LLM Evaluation System

Block 1 (Days 1–15): Foundations & Environment Setup

• Topics: Overview of LLM evaluation goals and benchmarks (MT-Bench, HELM, BBH, Arena Hard). Survey existing frameworks (OpenAI Evals, LangChain/Evals, DeepEval). Set up Python environment, version control (Git) and data versioning (DVC). Design a reproducible evaluation pipeline blueprint (FastAPI + task queue).

• Deliverables: A GitHub repo scaffold with project structure and CI templates. A simple “hello world” eval example (e.g. one QA prompt evaluated on two LLMs). Documentation of system requirements and architecture. Initial blog post outlining goals and design.

• Tools & Data: Python, virtualenv /Poetry, Git/GitHub, Docker, DVC (for dataset/experiment tracking), OpenAI API keys. Sample HF models or playground queries. A basic dataset (e.g. few-shot QA or tasks from HELM).

• References: Leverage OpenAI’s Evals framework as a starting point (Evals provide a framework for evaluating LLMs) and LangChain’s LangSmith tools for LLM-as-judge evaluation (Evaluation Quick Start | LangSmith). Review LMSYS/FastChat (Chatbot Arena) for design inspiration (GitHub - lm-sys/FastChat: An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.).

• Best Practices: Establish Git-based workflows and clear versioning (including DVC for data). Save all prompts, model commits, and seeds to ensure reproducibility. Begin writing unit tests for evaluation components. Define coding standards and CI for evaluation scripts.

Block 2 (Days 16–30): Data & Task Pipeline Development

• Topics: Build pipelines for task and dataset creation. Focus on diverse evaluation tasks: question answering, summarization, coding, bias/toxicity, hallucination tests. Use Hugging Face Datasets (e.g. TruthfulQA, MMLU, SQuAD, Toxicity datasets). Develop data transformations (e.g. multiple-choice generation, context injection). Discuss data ethics and crowdworker roles (e.g. fair treatment, training).

• Deliverables: Code modules that download/process benchmark datasets (with DVC for versioning). A set of canonical eval tasks (JSON/JSONL files) covering at least 3 domains (e.g. reasoning QA, summarization, toxicity). Data quality report and annotation guidelines document. Blog post on data selection and ethical considerations (mentioning crowdworker fairness).

• Tools & Data: HuggingFace Datasets/Transformers, Pandas, JSONL utilities. Datasets: TruthfulQA (GitHub - sylinrl/TruthfulQA: TruthfulQA: Measuring How Models Imitate Human Falsehoods), a summarization dataset (CNN/DailyMail), a coding problem set, a toxicity corpus (e.g. Civil Comments). DVC for data pipelines. LabelStudio or custom script for preliminary human labeling design.

• References: Use the TruthfulQA repo as example of an eval dataset with “truthfulness” tasks (GitHub - sylinrl/TruthfulQA: TruthfulQA: Measuring How Models Imitate Human Falsehoods). Follow guidelines on crowdsourcing ethics (ensuring fair pay/support to avoid exploitation) (“Lost in the crowd”: ethical concerns in crowdsourced evaluations of LLMs | AI and Ethics). Study how large benchmarks categorize tasks (Navigating the LLM Benchmark Boom: A Comprehensive Catalogue).

• Best Practices: Enforce data versioning and documentation (e.g. DVC, Markdown datacards). Anonymize any sensitive content. Randomize and seed splits for reproducibility. Prepare clear annotation instructions with examples. Log data lineage for audit trails.

Block 3 (Days 31–45): Core Evaluation Pipeline & APIs

• Topics: Implement the core evaluation backend. Develop FastAPI endpoints to submit prompts and retrieve model responses (supporting OpenAI, Anthropic, HuggingFace models). Integrate OpenAI Evals registry for standard benchmarks (GitHub - openai/evals: Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.). Use LangChain/Evals tooling to write custom evaluators for tasks. Plan a Celery task queue for async evaluation (to be built out later).

• Deliverables: Working FastAPI app with endpoints like /eval?model=X&task=Y. Example use-case: submit a few test prompts and get scored responses. CLI or script to run bulk evals on a model. Unit tests for evaluation flow. Baseline evaluation report on one sample task (e.g. GPT-4 vs local model on truthfulness). Technical blog on setting up the eval server.

• Tools & Data: Python FastAPI/Uvicorn, Celery (initial integration), Redis/RabbitMQ for broker, PostgreSQL (light setup) or SQLite for results. OpenAI Evals library (pip install evals) and LangChain/Eval packages (Evaluation Quick Start | LangSmith). HF Transformers for local model inference. Example prompt files from Block 2.

