Healer uses LLMs to dynamically generate and execute runtime error-handling code, with GPT-4 recovering from 72.8% of errors across four datasets.
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LLMs produce executable code only 42.55% of the time under API evolution without full documentation, improving to 66.36% with structured docs and by 11% more with reasoning strategies, yet outdated patterns persist.
GRACE dynamically constructs and updates coresets for LLM training using representation diversity, gradient-based importance, and k-NN graph propagation to improve efficiency and performance.
LLMs exhibit identity-dependent hedging on human rights questions, with group identity as the strongest predictor among tested factors, and group steering mitigates the disparity.
Systematic tests show that specific PDF parsers combined with overlapping chunking strategies better preserve structure and improve RAG answer correctness on financial QA benchmarks including the new TableQuest dataset.
SPRINT refines LLM-generated intents for session-based recommendation via a global intent pool, performance validation, selective LLM invocation during training, and a lightweight intent predictor for scalable inference without LLM calls.
RECOVER is an LLM-powered RPM system for postoperative GI cancer care, built from 7 participatory design sessions and 5 patient interviews, then piloted with 4 staff and 5 patients to derive design strategies and responsible AI insights.
A survey that organizes LLMs-as-judges research into functionality, methodology, applications, meta-evaluation, and limitations.
A systematic literature review that organizes recent work on LLMs for code generation into a taxonomy covering data curation, model advances, evaluations, ethics, environmental impact, and applications, with benchmark comparisons.
citing papers explorer
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Towards Agentic Runtime Healing
Healer uses LLMs to dynamically generate and execute runtime error-handling code, with GPT-4 recovering from 72.8% of errors across four datasets.
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When LLMs Lag Behind: Knowledge Conflicts from Evolving APIs in Code Generation
LLMs produce executable code only 42.55% of the time under API evolution without full documentation, improving to 66.36% with structured docs and by 11% more with reasoning strategies, yet outdated patterns persist.
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GRACE: A Dynamic Coreset Selection Framework for Large Language Model Optimization
GRACE dynamically constructs and updates coresets for LLM training using representation diversity, gradient-based importance, and k-NN graph propagation to improve efficiency and performance.
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Hedging and Non-Affirmation: Quantifying LLM Alignment on Questions of Human Rights
LLMs exhibit identity-dependent hedging on human rights questions, with group identity as the strongest predictor among tested factors, and group steering mitigates the disparity.
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Empirical Evaluation of PDF Parsing and Chunking for Financial Question Answering with RAG
Systematic tests show that specific PDF parsers combined with overlapping chunking strategies better preserve structure and improve RAG answer correctness on financial QA benchmarks including the new TableQuest dataset.
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SPRINT: Scalable and Predictive Intent Refinement for LLM-Enhanced Session-based Recommendation
SPRINT refines LLM-generated intents for session-based recommendation via a global intent pool, performance validation, selective LLM invocation during training, and a lightweight intent predictor for scalable inference without LLM calls.
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RECOVER: Designing a Large Language Model-based Remote Patient Monitoring System for Postoperative Gastrointestinal Cancer Care
RECOVER is an LLM-powered RPM system for postoperative GI cancer care, built from 7 participatory design sessions and 5 patient interviews, then piloted with 4 staff and 5 patients to derive design strategies and responsible AI insights.
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LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods
A survey that organizes LLMs-as-judges research into functionality, methodology, applications, meta-evaluation, and limitations.
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A Survey on Large Language Models for Code Generation
A systematic literature review that organizes recent work on LLMs for code generation into a taxonomy covering data curation, model advances, evaluations, ethics, environmental impact, and applications, with benchmark comparisons.