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Notes on large language models
160 notes, page 1 of 7
An Agentic Approach for Active Data Collection, Travel Behavior Modeling, and Weather-Sensitive Demand Prediction
The study presents a three-stage approach for combining digital data collection, predictive modeling, and weather-sensitive demand forecasting. It uses a chatbot to collect travel mode choices from student commuters…
MemTrapBench: Benchmarking Cognitive Traps in LLM Memory Use
Researchers have introduced a new benchmark called MemTrapBench to test the performance of large language models (LLMs) in terms of cognitive traps caused by their memory components. The study identifies two types of…
MidTool: Mid-training Data Synthesis for Agentic Tool Use
Researchers have developed MidTool, a system for teaching large language models how to use tools effectively during mid-training. This is done by combining large-scale web and PDF data with synthetic supervision from…
MidTool: Mid-training Data Synthesis for Agentic Tool Use
In this research, the authors introduce MidTool, a system used for training large language models during the mid-training stage. MidTool focuses on improving the model's abilities in general tool use, which includes…
Identifying Implicit Premises for Logical Reconstruction of Argument Graphs
The authors propose a neuro-symbolic pipeline to generate implicit premises from natural language text, which can be translated into logical formulae to show the logical relationships between statements (entailment…
rEDMRec: Distilling Large Language Model Reasoning into an Editable Experience Memory for Recommendation
The paper proposes a new approach for recommendation systems, called rEDMRec, which distills the reasoning of large language models into an editable memory that can be used to rank candidate items without regenerating…
Self-prompting and cross-model consensus enable reproducible data extraction from scientific literature with large language models
The study explores the ability of large language models (LLMs) to extract contextualized information from research articles efficiently. The authors present four workflows that demonstrate LLMs can perform well in data…
Tuning the Stochastic Machine: A Systems Engineer's Operating Model for Human-AI Engineering
The author, a systems engineer, argues that issues with Large Language Models (LLMs) are more about operational procedures than tooling. They propose an operating model for human-AI engineering based on seven…
What is Missing from AI Post-Training AI: An Empirical Analysis
Researchers have found that large language models (LLMs) used to post-train other LLMs often follow one training strategy from the beginning and make only local adjustments within it, rather than reevaluating the…
Semantic Bandits: In-Context Exploration-Exploitation is Biased by Semantic Priors
Researchers introduce the concept of semantic bandits to study how pre-training affects exploration behavior of Large Language Models (LLMs) used as decision-making agents. They find that action labels with semantic…
A corpus-specific clinical RAG system matches or outperforms newer frontier LLMs on HealthBench
A research study compared the performance of VITA, a specialized clinical AI tool for low- and middle-income settings, with various frontier large language models (LLMs) on the HealthBench benchmark. The study found…
RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks
Researchers have introduced a new benchmark called RealisticTritonBench to improve the evaluation of large language models (LLMs) in generating GPU kernels for AI frameworks using Triton. The new benchmark derives…
ReLTEx: Reliable LLM-based Taxonomy Expansion
The paper discusses a new framework called ReLTEx for improving the reliability of Large Language Model (LLM)-based taxonomy expansion. The framework combines LLM-driven candidate generation with structure-aware…
Agentic Harnesses: LLM-Driven Verification Layers for Robot Autonomy
Researchers propose a verification layer using large language models (LLMs) to ensure the feasibility and ethical alignment of actions planned by robot autonomy systems. The system can evaluate the permissibility of…
From Values to Benchmarks: Evaluating Large Language Models for Governmental Use in Dutch
The paper presents a new evaluation framework called 'Grip on LLMs' specifically designed for Dutch governmental use of large language models (LLMs). The framework is created in collaboration with domain experts and…
KGCaRe: Explainable Complex Conditional Question Answering using Automatic Knowledge Graph Construction and Context Retrieval with LLMs
The study proposes a new method called KGCaRe for answering complex conditional questions using large language models (LLMs) and retrieval-augmented generation (RAG). This approach combines neural retrieval with…
Parameter Exploration for RLVR via Variational Learning
Researchers propose a new approach called Perturbed Parameter Policy Optimization (3PO) to enhance exploration in large language models (LLMs). This method explores the parameter space instead of the action space…
SHE: Trajectory-driven Safety Harness Evolution for LLM Agents
Researchers propose a framework called Safety Harness Evolution (SHE) to improve the safety of large language model agents. SHE learns evolving safe boundaries from rollout trajectories, allowing for localized harness…
EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning
The paper introduces EnvACE, a method for training large language model agents for long-term tool use without relying on interactions with real or synthesized environments, which can be costly to construct and verify…
HarnessOpt-Bench: Evaluating LLMs at Harness Optimization
The text introduces HarnessOpt-Bench, a benchmark for evaluating the performance of Large Language Models (LLMs) in optimizing AI systems. The benchmark involves an iterative process where an LLM edits a given harness…
Learning Globally Reusable Skills for Coding Agents
Researchers present a framework called GSE (Globalized Skill Evolution) that aims to improve automated skill evolution for Large Language Model (LLM) agents in software engineering tasks. The proposed method addresses…
Learning Globally Reusable Skills for Coding Agents
Researchers propose a new method called GSE (Globalized Skill Evolution) to help Large Language Model agents continuously improve their skills without needing expensive retraining. The proposed method optimizes skill…
MicroEvo: Knowledge-Guided LLM Sampling for Efficient Microarchitecture Design Space Exploration
A new method called MicroEvo is proposed for efficient exploration of microarchitecture design spaces. This approach utilizes a combination of large language models (LLMs) and Monte Carlo Tree Search (MCTS) to optimize…
A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination
A research paper introduces a new framework called A-SR for symbolic regression. A-SR is designed to discover closed-form equations from data and uses a self-evolving agentic system that coordinates formula discovery…
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