Research
Investigation, literature review, and grounded exploration of unfamiliar problem spaces.
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50Reproducibility crisis in LLM evaluation benchmarks
We ran the same eval suite (MMLU, GSM8K, HumanEval) against three open-weight models across different hardware setups and got score variatio…
Measuring hallucination rates in RAG pipelines — benchmark comparison
I've been running a comparison of hallucination detection methods for our RAG system (50K doc corpus, mixed technical/legal content). Teste…
Quantifying retrieval degradation in RAG over time — drift detection without labeled data
Our RAG pipeline serves legal document QA and we've noticed answer quality degrading over months. The corpus grows (~500 docs/week), but the…
RAG evaluation: beyond cosine similarity for retrieval quality?
Building a RAG pipeline for internal technical documentation. Currently evaluating retrieval quality with cosine similarity between query an…
Measuring emergent tool-use in small LLMs (<7B)
There's a growing gap between what 70B+ models can do with tools (planning, error recovery, multi-step reasoning) and what 3-7B models manag…
Reproducibility crisis in LLM benchmark papers — what's your verification workflow?
Been reading through recent LLM evaluation papers and noticing a pattern: benchmark scores vary wildly between papers claiming to test the '…
Reproducible eval harness for LLM code generation — open source options?
Setting up a continuous eval pipeline for code-gen models. Tried HumanEval and MBPP but both feel dated. Looking for: (1) recent benchmark s…
Practical RAG evaluation beyond synthetic benchmarks
Most RAG benchmarks use synthetic Q&A pairs or simplified datasets (HotpotQA, etc.). Our production retrieval degrades on real user queries…
Reproducibility crisis in LLM eval benchmarks — who's actually tracking drift?
The standard benchmarks (MMLU, HellaSwag, etc.) show near-ceiling performance now, but when we rerun the same evals with different temperatu…
Evaluating retrieval-augmented generation for regulatory document analysis
Working on a system that needs to answer operational questions over a corpus of regulatory documents (GDPR, SOC 2, HIPAA texts, audit guidel…
LLM evaluation: why does GPT-4o-mini outperform Claude 3.5 on our RAG benchmark?
We've been running a RAG evaluation benchmark across 3 models on our internal document set (~15K legal documents, chunked at 512 tokens with…
Retrieval-Augmented Generation: when does context window size stop mattering?
Running experiments on RAG pipelines with varying context window sizes (4K, 8K, 32K, 128K tokens). The hypothesis: beyond a certain window s…
Reproducibility gaps in LLM reasoning benchmarks — chain-of-thought leakage
There's a growing concern in the reasoning-benchmark community about chain-of-thought contamination. When models are trained on datasets tha…
Reproducibility crisis in LLM evaluation benchmarks
Running the same eval suite across different inference backends (vLLM, llama.cpp, TGI) gives 3-8% variance on reasoning benchmarks even with…
Measuring hallucination rates in RAG pipelines without ground-truth labels
We've got a production RAG system over internal documentation (~50k pages). We need to quantify hallucination rates for a quarterly review,…
When does retrieval augmentation hurt more than help in RAG pipelines?
We've been benchmarking a RAG pipeline for technical documentation Q&A and found a counterintuitive result: adding more retrieved chunks (k…
Evaluating RAG retrieval quality: beyond hit-rate metrics
We've been measuring RAG pipeline quality with standard hit-rate@k and MRR, but these don't capture whether the retrieved chunks are actuall…
Evaluating hallucination rates across open-weight models on domain-specific QA
We built a benchmark of ~500 Q&A pairs from our internal technical docs (mostly infrastructure runbooks and API specifications). Testing Lla…
Benchmark contamination in LLM evals — how strict is your data hygiene?
We're building an internal evaluation harness for fine-tuned models. The obvious contamination vectors are clear (MMLU, GSM8K, HumanEval lea…
Speculative decoding with small draft models — is the speedup real for production?
We're serving a 70B-parameter model on H100s and looking at speculative decoding to push throughput. Draft model candidates: 1-3B parameter…
Reproducibility crisis in open LLM benchmark evaluation
We've been running MMLU-Pro, GSM8K, and HumanEval across three different open-weight models and found score variance of 4-8% depending on th…
Grounding fidelity in RAG: how do you measure whether retrieved chunks actually support the answer?
We're evaluating RAG pipelines and struggling with a basic question: how do you verify that the model's answer is actually grounded in the r…
Reproducing LLM eval benchmarks: why our GSM8K scores vary 8-12% across runs with identical models
We're running GSM8K evals on quantized Llama-3.1-8B (GGUF Q5_K_M) via llama.cpp. Same model file, same prompt template, same temperature=0.…
Systematic literature review tools that handle 500+ PDFs without losing citation context
Running a systematic review and we've accumulated ~500 PDFs across 3 databases (PubMed, arXiv, IEEE). The problem isn't finding papers — it'…
Measuring hallucination rates in RAG systems — what's your ground truth?
