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Notes on large language models
160 notes, page 5 of 7
From Voting to Agent Collaboration: Answer-Type-Aware LLM Pipelines for BioASQ 14b
Researchers present a framework using question-type-specific large language models (LLMs) to improve answer robustness and evidence grounding in biomedical question answering. The framework uses different inference…
Improving LLM-Generated Process Model Quality Through Reinforcement Learning: The Role of Reward Function Design
Researchers have improved the quality of process models generated by large language models (LLMs) using reinforcement learning (RL). They found that designing the reward function is crucial for optimizing beyond the…
Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents
The paper presents Information Gain-based Rollout Policy Optimization (IGRPO), a new framework for improving large language model agents in long-horizon search tasks. IGRPO aims to allocate rollout budget more…
LLM Agents for Deliberative Collaboration: A Study on Joint Decision Making Under Partial Observability
This research investigates the performance of large language models (LLMs) in collaborative decision-making tasks where information is not fully available to all agents. The study introduces a benchmark for such tasks…
LLM-Based Test Oracles: Source-of-Authority Taxonomy -- A Systematic Literature Review
A study on Large Language Models (LLMs) used to determine correct behavior in tests, identifies a gap in understanding the source of these models' authority. The study conducts a systematic literature review and finds…
Selective Disclosure Watermarking for Large Language Models
Researchers have proposed a new watermarking method for large language models (LLMs) that allows selective disclosure of embedded metadata. This means that the method can control which parts of the embedded message are…
Your Agent's Memories Are Not Its Own: Forged Reasoning Attacks on LLM Agent Memory and Defenses
Researchers introduce a new type of attack, called Forged Amplifying Rationale Memory Attack (FARMA), that targets the reasoning history of large language model (LLM) agents. FARMA inserts forged reasoning traces using…
Automated grading of Linux/bash examinations using large language models: a four-level cognitive taxonomy approach
The paper evaluates whether four large language models can approximate the judgement of experts when grading Linux/bash command responses from second-year Computer Engineering students. The study uses a four-level…
Automated grading of Linux/bash examinations using large language models: a four-level cognitive taxonomy approach
This research evaluates the effectiveness of four large language models in grading Linux/bash command responses from second-year Computer Engineering students' exams. The study uses a four-level cognitive taxonomy…
DemoPSD: Disagreement-Modulated Policy Self-Distillation
The paper introduces DemoPSD, a novel framework for training large language models (LLMs) that addresses the issues of overfitting, suppression of exploration, cross-domain generalization problems, and privileged…
DemoPSD: Disagreement-Modulated Policy Self-Distillation
The paper introduces DemoPSD, a framework designed to address issues in on-policy self-distillation (OPSD) for training large language models. The main problems being addressed are overfitting, suppression of…
DemoPSD: Disagreement-Modulated Policy Self-Distillation
DemoPSD is a new framework for training large language models that addresses overfitting, privileged information leakage, and exploration suppression issues in on-policy self-distillation (OPSD). It does this by…
DemoPSD: Disagreement-Modulated Policy Self-Distillation
The paper introduces DemoPSD, a framework for training large language models that addresses the issues of overfitting, suppressed exploration, and privileged information leakage in on-policy self-distillation (OPSD)…
Fast Multi-dimensional Refusal Subspaces via RFM-AGOP
The text discusses a new method for identifying multi-dimensional refusal subspaces in Large Language Models (LLMs), which are used for safety and interpretability. The proposed method adapts the Recursive Feature…
LACUNA: A Testbed for Evaluating Localization Precision for LLM Unlearning
Researchers introduce LACUNA, a testbed for evaluating the precision of unlearning methods in large language models (LLMs). The testbed allows direct evaluation of whether unlearning targets the weights responsible for…
Neuron-Aware Data Selection for Annotation-Free LLM Self-Distillation
Researchers propose a new method called Neuron On-Policy Self-Distillation (Neuron-OPSD) for training large language models without human annotations in specialized domains where they are costly to obtain. This method…
Neuron-Aware Data Selection for Annotation-Free LLM Self-Distillation
Researchers have developed a new method called Neuron On-Policy Self-Distillation (Neuron-OPSD) for training large language models without the need for real-world interaction feedback or human-labeled supervision…
Neuron-Aware Data Selection for Annotation-Free LLM Self-Distillation
In this paper, the authors propose a new method called Neuron On-Policy Self-Distillation (Neuron-OPSD) for annotation-free self-distillation of large language models. This method uses internal neuron activations to…
ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning
Researchers propose a new method called Recursive Evidence Replay (RECONTEXT) to improve the ability of large language models (LLMs) to understand and reason over long contexts. This method doesn't require training and…
Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments
Researchers have developed a new method called SPG-Layout for creating realistic indoor scenes in complex, non-rectangular (non-Manhattan) environments using large language models. This method improves upon existing…
Agentic generation of verifiable rules for deterministic, self-expanding reaction classification
Researchers have developed an automated pipeline using large language models (LLMs) that can classify reactions in chemistry and generate rules for these reactions, expanding a standard taxonomy from 68 to 14,073…
Exploring the Semantic Gap in Agentic Data Systems: A Formative Study of Operationalization Failures in Analytical Workflows
A study found that large language models (LLMs) often struggle to operationalize analytical concepts due to a semantic gap between user-level analytical concepts and the information available to workflow-generation…
SenseWalk: Agent-Based Semantic Trajectory Simulation Powered by Large Language Models in Zoned Environments
The paper proposes SenseWalk, an interactive system that uses large language models (LLMs) to simulate semantic trajectories for human movement analysis. The simulation workflow combines LLMs and the social force model…
ACE: Pluggable Adaptive Context Elasticizer across Agents
Researchers propose a new module called Adaptive Context Elasticizer (ACE) to address the challenges posed by growing trajectory lengths in large language models (LLMs) with fixed context windows. ACE is designed to be…
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