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Isi mmalite enwetara
Akụkọ ọ bụla na-ejikọta na ihe akaebe siri ike dị: isi mmalite mgbe ọ dị, ma ọ bụghị nke akọwapụtara nke ọma.
Bekee dị larịị
Gịnị mere, ihe mere o ji dị mkpa, na ihe na-ekiri - na-enweghị jargon.
Enweghị ndochi
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Akụkọ ndị ọzọ
9 akụkọIhe ohuru ohuru
Paper reports a temporal method for detecting hallucinations at the token level
An arXiv preprint describes a hallucination detector that combines text statistics, entailment signals and language-model surprisal across sequences instead of judging tokens independently. Its BiGRU model reached an AUC of 0.840 on RAGTruth, according to the paper.arxiv.orgIhe ohuru ohuru
Entity tracking emerges at 410 million parameters, exceeds humans across naturalistic narratives
Understanding language requires tracking entities across discourse - i.e., knowing where things are and how they change, even when not explicitly stated.arxiv.orgIhe ohuru ohuru
Paper reports compiler-guided search improves Lean theorem-proving efficiency
An arXiv preprint proposes an adaptive proof-search method for context-dependent Lean 4 projects. Its authors report a 12.8-percentage-point average pass-rate improvement within a pass@32 budget while using 21.9% fewer LLM calls than pass@k baselines.arxiv.orgIhe ohuru ohuru
Benchmark Separates Geometry Solving From Diagram Construction
A new open-source benchmark tests whether foundation models can construct faithful geometry diagrams, not merely solve the underlying problems. Its authors report that evaluated models achieved an average compile success rate of 36.14%, exposing a gap between mathematical answers and usable visual constructions.arxiv.orgIhe ohuru ohuru
Alignment Is All You Need: Instruction-Free Training for General Audio-Language Models
A new approach to training multimodal large language models (MLLMs) eliminates the need for extensive task-specific supervision.arxiv.orgNchekwa
Are LLMs Safe Beyond Text: Do Emojis Expose Gaps in Safety Evaluation
This work examines emoji-augmented prompts as a test case for gaps in safety evaluation of large language models (LLMs).arxiv.orgỤlọ ọrụ
Bridging Search and CRM: Productionizing AI Product Research Agents for Customer Re-Engagement
Modern e-commerce platforms often operate search, recommendation, personalization, and CRM systems independently, limiting opportunities for proactive customer re-engagement.arxiv.orgIhe ohuru ohuru
New benchmark shows Vietnam’s exam rubric can change how language models rank
A paper introduces THPT-Ladder, a 632-item benchmark that applies Vietnam’s 2025 national exam grading scheme to language models and reports materially different scores from standard proportional-accuracy measures.arxiv.orgIhe ohuru ohuru
Netflix paper outlines a lifecycle for LLM judges evaluating recommendation explanations
An arXiv paper describes how Netflix built, deployed and continuously monitored an LLM judge for recommendation explanations, reporting viewing and engagement gains in a five-week A/B test involving tens of millions of members.arxiv.org
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