• References: Leverage the OpenAI Evals framework (it “offers an existing registry of evals”) and LangSmith’s open-evals for LLM-as-judge components (Evaluation Quick Start | LangSmith). Note that LMSYS FastChat provides OpenAI-compatible RESTful APIs for multi-model serving (GitHub - lm-sys/FastChat: An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.) – mimic this interface style for interoperability.

• Best Practices: Containerize the service (Docker) and define clear API schemas (OpenAPI). Validate and sanitize inputs. Ensure idempotent tasks (so re-running evaluations is safe). Implement logging of all requests and random seeds for traceability.

Block 4 (Days 46–60): Human-in-the-Loop Annotation System

• Topics: Design a UI/workflow for human evaluation. Implement interfaces for crowdworkers/ annotators to rate or label model outputs (e.g. side-by-side comparisons, Likert scales). Include multilanguage support and task randomization. Cover crowdworker management: instructions, pay schemes, and quality control (gold questions, consensus). Address ethical considerations (avoid exploitation, ensure fair wage) (“Lost in the crowd”: ethical concerns in crowdsourced evaluations of LLMs | AI and Ethics).

• Deliverables: A functional annotation UI (using Gradio or Streamlit) showing model responses alongside prompts or references. Backend to record human ratings in the database. A set of annotated examples with inter-annotator agreement analysis. Documentation on annotator guidelines and consent. A blog entry discussing crowdworkflow (citing the “diffusion of responsibility” in crowdsourcing).

• Tools & Data: Gradio or Streamlit for quick web UIs. JavaScript (Vue/React) or a form-based dashboard if needed. Authentication for annotators (token-based access). Use sample prompts from Block 2. Possibly Label Studio for advanced annotation management. Records stored in Postgres.

• References: LMSYS Chatbot Arena used crowdsourced human votes in “side-by-side LLM battles” with an Elo leaderboard (GitHub - lm-sys/FastChat: An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.) – use a similar voting/rating approach. Consult crowdsourcing ethics studies (e.g. bias and exploitation issues [“Lost in the crowd”: ethical concerns in crowdsourced evaluations of LLMs | AI and Ethics]) when designing tasks and compensation.

• Best Practices: Assign multiple annotators per item to measure reliability. Include control questions to check attentiveness. Randomize model order in comparisons to avoid bias. Keep paragraphs short in tasks and provide examples. Store annotation timestamps and annotator IDs for auditing.

Block 5 (Days 61–75): Automated Grading with LLM Judges

• Topics: Integrate LLM-as-judge evaluators. Use strong LLMs (GPT-4, Claude) to automatically grade or score outputs against rubrics. Implement chains-of-thought prompting for rubric scoring. Compare automated scores to human labels to calibrate. Explore fine-grained metrics (e.g. helpfulness vs correctness).

• Deliverables: Code modules that send model outputs to GPT-4/Claude for grading (e.g. “Rate this answer 1–5 on correctness and helpfulness”). A small leaderboard that shows automated vs human scores for test cases. Analysis report on LLM-judge reliability (citing that GPT-4 aligns with humans ~80% of time [MT-Bench (Multi-turn Benchmark) — Klu]).

• Tools & Data: OpenAI API (GPT-4o/GPT-4), Anthropic API (Claude). Use LangChain to orchestrate LLM-as-judge calls. Include “evaluator” scripts from the OpenEvals package (GitHub - openai/evals: Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.). Benchmark example: MT-Bench uses GPT-4 as judge (MT-Bench (Multi-turn Benchmark) — Klu).

• References: Reference the MT-Bench approach: “advanced LLM judges (e.g., GPT-4) score multi turn conversations”. The Klu.ai guide notes GPT-4 “scores and explains responses, aligning with human preferences over 80% of the time”. Base our grading approach on these insights.

• Best Practices: Avoid self-evaluation bias (do not ask an LLM to grade its own output). Use clear rubric prompts and sample answers. Log the full reasoning from the judge LLM for transparency. Store multiple metrics (e.g. helpfulness and harmlessness). Regularly validate automated grading with occasional human reviews.

Block 6 (Days 76–90): Expanding Task Coverage (Summarization, Coding, Reasoning)

• Topics: Add diverse evaluation tasks: multi-step reasoning, summarization, code generation, translation. Build pipelines to generate and evaluate answers (e.g. ROUGE/BLEU for summaries, execution tests for code). Incorporate Chain-of-Thought tasks (BigBench Hard subset) (BBH Dataset | Papers With Code). Plan multilingual tasks.

• Deliverables: Working pipelines for (a) summarization evaluation (with ROUGE/LexRank), (b) coding challenges (execute code against test cases, e.g. via exec or Docker sandbox), (c) logical reasoning QA (with step-by-step output). Report on model baselines on these tasks. Code to auto-run these evals in batch. Blog on task diversity (mention how BBH tasks require CoT [BBH Dataset | Papers With Code]).

