Tool learning with large language models: A survey
Recently, tool learning with large language models (LLMs) has emerged as a promising
paradigm for augmenting the capabilities of LLMs to tackle highly complex problems …
paradigm for augmenting the capabilities of LLMs to tackle highly complex problems …
The landscape of emerging ai agent architectures for reasoning, planning, and tool calling: A survey
This survey paper examines the recent advancements in AI agent implementations, with a
focus on their ability to achieve complex goals that require enhanced reasoning, planning …
focus on their ability to achieve complex goals that require enhanced reasoning, planning …
Embedding self-correction as an inherent ability in large language models for enhanced mathematical reasoning
Accurate mathematical reasoning with Large Language Models (LLMs) is crucial in
revolutionizing domains that heavily rely on such reasoning. However, LLMs often …
revolutionizing domains that heavily rely on such reasoning. However, LLMs often …
Logic-enhanced language model agents for trustworthy social simulations
We introduce the Logic-Enhanced Language Model Agents (LELMA) framework, a novel
approach to enhance the trustworthiness of social simulations that utilize large language …
approach to enhance the trustworthiness of social simulations that utilize large language …
[HTML][HTML] Dynamic Storage Optimization for Communication between AI Agents
Today, AI is primarily narrow, meaning that each model or agent can only perform one task
or a narrow range of tasks. However, systems with broad capabilities can be built by …
or a narrow range of tasks. However, systems with broad capabilities can be built by …
Abstraction-of-Thought Makes Language Models Better Reasoners
Abstract reasoning, the ability to reason from the abstract essence of a problem, serves as a
key to generalization in human reasoning. However, eliciting language models to perform …
key to generalization in human reasoning. However, eliciting language models to perform …
Sparse Rewards Can Self-Train Dialogue Agents
Recent advancements in state-of-the-art (SOTA) Large Language Model (LLM) agents,
especially in multi-turn dialogue tasks, have been primarily driven by supervised fine-tuning …
especially in multi-turn dialogue tasks, have been primarily driven by supervised fine-tuning …
Logical Discrete Graphical Models Must Supplement Large Language Models for Information Synthesis
G Coppola - arxiv preprint arxiv:2403.09599, 2024 - arxiv.org
Given the emergent reasoning abilities of large language models, information retrieval is
becoming more complex. Rather than just retrieve a document, modern information retrieval …
becoming more complex. Rather than just retrieve a document, modern information retrieval …
Engineering Data Funnel (WIP)–An Ontology-Enhanced LLM-Based Agent and MoE System for Engineering Data Processing
Automation Engineering of a process automation system is still a very manual effort due to
limited support for the interpretation and processing of process design specification …
limited support for the interpretation and processing of process design specification …
Recommender Systems Meet Large Language Model Agents: A Survey
In recent years, the integration of Large Language Models (LLMs) and Recommender
Systems (RS) has revolutionized the way personalized and intelligent user experiences are …
Systems (RS) has revolutionized the way personalized and intelligent user experiences are …