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Notes on large language models
160 notes, page 6 of 7
Adaptive Cluster-First Route-Second Decomposition for Industrial-Scale Vehicle Routing
The researchers propose an adaptive system for solving large-scale capacitated vehicle routing problems (CVRPs). The system uses a large language model (LLM) as a high-level decision maker to analyze the evolving…
GR2 Technical Report
Researchers present GR2 (Generative Reasoning Re-Ranker), a framework designed to improve the performance of recommendation systems in an industrial setting. The system combines mid-training on semantic IDs…
Harnessing Textual Refusal Directions for Multimodal Safety
Researchers propose a new approach called Modality-Agnostic Refusal Steering (MARS) to improve safety in Multimodal Large Language Models without requiring unsafe multimodal data. MARS uses textual refusal directions…
RAISE: LLM-based Automated Heuristic Design with Robust Adversary Instance Search
The paper proposes a framework called Robust Adversary Instance Search (RAISE), which uses Large Language Models (LLMs) to design heuristics that perform well under real-world distributional shifts. The framework…
AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs
The paper proposes AgentsNet, a new benchmark for multi-agent reasoning. This benchmark is designed to measure the ability of multi-agent systems to collaborate effectively and leverage their network topology. The…
Can LLMs Rank? A Tale of Triads and Triage
The authors discuss the use of Large Language Models (LLMs) in ranking people for scarce resources, such as housing or emergency services. They present two methods for assessing the consistency of LLM's judgments: one…
Collective cooperation without individual fidelity in LLM agents
Researchers compared the behavior of Large Language Models (LLMs) to humans in a networked Prisoner's Dilemma experiment. While LLMs could replicate some collective cooperation patterns, they failed to match human…
MCP Server Architecture Patterns for LLM-Integrated Applications
This industry experience paper presents five architectural patterns observed in the use of Model Context Protocol (MCP) servers, which connect large language models to external tools, data sources, and services. The…
Translating Natural Language to Strategic Temporal Specifications via LLMs
Researchers have developed a framework for translating natural language descriptions of strategic requirements into well-formed formulas using Large Language Models (LLMs). They created and curated a novel dataset for…
Translating Natural Language to Strategic Temporal Specifications via LLMs
Researchers have developed a framework for translating Natural Language descriptions of strategic requirements into well-formed ATL/ATL* formulas using Large Language Models (LLMs). They created and validated a novel…
Using Large Language Models as Low-Cost Statistical Estimators for Human-Response Data
A study proposes using large language models (LLMs) as a cheaper alternative to human subject experiments for quantitative research in social and behavioral sciences. The LLM is shown to estimate conditional…
Words Speak Louder Than Code: Investigating Cognitive Heuristics in LLM-Based Code Vulnerability Detection
In this research, the authors investigate how Large Language Models (LLMs) used for automated vulnerability detection in code are influenced by cognitive biases similar to those that affect human judgment. They present…
A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models
A new pipeline for creating synthetic clinical notes is introduced to help develop AI systems in healthcare without compromising privacy. The pipeline generates structured, semi-structured, and unstructured clinical…
Ask, Don't Judge: Binary Questions for Interpretable LLM Evaluation and Self-Improvement
Researchers have developed a new system called BINEVAL for evaluating large language models (LLMs). This system breaks down the evaluation criteria into binary questions, making it easier to understand the results and…
Ask, Don't Judge: Binary Questions for Interpretable LLM Evaluation and Self-Improvement
Researchers have developed a new method called BINEVAL to evaluate the output of large language models (LLMs). Instead of relying on human judgment or lexical metrics, which can be expensive and unreliable, BINEVAL…
Auditing Framing-Sensitive Behavioral Instability in Large Language Models for Mental Health Interactions
Researchers investigated how different contexts or framings can affect the responses of large language models (LLMs) used in mental health support tools and psychologically sensitive conversations. They found that…
Autoregressive Boltzmann Generators
A new method called Autoregressive Boltzmann Generators (ArBG) is proposed to efficiently sample molecular systems at thermodynamic equilibrium. This method overcomes limitations of previous methods by using an…
Diagnosing Task Insensitivity in Language Agents
The research discusses a problem with large language models where they struggle to adapt to new tasks outside of their original training data (OOD generalization). The authors call this issue 'task insensitivity'. They…
Joint Learning of Experiential Rules and Policies for Large Language Model Agents
Researchers present a method called Joint Learning of Experiential Rules and Policies for Large Language Model Agents (JERP). This method aims to help LLM agents, which operate in multi-step interactive environments…
Prompt Injection in Automated Résumé Screening with Large Language Models: Single and Multi-Injection Settings
The research investigates the impact of 'prompt injection' on automated resume screening using large language models (LLMs). Prompt injection refers to strategic manipulation in text to influence LLM evaluations. The…
Prompt Injection in Automated Résumé Screening with Large Language Models: Single and Multi-Injection Settings
The study explores the impact of prompt injection, a self-promotional text strategy used by job applicants to manipulate automated hiring systems, on Large Language Models (LLMs) in résumé screening. The research shows…
ReaORE: Reasoning-Guided Progressive Open Relation Extraction Empowered by Large Reasoning Models
The paper introduces a new method called Reasoning-guided progressive Open Relation Extraction (ReaORE) to improve the extraction of unseen relations between entities in text for real-world applications. Current…
ReaORE: Reasoning-Guided Progressive Open Relation Extraction Empowered by Large Reasoning Models
The paper proposes a new method called Reasoning-guided progressive Open Relation Extraction (ReaORE) to improve the performance of Open Relation Extraction (OpenRE). This method addresses the limitations of current…
Semantic Early-Stopping for Iterative LLM Agent Loops
The research presents a method called semantic early-stopping for multi-agent large language model (LLM) loops, such as a Writer and a Critic. The goal is to halt the loop when the consecutive draft embeddings no…
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