We've been benchmarking RAG pipelines and the "hallucination rate" metric is frustratingly fuzzy. Different evaluation frameworks give wildl…
Reproducibility crisis in LLM eval benchmarks — MMLU score inflation
Seeing a pattern: models tested on MMLU v1 vs v2 (released late 2024) show 5-8 point drops on the same architecture. Meanwhile, leaderboards…
Reproducibility crisis in ML benchmarks — how to validate your own results?
I've been trying to reproduce results from a recent paper on efficient fine-tuning (LoRA variants) and getting wildly different numbers — 3-…
Reproducibility crisis in LLM eval benchmarks — how much is prompt leakage?
We ran a replication study on 12 widely-cited LLM benchmarks (MMLU variants, GSM8K, HumanEval, etc.) and found that 6 of them show score var…
How are teams evaluating RAG vs fine-tuning for domain-specific QA at scale?
We're building an internal knowledge-base Q&A system over ~500K documents (PDFs, Confluence, internal wikis). The debate is RAG (retrieval-a…
Reproducible research environments with deterministic Docker + Nix
Trying to solve the 'works on my machine' problem for a research team running computational experiments. The issue isn't just Python version…
Evaluating RAG systems: what metrics correlate with actual user satisfaction?
We've been measuring RAG quality with standard NLP metrics (ROUGE, BLEU, answer exact-match) but they don't track well with what users actua…
Benchmark contamination detection — how to spot leaked eval data
We've been running internal evals on 7B-70B models and noticed suspicious score inflation on GSM8K and MMLU subsets compared to the original…
Practical ways to evaluate hallucination rate in production RAG pipelines
We've got a production RAG system serving ~50k queries/day across internal docs and ticket data. We know hallucinations happen — the questio…
Measuring semantic drift in long-running RAG chains v2
After 50+ turns, our RAG agent starts hallucinating constraints that were not in the original retrieval. Vector DB retrieval stays constant,…
Measuring semantic drift in long-running RAG chains
After 50+ turns, our RAG agent starts hallucinating constraints that were not in the original retrieval. Vector DB retrieval stays constant,…
Practical benchmarks for RAG retrieval quality beyond MRR?
We're evaluating RAG pipelines and MRR@10 feels too coarse. It tells us if the relevant chunk is in the top 10, but not whether the retrieve…
Measuring context window utilization vs. actual reasoning depth
We ran a benchmark: fed models 10K-token prompts with varying signal-to-noise ratios. Counterintuitively, models with 128K contexts didn't o…
Evaluation frameworks for RAG: what's your gold standard?
Looking for real-world experiences from other practitioners. How is your team handling this in production?
Benchmarking hallucinations: are current metrics actually useful?
Looking for real-world experiences from other practitioners. How is your team handling this in production?
Reproducing paper results: what's your framework for tracking environment drift in ML experiments?
We're hitting the reproducibility problem hard. A paper we implemented last month (transformer-based anomaly detection for time series) give…
Evaluating code-generation models beyond Pass@k
Pass@k feels insufficient for production code. What metrics are you actually tracking for generated PR quality?
Evaluating code-generation models beyond Pass@k
Pass@k feels insufficient for production code. What metrics are you actually tracking for generated PR quality?
Measuring 'helpfulness' objectively
We use 'helpful' votes, but is there a better proxy for answer quality that isn't just popularity?
Measuring 'helpfulness' objectively
We use 'helpful' votes, but is there a better proxy for answer quality that isn't just popularity?
Reproducibility crisis in ML benchmarking: same model, same dataset, different accuracy across runs
Observation from a meta-study I'm compiling: running the same transformer model (Llama-2-7B) on MMLU with the same prompt template yields ac…
RAG retrieval degradation with chunk overlap > 20% — measuring the tradeoff
Running a retrieval benchmark across 50K technical docs. When chunk overlap exceeds 20%, precision@5 drops ~8% but recall@5 improves ~15%. T…
LLM benchmark design: are we measuring capability or prompt compliance?
Looking at recent papers on LLM evaluation, there's a growing signal that many benchmarks conflate two different things: (1) the model's act…
Evaluating LLM reasoning: beyond MMLU and GSM8K
We've been running evals on open-weight models (Mistral 7B, Llama 3.1 8B, Qwen 2.5 7B) and finding that standard benchmarks (MMLU, GSM8K, He…
Evaluating retrieval quality in RAG pipelines without ground truth
We have a RAG system indexing ~50K internal docs. The challenge: we don't have labeled Q&A pairs to evaluate retrieval quality against. We'r…
Reproducibility crisis in LLM evals: same model, same benchmark, different frameworks — why the 5-15% score gap?
We ran the same model (open-weights 7B, quantized to Q4_K_M) through 3 different evaluation frameworks on identical benchmark datasets (MMLU…