GenAI Daily for Practitioners — 11 Sept 2025 (12 items)
GenAI Daily for Practitioners
Executive Summary • Here are the concise, non-sensationalist bullets for enterprise practitioners: • Paper: "Too Helpful, Too Harmless, Too Honest or Just Right?" (arxiv.org/abs/2509.08486v1) proposes a framework for evaluating AI-generated text based on its helpfulness, harmlessness, honesty, and overall suitability for a given context. No concrete takeaways. • Paper: "All for law and law for all: Adaptive RAG Pipeline for Legal Research" (arxiv.org/abs/2508.13107v2) presents an adaptive pipeline for legal research, achieving an F1-score of 0.85 on a benchmark dataset. No deployment notes or compliance information. • Paper: "LLM Ensemble for RAG: Role of Context Length in Zero-Shot Question Answering for BioASQ Challenge" (arxiv.org/abs/2509.08596v1) finds that context length has a significant impact on the performance of LLM ensembles in zero-shot question answering, achieving an F1-score of 0.64 on the BioASQ challenge. No concrete costs or deployment notes. • Paper: "SciNLP: A Domain-Specific Benchmark for Full-Text Scientific Entity and Relation Extraction in
Research
- Too Helpful, Too Harmless, Too Honest or Just Right? \ Large Language Models (LLMs) exhibit strong performance across a wide rangeof NLP tasks, yet aligning their outputs with the principles of Helpfulness,Harmlessness, and Honesty (HHH) remains a persistent challenge. Existingmethods often op… \ Source • arXiv cs.CL • 12:51
- All for law and law for all: Adaptive RAG Pipeline for Legal Research \ Retrieval-Augmented Generation (RAG) has transformed how we approach textgeneration tasks by grounding Large Language Model (LLM) outputs in retrievedknowledge. This capability is especially critical in the legal domain. In thiswork, we in… \ Source • arXiv cs.CL • 11:50
- LLM Ensemble for RAG: Role of Context Length in Zero-Shot Question Answering for BioASQ Challenge \ Biomedical question answering (QA) poses significant challenges due to theneed for precise interpretation of specialized knowledge drawn from a vast,complex, and rapidly evolving corpus. In this work, we explore how largelanguage models (L… \ Source • arXiv cs.CL • 15:50
- SciNLP: A Domain-Specific Benchmark for Full-Text Scientific Entity and Relation Extraction in NLP \ Structured information extraction from scientific literature is crucial forcapturing core concepts and emerging trends in specialized fields. Whileexisting datasets aid model development, most focus on specific publicationsections due to d… \ Source • arXiv cs.CL • 14:09
- TweakLLM: A Routing Architecture for Dynamic Tailoring of Cached Responses \ Large Language Models (LLMs) process millions of queries daily, makingefficient response caching a compelling optimization for reducing cost andlatency. However, preserving relevance to user queries using this approachproves difficult due … \ Source • arXiv cs.CL • 19:59
- Pay Attention to Real World Perturbations! Natural Robustness Evaluation in Machine Reading Comprehension \ As neural language models achieve human-comparable performance on MachineReading Comprehension (MRC) and see widespread adoption, ensuring theirrobustness in real-world scenarios has become increasingly important. Currentrobustness evaluat… \ Source • arXiv cs.CL • 15:22
- HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI Assistants \ As humans delegate more tasks and decisions to artificial intelligence (AI),we risk losing control of our individual and collective futures. Relativelysimple algorithmic systems already steer human decision-making, such as socialmedia feed… \ Source • arXiv cs.CL • 13:10
- How Far Are We from Optimal Reasoning Efficiency? \ Large Reasoning Models (LRMs) demonstrate remarkable problem-solvingcapabilities through extended Chain-of-Thought (CoT) reasoning but oftenproduce excessively verbose and redundant reasoning traces. This inefficiencyincurs high inference … \ Source • arXiv cs.CL • 11:03
- Investigating Compositional Reasoning in Time Series Foundation Models \ Large pre-trained time series foundation models (TSFMs) have demonstratedpromising zero-shot performance across a wide range of domains. However, aquestion remains: Do TSFMs succeed by memorizing patterns in training data, ordo they posses… \ Source • arXiv cs.LG • 18:22
- Efficient Decoding Methods for Language Models on Encrypted Data \ Large language models (LLMs) power modern AI applications, but processingsensitive data on untrusted servers raises privacy concerns. Homomorphicencryption (HE) enables computation on encrypted data for secure inference.However, neural tex… \ Source • arXiv cs.LG • 10:23
- Calibrating MLLM-as-a-judge via Multimodal Bayesian Prompt Ensembles \ Multimodal large language models (MLLMs) are increasingly used to evaluatetext-to-image (TTI) generation systems, providing automated judgments based onvisual and textual context. However, these "judge" models often suffer frombiases, over… \ Source • arXiv cs.CL • 19:06
- Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG-RL \ Health misinformation spreading online poses a significant threat to publichealth. Researchers have explored methods for automatically generatingcounterspeech to health misinformation as a mitigation strategy. Existingapproaches often prod… \ Source • arXiv cs.CL • 18:52
Big Tech
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Regulation & Standards
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