialogue history for personalized chatbots.
Pre-training for Information Retrieval: Are Hyperlinks Fully Explored?
Hyperlinks, which are commonly used in Web pages, have been leveraged for designing pre-training objectives.
Enhancing User Behavior Sequence Modeling by Generative Tasks for Session Search
To help the encoding of the current user behavior sequence, we propose to use a decoder and the information of future sequences and a supplemental query.
From Easy to Hard: A Dual Curriculum Learning Framework for Context-Aware Document Ranking
In this work, we propose a curriculum learning framework for context-aware document ranking, in which the ranking model learns matching signals between the search context and the candidate document in an easy-to-hard manner.
Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding
Our experiments on 13 benchmark datasets across five natural language understanding tasks demonstrate the superiority of our method.
PReGAN: Answer Oriented Passage Ranking with Weakly Supervised GAN
Beyond topical relevance, passage ranking for open-domain factoid question answering also requires a passage to contain an answer (answerability).
Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation
Existing personalized dialogue systems have tried to extract user profiles from dialogue history to guide personalized response generation.
PSSL: Self-supervised Learning for Personalized Search with Contrastive Sampling
Personalized search plays a crucial role in improving user search experience owing to its ability to build user profiles based on historical behaviors.
Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking
To learn a more robust representation of the user behavior sequence, we propose a method based on contrastive learning, which takes into account the possible variations in user's behavior sequences.
One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profiles
Specifically, leveraging the benefits of Transformer on language understanding, we train a personalized language model to construct a general user profile from the user's historical responses.
Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot
To learn a user's personalized language style, we elaborately build language models from shallow to deep using the user's historical responses; To model a user's personalized preferences, we explore the conditional relations underneath each post-response pair of the user.
Proactive Retrieval-based Chatbots based on Relevant Knowledge and Goals
The final response is selected according to the predicted knowledge, the goal to achieve, and the context.
Emotion Eliciting Machine: Emotion Eliciting Conversation Generation based on Dual Generator
Then we propose a dual encoder-decoder structure to model the generation of responses in both positive and negative side based on the changes of the user's emotion status in the conversation.
BERT4SO: Neural Sentence Ordering by Fine-tuning BERT
Sentence ordering aims to arrange the sentences of a given text in the correct order.
Neural Sentence Ordering Based on Constraint Graphs
Our experiments on five benchmark datasets show that our method outperforms all the existing baselines significantly, achieving a new state-of-the-art performance.
Content Selection Network for Document-grounded Retrieval-based Chatbots
It is thus crucial to select the part of document content relevant to the current conversation context.
Pchatbot: A Large-Scale Dataset for Personalized Chatbot
This enables the development of personalized dialogue models that directly learn implicit user personality from the user's dialogue history.
S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
To tackle this problem, we propose the model S^3-Rec, which stands for Self-Supervised learning for Sequential Recommendation, based on the self-attentive neural architecture.
ScriptWriter: Narrative-Guided Script Generation
In dialogue systems, it would also be useful to drive dialogues by a dialogue plan.
Improving Multi-Turn Response Selection Models with Complementary Last-Utterance Selection by Instance Weighting
Open-domain retrieval-based dialogue systems require a considerable amount of training data to learn their parameters.
