Search Results for author:
Found 27 papers, 19 papers with code
Enhancing Chinese Pre-trained Language Model via Heterogeneous Linguistics Graph
Chinese pre-trained language models usually exploit contextual character information to learn representations, while ignoring the linguistics knowledge, e. g., word and sentence information.
RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation
Generative models have demonstrated considerable potential in software engineering, particularly in tasks such as code generation and debugging.
MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization
Scientific data visualization plays a crucial role in research by enabling the direct display of complex information and assisting researchers in identifying implicit patterns.
Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents
Current language model-driven agents often lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions.
UniMem: Towards a Unified View of Long-Context Large Language Models
Although there exist various methods devoted to enhancing the long-context processing ability of large language models (LLMs), they are developed in an isolated manner and lack systematic analysis and integration of their strengths, hindering further developments.
Investigate-Consolidate-Exploit: A General Strategy for Inter-Task Agent Self-Evolution
This paper introduces Investigate-Consolidate-Exploit (ICE), a novel strategy for enhancing the adaptability and flexibility of AI agents through inter-task self-evolution.
DebugBench: Evaluating Debugging Capability of Large Language Models
Previous evaluations of LLMs' debugging ability are significantly limited by the risk of data leakage, the scale of the dataset, and the variety of tested bugs.
GitAgent: Facilitating Autonomous Agent with GitHub by Tool Extension
As GitHub has hosted a multitude of repositories which can be seen as a good resource for tools, a promising solution is that LLM-based agents can autonomously integrate the repositories in GitHub according to the user queries to extend their tool set.
ProAgent: From Robotic Process Automation to Agentic Process Automation
Empirical experiments are conducted to detail its construction and execution procedure of workflow, showcasing the feasibility of APA, unveiling the possibility of a new paradigm of automation driven by agents.
Rational Decision-Making Agent with Internalized Utility Judgment
Central to the development of rationality is the construction of an internalized utility judgment, capable of assigning numerical utilities to each decision.
AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors
Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks.
Prompt2Gaussia: Uncertain Prompt-learning for Script Event Prediction
In this paper, we regard public pre-trained language models as knowledge bases and automatically mine the script-related knowledge via prompt-learning.
ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
Based on ToolBench, we fine-tune LLaMA to obtain an LLM ToolLLaMA, and equip it with a neural API retriever to recommend appropriate APIs for each instruction.
Ranked #3 on
Trajectory Planning
on ToolBench
Exploring Format Consistency for Instruction Tuning
Instruction tuning has emerged as a promising approach to enhancing large language models in following human instructions.
Communicative Agents for Software Development
At the core of this paradigm lies ChatDev, a virtual chat-powered software development company that mirrors the established waterfall model, meticulously dividing the development process into four distinct chronological stages: designing, coding, testing, and documenting.
Tool Learning with Foundation Models
Considering the lack of a systematic tool learning evaluation in prior works, we experiment with 18 representative tools and show the potential of current foundation models in skillfully utilizing tools.
Contrastive Cross-Domain Sequential Recommendation
Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains.
Enhancing Multimodal Entity and Relation Extraction with Variational Information Bottleneck
For the first issue, a refinement-regularizer probes the information-bottleneck principle to balance the predictive evidence and noisy information, yielding expressive representations for prediction.
Event Causality Extraction with Event Argument Correlations
Event Causality Identification (ECI), which aims to detect whether a causality relation exists between two given textual events, is an important task for event causality understanding.
Relation-Guided Few-Shot Relational Triple Extraction
To instantiate this strategy, we further propose a model, RelATE, which builds a dual-level attention to aggregate relationrelevant information to detect the relation occurrence and utilizes the annotated samples of the detected relations to extract the corresponding head/tail entities.
Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck
As a promising way, Cross-Domain Recommendation (CDR) has attracted a surge of interest, which aims to transfer the user preferences observed in the source domain to make recommendations in the target domain.
Document-Level Event Extraction via Human-Like Reading Process
Meanwhile, rough reading is explored in a multi-round manner to discover undetected events, thus the multi-events problem is handled.
Enhanced Language Representation with Label Knowledge for Span Extraction
Recent works introduce the label knowledge to enhance the text representation by formalizing the span extraction task into a question answering problem (QA Formalization), which achieves state-of-the-art performance.
Deep Structural Point Process for Learning Temporal Interaction Networks
A temporal interaction network consists of a series of chronological interactions between users and items.
Few-Shot Event Detection with Prototypical Amortized Conditional Random Field
Event detection tends to struggle when it needs to recognize novel event types with a few samples.
Label Enhanced Event Detection with Heterogeneous Graph Attention Networks
A heterogeneous graph attention networks is then introduced to propagate relational message and enrich information interaction.
Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clustering
We first build a representation extractor to derive features for unlabeled data from the target domain (no test data is necessary) and then group them with a cluster miner.
