Search Results for author:
Found 16 papers, 7 papers with code
Retrieval-Augmented Process Reward Model for Generalizable Mathematical Reasoning
By utilizing a two-stage retrieval-enhanced mechanism, RetrievalPRM retrieves semantically similar questions and steps as a warmup, enhancing PRM's ability to evaluate target steps and improving generalization and reasoning consistency across different models and problem types.
Boost, Disentangle, and Customize: A Robust System2-to-System1 Pipeline for Code Generation
This work lays the groundwork for advancing LLM capabilities in complex reasoning tasks, offering a novel System2-to-System1 solution.
Agentic Information Retrieval
Information state refers to a particular information context that the user is right in within a dynamic environment, encompassing not only the acquired information items but also real-time user preferences, contextual factors, and decision-making processes.
RethinkMCTS: Refining Erroneous Thoughts in Monte Carlo Tree Search for Code Generation
More importantly, we construct verbal feedback from fine-grained code execution feedback to refine erroneous thoughts during the search.
SINKT: A Structure-Aware Inductive Knowledge Tracing Model with Large Language Model
Knowledge Tracing (KT) aims to determine whether students will respond correctly to the next question, which is a crucial task in intelligent tutoring systems (ITS).
ELCoRec: Enhance Language Understanding with Co-Propagation of Numerical and Categorical Features for Recommendation
Concretely, we propose to inject the preference understanding capability into LLM via a GAT expert model where the user preference is better encoded by parallelly propagating the temporal relations, and rating signals as well as various side information of historical items.
Learning Structure and Knowledge Aware Representation with Large Language Models for Concept Recommendation
However, integrating LLMs into concept recommendation presents two urgent challenges: 1) How to construct text for concepts that effectively incorporate the human knowledge system?
FINED: Feed Instance-Wise Information Need with Essential and Disentangled Parametric Knowledge from the Past
In this paper, we propose FINED to Feed INstance-wise information need with Essential and Disentangled parametric knowledge from past data for recommendation enhancement.
DisCo: Towards Harmonious Disentanglement and Collaboration between Tabular and Semantic Space for Recommendation
In this paper, we propose DisCo to Disentangle the unique patterns from the two representation spaces and Collaborate the two spaces for recommendation enhancement, where both the specificity and the consistency of the two spaces are captured.
CodeGRAG: Bridging the Gap between Natural Language and Programming Language via Graphical Retrieval Augmented Generation
Despite the intelligence shown by the general large language models, their specificity in code generation can still be improved due to the syntactic gap and mismatched vocabulary existing among natural language and different programming languages.
CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models
With the emergence of Large Language Models (LLMs), there has been a significant improvement in the programming capabilities of models, attracting growing attention from researchers.
ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation
With large language models (LLMs) achieving remarkable breakthroughs in natural language processing (NLP) domains, LLM-enhanced recommender systems have received much attention and have been actively explored currently.
Learning Enhanced Representations for Tabular Data via Neighborhood Propagation
Prediction over tabular data is an essential and fundamental problem in many important downstream tasks.
Inductive Relation Prediction Using Analogy Subgraph Embeddings
Prevailing methods for relation prediction in heterogeneous graphs aim at learning latent representations (i. e., embeddings) of observed nodes and relations, and thus are limited to the transductive setting where the relation types must be known during training.
GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network
Heterogeneous information network (HIN) has been widely used to characterize entities of various types and their complex relations.
An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph
To the best of our knowledge, this is the first work providing an efficient neighborhood-based interaction model in the HIN-based recommendations.
