Search Results for author:
Found 22 papers, 12 papers with code
Great~Truths~are ~Always ~Simple: A Rather Simple Knowledge Encoder for Enhancing the Commonsense Reasoning Capacity of Pre-Trained Models
Commonsense reasoning in natural language is a desired ability of artificial intelligent systems.
CAFE: Retrieval Head-based Coarse-to-Fine Information Seeking to Enhance Multi-Document QA Capability
Advancements in Large Language Models (LLMs) have extended their input context length, yet they still struggle with retrieval and reasoning in long-context inputs.
Embodied Intelligence: The Key to Unblocking Generalized Artificial Intelligence
The ultimate goal of artificial intelligence (AI) is to achieve Artificial General Intelligence (AGI).
R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning
To address this, we propose \textbf{R1-Searcher}, a novel two-stage outcome-based RL approach designed to enhance the search capabilities of LLMs.
An Empirical Study on Eliciting and Improving R1-like Reasoning Models
This approach achieves a remarkable accuracy of 86. 67% with greedy search on AIME 2024, underscoring its effectiveness in enhancing model capabilities.
LongReD: Mitigating Short-Text Degradation of Long-Context Large Language Models via Restoration Distillation
To address these challenges, we propose Long Context Pre-training with Restoration Distillation (LongReD), a novel approach designed to mitigate short-text performance degradation through minimizing the distribution discrepancy between the extended and original models.
Holistically Guided Monte Carlo Tree Search for Intricate Information Seeking
We reformulate the task as a progressive information collection process with a knowledge memory and unite an adaptive checklist with multi-perspective reward modeling in MCTS.
YuLan-Mini: An Open Data-efficient Language Model
Effective pre-training of large language models (LLMs) has been challenging due to the immense resource demands and the complexity of the technical processes involved.
RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement
Existing large language models (LLMs) show exceptional problem-solving capabilities but might struggle with complex reasoning tasks.
Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems
We introduce an ``imitate, explore, and self-improve'' framework, denoted as \textbf{STILL-2}, as our primary technical approach to train the reasoning model.
Enhancing LLM Reasoning with Reward-guided Tree Search
This framework is implemented by integrating the policy model, reward model, and search algorithm.
Towards Effective and Efficient Continual Pre-training of Large Language Models
To make the CPT approach more traceable, this paper presents a technical report for continually pre-training Llama-3 (8B), which significantly enhances the Chinese language ability and scientific reasoning ability of the backbone model.
Mix-CPT: A Domain Adaptation Framework via Decoupling Knowledge Learning and Format Alignment
However, this method may result in inefficient knowledge memorization due to a lack of awareness of knowledge utilization and imposes substantial demands on LLMs to simultaneously learn knowledge utilization and format alignment with limited training samples.
KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph
To guarantee the effectiveness, we leverage program language to formulate the multi-hop reasoning process over the KG, and synthesize a code-based instruction dataset to fine-tune the base LLM.
ReasoningLM: Enabling Structural Subgraph Reasoning in Pre-trained Language Models for Question Answering over Knowledge Graph
To better perform reasoning on KG, recent work typically adopts a pre-trained language model~(PLM) to model the question, and a graph neural network~(GNN) based module to perform multi-hop reasoning on the KG.
StructGPT: A General Framework for Large Language Model to Reason over Structured Data
Specially, we propose an \emph{invoking-linearization-generation} procedure to support LLMs in reasoning on the structured data with the help of the external interfaces.
A Survey of Large Language Models
To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph
Multi-hop Question Answering over Knowledge Graph~(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question on a large-scale Knowledge Graph (KG).
Great Truths are Always Simple: A Rather Simple Knowledge Encoder for Enhancing the Commonsense Reasoning Capacity of Pre-Trained Models
Commonsense reasoning in natural language is a desired ability of artificial intelligent systems.
Complex Knowledge Base Question Answering: A Survey
Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB).
A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions
In this paper, we elaborately summarize the typical challenges and solutions for complex KBQA.
TextBox: A Unified, Modularized, and Extensible Framework for Text Generation
In this paper, we release an open-source library, called TextBox, to provide a unified, modularized, and extensible text generation framework.
