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Found 29 papers, 19 papers with code
A Joint Multiple Criteria Model in Transfer Learning for Cross-domain Chinese Word Segmentation
Chinese word segmentation (CWS) is an essential task for Chinese downstream NLP tasks.
OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence
In this paper, we explore the potential of multimodal LLMs (MLLM) for geospatial artificial intelligence (GeoAI), a field that leverages spatial data to address challenges in domains including Geospatial Semantics, Health Geography, Urban Geography, Urban Perception, and Remote Sensing.
Entropy-based Exploration Conduction for Multi-step Reasoning
In large language model (LLM) reasoning, multi-step processes have proven effective for solving complex tasks.
A Survey of Model Architectures in Information Retrieval
This survey examines the evolution of model architectures in information retrieval (IR), focusing on two key aspects: backbone models for feature extraction and end-to-end system architectures for relevance estimation.
TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics
In this study, we demonstrate that existing methods, such as GraphMixer and DyGFormer, are inherently incapable of learning simple sequential dynamics, such as ``a user who has followed OpenAI and Anthropic is more likely to follow AI at Meta next.''
LEKA:LLM-Enhanced Knowledge Augmentation
Therefore, we design a knowledge augmentation method LEKA for knowledge transfer that actively searches for suitable knowledge sources that can enrich the target domain's knowledge.
Can Large Language Models Understand Preferences in Personalized Recommendation?
To address this, we introduce PerRecBench, disassociating the evaluation from these two factors and assessing recommendation techniques on capturing the personal preferences in a grouped ranking manner.
RALI@TREC iKAT 2024: Achieving Personalization via Retrieval Fusion in Conversational Search
The Recherche Appliquee en Linguistique Informatique (RALI) team participated in the 2024 TREC Interactive Knowledge Assistance (iKAT) Track.
Thought Space Explorer: Navigating and Expanding Thought Space for Large Language Model Reasoning
Recent advances in large language models (LLMs) have demonstrated their potential in handling complex reasoning tasks, which are usually achieved by constructing a thought chain to guide the model to solve the problem with multi-step thinking.
A Survey of Conversational Search
As a cornerstone of modern information access, search engines have become indispensable in everyday life.
Aligning Query Representation with Rewritten Query and Relevance Judgments in Conversational Search
In this paper, we leverage both rewritten queries and relevance judgments in the conversational search data to train a better query representation model.
How to Leverage Personal Textual Knowledge for Personalized Conversational Information Retrieval
Personalized conversational information retrieval (CIR) combines conversational and personalizable elements to satisfy various users' complex information needs through multi-turn interaction based on their backgrounds.
Boosting Biomedical Concept Extraction by Rule-Based Data Augmentation
Document-level biomedical concept extraction is the task of identifying biomedical concepts mentioned in a given document.
Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering
By examining the learning dynamics and equilibrium of the contrastive loss, we offer a fresh lens to understand contrastive learning via graph theory, emphasizing its capability to capture high-order connectivity.
Retrieval-Augmented Feature Generation for Domain-Specific Classification
Feature generation can significantly enhance learning outcomes, particularly for tasks with limited data.
CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search
In this paper, we study how open-source large language models (LLMs) can be effectively deployed for improving query rewriting in conversational search, especially for ambiguous queries.
DoRA: Enhancing Parameter-Efficient Fine-Tuning with Dynamic Rank Distribution
Fine-tuning large-scale pre-trained models is inherently a resource-intensive task.
Mixture of Latent Experts Using Tensor Products
For \textit{routing function}, we tailor two innovative routing functions according to the granularity: \texttt{TensorPoly-I} which directs to each rank within the entangled tensor while \texttt{TensorPoly-II} offers a finer-grained routing approach targeting each order of the entangled tensor.
A Survey on Large Language Models with Multilingualism: Recent Advances and New Frontiers
The survey aims to help the research community address multilingual problems and provide a comprehensive understanding of the core concepts, key techniques, and latest developments in multilingual natural language processing based on LLMs.
Language Modeling Using Tensor Trains
We propose a novel tensor network language model based on the simplest tensor network (i. e., tensor trains), called `Tensor Train Language Model' (TTLM).
A User-Centric Multi-Intent Benchmark for Evaluating Large Language Models
Based on these ability scores, it is hard for users to determine which LLM best suits their particular needs.
ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval
Conversational search requires accurate interpretation of user intent from complex multi-turn contexts.
ConvSDG: Session Data Generation for Conversational Search
Conversational search provides a more convenient interface for users to search by allowing multi-turn interaction with the search engine.
History-Aware Conversational Dense Retrieval
To address the aforementioned issues, we propose a History-Aware Conversational Dense Retrieval (HAConvDR) system, which incorporates two ideas: context-denoised query reformulation and automatic mining of supervision signals based on the actual impact of historical turns.
Collaboration and Transition: Distilling Item Transitions into Multi-Query Self-Attention for Sequential Recommendation
Second, we develop a transition-aware embedding distillation module that distills global item-to-item transition patterns into item embeddings, which enables the model to memorize and leverage transitional signals and serves as a calibrator for collaborative signals.
MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model
To enable LLMs to tackle the task in a zero-shot manner, we introduce MoqaGPT, a straightforward and flexible framework.
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.
Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Search
Precisely understanding users' contextual search intent has been an important challenge for conversational search.