• Tools & Data: NLTK/ROUGE score, LangChain/SBERT for summary similarity. For code: a Dockerized Python executor or exec() with timeouts (e.g. pytest harness). Datasets: CNN/DailyMail (summarization), CodeContests or HumanEval (code), BBH tasks (BBH Dataset | Papers With Code). BigBench Hard via lm-eval-harness.

• References: Use the BBH description: “23 tasks requiring multi-step reasoning” and note that Chain-of-Thought prompting dramatically improved scores. Leverage existing evaluation harnesses (lm-eval-harness/BigBench) for implementation.

• Best Practices: Use few-shot or CoT prompts as needed and document prompt templates. Keep code sandboxed for security. Track and fix randomness in summarization (set random seed for BLEU). Ensure reproducible splits for tasks.

Block 7 (Days 91–105): Safety, Guardrails & Ethical Scoring

• Topics: Integrate guardrails to filter/score outputs: toxicity/hate detection, bias checks, refusal compliance, privacy leaks. Implement input validation (prompt injection detectors) and output screening. Use oracles like Perspective API, Detoxify, or custom classifiers. Plan refusal tests (e.g. refusal on disallowed queries).

• Deliverables: A safety pipeline that flags each model response on categories (e.g. “Toxicity: yes/no, Bias: category”). Hook DeepEval or similar for automated guards (LLM Guardrails for Data Leakage, Prompt Injection, and More - Confident AI). A set of adversarial prompts (from known red-teaming benchmarks). Report on model safety metrics (harmlessness). Update UI to display guardrail scores. Research report citing common vulnerabilities (prompt injection, data leakage) and our countermeasures.

• Tools & Data: Perspective API or open-source toxicity models. Confident AI’s DeepEval for guardrail scripts (LLM Guardrails for Data Leakage, Prompt Injection, and More - Confident AI). Protected attribute datasets (BiasBench, BBQ, etc.). Custom regex detectors for PII. For prompt attacks: use Jailbreak and “DoAnything” corpora.

• References: Guardrails are “rules and filters… to protect LLMs from vulnerabilities like data leakage, bias, hallucination, prompt injections” offer examples of toxicity and privacy checks (LLM Guardrails for Data Leakage, Prompt Injection, and More - Confident AI). Confident AI’s guide and DeepEval library (LLM Guardrails for Data Leakage, Prompt Injection, and More - Confident AI). Incorporate best practices from safety benchmarks (e.g. TrustLLM, AIRBench).

• Best Practices: Always run guardrails in a fail-safe manner (retry generation on violations). Keep an audit log of all refusals/filters triggered. For privacy, do not expose user data in logs. Regularly update filter models as threats evolve.

Block 8 (Days 106–120): Leaderboard Backend & Frontend

• Topics: Develop a model-ranking leaderboard supporting multiple metrics (helpfulness, correctness, harmlessness). Include versioning of models and tasks. Support multi-turn conversation logs. Compute composite scores (e.g. weighted sum of metrics or Elo rating from pairwise comparisons [GitHub - lm-sys/FastChat: An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.]).

• Deliverables: Database schema (tables for models, versions, tasks, metrics). A web UI displaying a table/graph of model rankings, with filters (task type, language, date). Example multi-turn conversation records stored/replayed for each eval. Demo submission flow showing how a new model’s scores appear. Technical write-up on the ranking algorithm (e.g. Elo or average-of-metrics).

• Tools & Data: PostgreSQL (or Timescale) for storing results. Web framework for frontend (React or a JS grid library, or a Flask+Jinja2 static page). Charting (Chart.js or VegaLite). Use FastChat’s approach: Chatbot Arena used a human-voted Elo leaderboard (GitHub - lm-sys/FastChat: An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.) – consider implementing Elo score from pairwise preferences. Display “trace” of evaluation (full prompt–response sequence) on demand.

• References: Cite Chatbot Arena/Elo leaderboard: “Chatbot Arena has collected over 1.5M human votes… to compile an online LLM Elo leaderboard”. Note that MT-Bench also provides an Elo score (MT-Bench (Multi-turn Benchmark) — Klu). Ensure the leaderboard reflects reproducibility (model version tags, random seeds).

• Best Practices: Record immutable evaluation traces (input, output, evaluator, timestamp) for audit. Include pagination and filtering for large data. Keep UI responsive (use caching or pre aggregation). Log submissions (user, timestamp) to prevent abuse.

Block 9 (Days 121–135): Scalable Backend & CI/CD

• Topics: Architect for scale. Use FastAPI + Uvicorn behind a load balancer. Offload evals to Celery worker pools. Set up Redis or RabbitMQ as queue/broker. Implement live logging (to ELK or Sentry) of eval runs. Develop an auth system (API keys or OAuth) to handle public/private projects. Ensure all components run in Docker/Kubernetes.

