Rigorous AI/LLM Study Methodology Review

@sajjadtaghvaeifr

1. Hello there I have already published similar researches. I have already worked with many LLM and multimodal LLM models. ALos I have worked with metahuristics models for hyperparametrs tuning 2. I have PHD rom sendai university in mechatronics. 3. Elsevier Expert system with application MDPI smart cities . 4. I will inform you this directly. 5. This Wednesday. 6. yes.

@edulov

Hi 1. Ph.D. in Applied Math and CS 2. Overall 30+ of experience 3. Last 7 years - working in ML/AI and doing a lot with LLM, especially in advanced and effective prompting 4. Well aware about evaluation, but slightly from the practical side. 5. I am to sincere for many project delivering critique and saying truth. In this case it becomes a huge plus. 6. I had a lot of NDA in my life. NONE violated, even after expiration 7. Taking into account specifics of your project I am asking to pay some immediately as a first milestone. 8. Immediately available. Regards.

@divyam12345

Dear Sir/Madam, I have strong experience in AI research, LLM evaluation, and academic review. I can provide a rigorous and critical methodology review covering experimental design, scoring bias, statistical analysis, reproducibility, and benchmark readiness. Let’s connect in the chatbox to discuss the project further, including the budget and timeline. I am ready to work with you, please connect in the chatbox for further discussions. Thank You. Dr. Divya.

@shadabkhan92

My background in Machine Learning (ML) makes me uniquely qualified to undertake this project. I have significant experience in conducting critical evaluations and audits, a skill set that aligns directly with your needs. I can assure you a hostile yet fair review that will examine your pilot study meticulously, identifying even the smallest of flaws because, let's be honest, neutral feedback is not what you're after. Importantly, my PhD and subsequent industry research positions have provided me with firsthand knowledge of benchmarking methodologies like those employed in your study. The very nature of my work demands extensive understanding and engagement with NLP, ML, and computational linguistics domains. This ensures that I am not only informed about the prevailing literature surrounding Language Model (LLM) benchmarking but that I am also well-versed in existing protocols for quality evaluation as suggested by Zheng et al. 2023. Finally, one key aspect that sets me apart is my ability to implement AI systems into diverse real-world applications — a skill set that extends beyond mere prototypes into production infrastructure. I am confident that my unique interdisciplinary approach encompassing AI systems, Odoo ERP end-to-end implementation, custom IoT hardware design, as well as full-stack development with instruments like React, Flutter, Django, Node - closely aligns

@emanz03

I will conduct a strict, hostile-but-fair audit of your LLM evaluation methodology, focusing on bias, statistical validity, and reproducibility. I’ll critically review your LLM-as-judge setup (position/length/sycophancy bias), stress-test prompt fairness across conditions, and assess whether your automated scoring truly reflects the underlying construct or introduces hidden measurement bias. I will also evaluate your statistical framework (small-sample tests, bootstrap CIs, multiple comparison control), check reproducibility under stochastic decoding settings, and flag any weak or non-defensible pre-registration claims. Finally, I’ll deliver a severity-ranked review (reject / revise / minor fixes) benchmarked against NeurIPS Datasets & Benchmarks standards. Deliverable: 6–10 page structured critique with clear, actionable issues and methodology-level recommendations.

@ammarahmed20

As an experienced Full Stack Developer with a specialty in AI and Machine Learning, I believe I have the unique skills needed for your AI/LLM Evaluation Methodology Review. My 6+ years in the industry have seen me build scalable web applications and AI-powered systems—exactly the attributes you're looking for in a reviewer. Although my background lies predominantly in engineering, considering the complexities of your project, I can seamlessly bring my proficiency of constructing clean, maintainable code and reliable systems that scale to ensure a comprehensive evaluation. One core aspect of my work that resonates with your objective is my commitment to clear communication and early technical clarity. I understand the importance of upfront transparency when it comes to rigourous evaluations like yours; identifying challenges or suggesting alternatives early goes a long way in ensuring efficiency. Moreover, my experience in working with AI integrates well with your project's need for familiarity with LLM benchmarking literature—I have previously engaged with OpenAI's LLM API, giving me invaluable insights to navigate through similar workflows.

@indreshpnk

Hello, I understand you need a hostile-but-fair AI/LLM evaluation methodology review of a pre-registered pilot benchmarking prompting conditions using LLM-as-judge plus heuristic scoring. Goal is a rigorous audit focused on bias, statistical validity, and reproducibility risks. Here’s what I can provide: 1. LLM-as-judge protocol audit covering position bias, length bias, sycophancy, and same-model judge dependence. 2. Statistical methodology review including paired non-parametric tests, bootstrap confidence intervals, multiple-comparison corrections, and critique of effect reporting validity. 3. Prompt + scorer fairness audit addressing token parity, instruction asymmetry, measurement-instrument bias, and stochastic reproducibility concerns. I bring over 4+ years of experience in NLP/ML research and LLM evaluation, with hands-on work in benchmarking systems and evaluation design aligned with HELM, MT-Bench, MMLU, and related LLM assessment frameworks, with strong focus on robustness and measurement validity. Just to clarify a few things: 1. Will full study artifacts (prompts, raw outputs, scoring rules) be shared under NDA? 2. Do you want the critique optimized for NeurIPS-style publication standards or internal improvement priority? Please come to the chat box to discuss more about your project. Best regards Indresh Kushwaha (≈ 1050–1150 chars, within limit)

@tarekhemdan

I'm a professor at the Department of Computer Science in Faculty of Science, Minia University, where I'm specializes in research areas such as Artificial Intelligence, Computer and Society, and Data Mining. With vast expertise in Pattern Recognition, Classification, Machine Learning, Image Processing, Computer Vision, Feature Extraction, Signal, Image and Video Processing, Feature Selection, Pattern Classification, Object Recognition, Image Segmentation, Data Mining and Knowledge Discovery, Image Data Analysis, Video Processing, Digital Image Processing, Image Analysis, Face Recognition, Face Detection, Segmentation, Image Recognition, Knowledge Discovery, Semantic Web, Web Mining, Information Technology, Information Extraction, Websites, Web of Data, Advanced Machine Learning, Supervised Learning, Machine Vision, and other related fields, I'm currently engaged in a project that utilizes Machine Learning algorithms to predict optimal drug combinations.

@adyashatk

PhD and postdoc at the Max Planck Institute & LMU Munich, Germany in AI and medical research. BS-MS from IISER Kolkata. Multiple peer-reviewed publications including Nature, with presentations at OHBM, ECNP, and SfN. My strongest capability across your seven is statistical evaluation methodology. I have designed evaluation frameworks on small stratified samples, benchmarked classical ML, ensemble methods, and transformer architectures, and built reproducibility into every pipeline, fixed seeds, version control, deterministic outputs. I have evaluated BERT, RoBERTa, and DistilBERT on text classification with structured metrics and SHAP explainability, and built prompt engineering pipelines using OpenAI and Anthropic APIs. I have conducted consortium-level methodology audits, so I understand rigorous scrutiny from both sides. For this review I would work in two passes. First, structural integrity: whether prompting conditions are fairly instantiated, whether the scorer is blind to design intent and not just condition identity, and whether the pre-registration is defensible. Second, the statistics: whether paired tests are correctly chosen, whether bootstrap CIs are appropriately reported, and whether the LLM-as-judge subsample is sufficient at the effect sizes being claimed. Deliverable is a severity-ranked critique distinguishing reject-worthy from fixable. New to this platform but actively freelancing across AI research and model evaluation. Available immediately. NDA: yes.