Tag: #research297 results
Evaluates LoRA for parameter-efficient fine-tuning of LLMs on organic reaction datasets like USPTO and C-H functionalisation. Matches full fine-tuning accuracy while preserving multi-task performance and mitigating forgetting. Reveals distinct reactivity patterns for better adaptation.
Locomo-Plus benchmarks cognitive memory in LLM agents under cue-trigger disconnects, focusing on latent conversational constraints. It proposes constraint consistency evaluation over string-matching. Reveals gaps in existing memory systems.
Study evaluates 17 LLMs on ODD-to-Python code generation for predator-prey model. Assesses executability, fidelity, efficiency via NetLogo baseline. GPT-4.1 excels, but reliability varies.
Fine-tuned LLMs like Llama predict mRS scores from admission notes alone. Achieves 33.9% exact 90-day accuracy and 76.3% binary, matching structured baselines. Enables seamless clinical integration without data extraction.
Uses AlphaEvolve to discover interpretable models of human and LLM strategic behavior from data. Analysis on iterated rock-paper-scissors shows frontier LLMs capable of deeper strategy than humans. Provides foundation for understanding behavioral differences in interactions.
Prompts pretrained LLMs to create QNP abstractions for generalized planning from domains and tasks. Automated debugging detects/fixes errors iteratively. Guided LLMs produce useful abstractions for qualitative numerical planning.
LLMs generate culturally unrepresentative recipe adaptations unlike humans. Outputs ignore cultural distance correlations from GlobalFusion dataset. Issues stem from weak cultural representations and novelty inflation.
Experience report on using LLMs for systematic mapping studies. Highlights time savings in screening and extraction but notes challenges like hallucinations and prompt engineering. Offers lessons and recommendations for adoption.
DBPlanBench exposes physical query plans for LLM-proposed localized edits, refined via evolutionary search. LLMs leverage semantic knowledge for optimizations like join orderings. Achieves up to 4.78x speedups, with transfers from small to large databases.
A self-evolving system uses Google's Gemini LLMs to autonomously generate, train, and deploy recommendation model improvements. It features an Offline Agent for hypothesis generation and an Online Agent for production validation. Deployed successfully at YouTube, surpassing manual workflows.