• Deliverables: A docker-compose or Helm chart for full stack (API, worker, DB, Redis). CI/CD pipelines (GitHub Actions) that auto-test the eval scripts on push (including a few fixed test cases). A monitoring dashboard (Prometheus/Grafana) tracking throughput/latency. Authentication: e.g. API token management and simple web login. System design doc.

• Tools & Data: Docker, Kubernetes (optional). Celery for asynchronous tasks, Redis/RabbitMQ as broker. Prometheus for metrics, Grafana for dashboards. GitHub Actions or Jenkins for CI (run unit tests, basic eval tests). Data version control (DVC) integrated into CI (Version Control & CI/CD for LLM Projects | Operations).

• References: Reinforce version control/CI best practices: “Applying disciplined development practices like version control and automated pipelines is just as significant for LLM applications as for traditional software”. Use DVC to track data/model versions alongside code (Version Control & CI/CD for LLM Projects | Operations). FastChat already demonstrates a distributed multi-model serving system (GitHub - lm-sys/FastChat: An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.) – model our API cluster after it.

• Best Practices: Write integration tests that replay a sample eval (checkscore). Use blue/green or canary deploys for safe updates. Secure secrets (API keys) in environment variables/secret manager. Regularly back up databases.

Block 10 (Days 136–150): Benchmark Integration & Comparative Analysis

• Topics: Integrate established benchmarks into the system: MT-Bench (multi-turn chat) (MT-Bench (Multi-turn Benchmark) — Klu), HELM (GitHub - stanford-crfm/helm: Holistic Evaluation of Language Models (HELM) is an open source Python framework created by the Center for Research on Foundation Models (CRFM) at Stanford for holistic, reproducible and transparent evaluation of foundation models, including large language models (LLMs) and multimodal models.), TruthfulQA (GitHub - sylinrl/TruthfulQA: TruthfulQA: Measuring How Models Imitate Human Falsehoods), BBH (BBH Dataset | Papers With Code), and new Arena Hard data (From Live Data to High-Quality Benchmarks: The Arena-Hard Pipeline | LMSYS Org). Configure the pipeline to run these benchmarks automatically and collect results.

• Deliverables: Scripts/datasets for each benchmark. A consolidated report/table comparing all model scores on these benchmarks (including confidence intervals). Visualization of how our models rank on standard leaderboards (e.g. HuggingFace’s Elo, if accessible). Update blog with analysis of how our system’s metrics correlate with benchmarks (for auditability).

• Tools & Data: Use the MT-Bench prompts and evaluation code (available via LMSYS/GitHub) (GitHub - lm-sys/FastChat: An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.). Download HELM benchmark from Stanford or use its GitHub (GitHub - stanford-crfm/helm: Holistic Evaluation of Language Models (HELM) is an open source Python framework created by the Center for Research on Foundation Models (CRFM) at Stanford for holistic, reproducible and transparent evaluation of foundation models, including large language models (LLMs) and multimodal models.). TruthfulQA dataset (from GitHub - sylinrl/TruthfulQA: TruthfulQA: Measuring How Models Imitate Human Falsehoods). BigBench Hard tasks via HuggingFace or EleutherAI harness. Arena-Hard prompt set (from LMSYS) (From Live Data to High-Quality Benchmarks: The Arena-Hard Pipeline | LMSYS Org).

• References: Highlight capability benchmarks: “HELM… provides broad coverage (accuracy, bias, toxicity, etc.)”. Note MT-Bench’s use of LLM judges and Elo (MT-Bench (Multi-turn Benchmark) — Klu). Arena-Hard improved separability over MT-Bench (From Live Data to High-Quality Benchmarks: The Arena-Hard Pipeline | LMSYS Org) – consider using their prompts for state-of-the-art testing.

• Best Practices: Treat benchmark datasets as fixed (no peeking future data). Log model versions used. Compare metrics across runs to detect drift. Provide script to reproduce benchmark scoring with fixed seeds.

Block 11 (Days 151–165): External API Access & Community Integration

• Topics: Build APIs for third parties to submit models (or outputs) and receive score reports. Design Swagger/OpenAPI docs. Support secure file uploads or containerized model registration. Also integrate with LangChain Eval and OpenAI Evals codebases: allow pulling eval configs from those sources for extensibility.

• Deliverables: Public REST endpoints (e.g. /submit-model, /get-results). Authentication tokens for partner teams. Example client library or CLI to submit a model and fetch scores. Guide on using OpenAI Evals registry with our system (or importing custom evals). Integration proof: run an eval from the LMSYS Arena codebase or LangChain’s Eval package within our pipeline.

• Tools & Data: FastAPI (with OAuth2/JWT). