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Found 47 papers, 32 papers with code
BookWorld: From Novels to Interactive Agent Societies for Creative Story Generation
Recent advances in large language models (LLMs) have enabled social simulation through multi-agent systems.
The Lighthouse of Language: Enhancing LLM Agents via Critique-Guided Improvement
In this work, we introduce Critique-Guided Improvement (CGI), a novel two-player framework, comprising an actor model that explores an environment and a critic model that generates detailed nature language feedback.
Implicit Reasoning in Transformers is Reasoning through Shortcuts
Test-time compute is emerging as a new paradigm for enhancing language models' complex multi-step reasoning capabilities, as demonstrated by the success of OpenAI's o1 and o3, as well as DeepSeek's R1.
FlowAgent: Achieving Compliance and Flexibility for Workflow Agents
Experiments on three datasets demonstrate that FlowAgent not only adheres to workflows but also effectively manages OOW queries, highlighting its dual strengths in compliance and flexibility.
Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy
Many previous models of named entity recognition (NER) suffer from the problem of Out-of-Entity (OOE), i. e., the tokens in the entity mentions of the test samples have not appeared in the training samples, which hinders the achievement of satisfactory performance.
QUILL: Quotation Generation Enhancement of Large Language Models
To improve the LLMs' quotation generation abilities, we construct a bilingual knowledge base that is broad in scope and rich in dimensions, containing up to 32, 022 quotes.
Faster Local Solvers for Graph Diffusion Equations
Standard iterative methods require accessing the whole graph per iteration, making them time-consuming for large-scale graphs.
EDGE: Enhanced Grounded GUI Understanding with Enriched Multi-Granularity Synthetic Data
Given the lack of high-quality training data for GUI-related tasks in existing work, this paper aims to enhance the GUI understanding and interacting capabilities of LVLMs through a data-driven approach.
Iterative Methods via Locally Evolving Set Process
We propose to use the \textit{locally evolving set process}, a novel framework to characterize the algorithm locality, and demonstrate that many standard solvers can be effectively localized.
LoGU: Long-form Generation with Uncertainty Expressions
To tackle these challenges, we propose a refinement-based data collection framework and a two-stage training pipeline.
Past Meets Present: Creating Historical Analogy with Large Language Models
Historical analogies, which compare known past events with contemporary but unfamiliar events, are important abilities that help people make decisions and understand the world.
AgentRE: An Agent-Based Framework for Navigating Complex Information Landscapes in Relation Extraction
The relation extraction (RE) in complex scenarios faces challenges such as diverse relation types and ambiguous relations between entities within a single sentence, leading to the poor performance of pure "text-in, text-out" language models (LMs).
Capturing Minds, Not Just Words: Enhancing Role-Playing Language Models with Personality-Indicative Data
Role-playing agents (RPA) have been a popular application area for large language models (LLMs), attracting significant interest from both industry and academia. While existing RPAs well portray the characters' knowledge and tones, they face challenges in capturing their minds, especially for small role-playing language models (RPLMs).
EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms
In this paper, we introduce EvoAgent, a generic method to automatically extend expert agents to multi-agent systems via the evolutionary algorithm, thereby improving the effectiveness of LLM-based agents in solving tasks.
Adaptive Reinforcement Learning Planning: Harnessing Large Language Models for Complex Information Extraction
Existing research on large language models (LLMs) shows that they can solve information extraction tasks through multi-step planning.
Boosting Scientific Concepts Understanding: Can Analogy from Teacher Models Empower Student Models?
Analogical reasoning plays a critical role in human cognition, enabling us to understand new concepts by associating them with familiar ones.
SelfGoal: Your Language Agents Already Know How to Achieve High-level Goals
Language agents powered by large language models (LLMs) are increasingly valuable as decision-making tools in domains such as gaming and programming.
Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization
Our empirical results reveal that our ADT is highly effective on challenging the tokenization of leading LLMs, including GPT-4o, Llama-3, Deepseek-R1 and so on, thus degrading these LLMs' capabilities.
SED: Self-Evaluation Decoding Enhances Large Language Models for Better Generation
Existing Large Language Models (LLMs) generate text through unidirectional autoregressive decoding methods to respond to various user queries.
P-ICL: Point In-Context Learning for Named Entity Recognition with Large Language Models
To obtain optimal point entities for prompting LLMs, we also proposed a point entity selection method based on K-Means clustering.
"A good pun is its own reword": Can Large Language Models Understand Puns?
Puns play a vital role in academic research due to their distinct structure and clear definition, which aid in the comprehensive analysis of linguistic humor.
Evaluating Character Understanding of Large Language Models via Character Profiling from Fictional Works
The prerequisite for these RPAs lies in the capability of LLMs to understand characters from fictional works.
Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction
The framework includes an evaluation model that can extract related entity pairs with high precision.
Negation Triplet Extraction with Syntactic Dependency and Semantic Consistency
To achieve NTE, we devise a novel Syntax&Semantic-Enhanced Negation Extraction model, namely SSENE, which is built based on a generative pretrained language model (PLM) {of Encoder-Decoder architecture} with a multi-task learning framework.
ToNER: Type-oriented Named Entity Recognition with Generative Language Model
In recent years, the fine-tuned generative models have been proven more powerful than the previous tagging-based or span-based models on named entity recognition (NER) task.
Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understanding
Large Language Models (LLMs) have demonstrated good performance in many reasoning tasks, but they still struggle with some complicated reasoning tasks including logical reasoning.
Exploiting Duality in Open Information Extraction with Predicate Prompt
Open information extraction (OpenIE) aims to extract the schema-free triplets in the form of (\emph{subject}, \emph{predicate}, \emph{object}) from a given sentence.
EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction
EasyTool purifies essential information from extensive tool documentation of different sources, and elaborates a unified interface (i. e., tool instruction) to offer standardized tool descriptions and functionalities for LLM-based agents.
M3PT: A Multi-Modal Model for POI Tagging
In this paper, we propose a novel Multi-Modal Model for POI Tagging, namely M3PT, which achieves enhanced POI tagging through fusing the target POI's textual and visual features, and the precise matching between the multi-modal representations.
Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure Abduction
The vital role of analogical reasoning in human cognition allows us to grasp novel concepts by linking them with familiar ones through shared relational structures.
ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base
Analogical reasoning is a fundamental cognitive ability of humans.
Distilling Script Knowledge from Large Language Models for Constrained Language Planning
In everyday life, humans often plan their actions by following step-by-step instructions in the form of goal-oriented scripts.
Causality-aware Concept Extraction based on Knowledge-guided Prompting
The prompt adopts the topic of the given entity from the existing knowledge in KGs to mitigate the spurious co-occurrence correlations between entities and biased concepts.
Group Buying Recommendation Model Based on Multi-task Learning
Furthermore, we propose two auxiliary losses corresponding to the two sub-tasks, to refine the representation learning in our model.
Improving Continual Relation Extraction through Prototypical Contrastive Learning
Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data, of which the major challenge is the catastrophic forgetting of old tasks.
Generative Entity Typing with Curriculum Learning
To overcome these drawbacks, we propose a novel generative entity typing (GET) paradigm: given a text with an entity mention, the multiple types for the role that the entity plays in the text are generated with a pre-trained language model (PLM).
Large-scale Multi-granular Concept Extraction Based on Machine Reading Comprehension
In order to supply existing KGs with more fine-grained and new concepts, we propose a novel concept extraction framework, namely MRC-CE, to extract large-scale multi-granular concepts from the descriptive texts of entities.
Factorial User Modeling with Hierarchical Graph Neural Network for Enhanced Sequential Recommendation
Most sequential recommendation (SR) systems employing graph neural networks (GNNs) only model a user's interaction sequence as a flat graph without hierarchy, overlooking diverse factors in the user's preference.
Contextual Information and Commonsense Based Prompt for Emotion Recognition in Conversation
The newly proposed ERC models have leveraged pre-trained language models (PLMs) with the paradigm of pre-training and fine-tuning to obtain good performance.
Relation-Specific Attentions over Entity Mentions for Enhanced Document-Level Relation Extraction
Compared with traditional sentence-level relation extraction, document-level relation extraction is a more challenging task where an entity in a document may be mentioned multiple times and associated with multiple relations.
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Improving Information Cascade Modeling by Social Topology and Dual Role User Dependency
However, the user dependencies in a cascade sequence captured by sequential models are generally unidirectional and inconsistent with diffusion trees.
Refining Sample Embeddings with Relation Prototypes to Enhance Continual Relation Extraction
As a typical task of continual learning, continual relation extraction (CRE) aims to extract relations between entities from texts, where the samples of different relations are delivered into the model continuously.
A Question-answering Based Framework for Relation Extraction Validation
However, few works have focused on how to validate and correct the results generated by the existing relation extraction models.
Complex Relation Extraction: Challenges and Opportunities
In recent years, many complex relation extraction tasks, i. e., the variants of simple binary relation extraction, are proposed to meet the complex applications in practice.
Convolutional Gaussian Embeddings for Personalized Recommendation with Uncertainty
Most of existing embedding based recommendation models use embeddings (vectors) corresponding to a single fixed point in low-dimensional space, to represent users and items.
A Knowledge-Enhanced Recommendation Model with Attribute-Level Co-Attention
Deep neural networks (DNNs) have been widely employed in recommender systems including incorporating attention mechanism for performance improvement.
Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation
These insights motivate us to propose a novel SR model MKM-SR in this paper, which incorporates user Micro-behaviors and item Knowledge into Multi-task learning for Session-based Recommendation.
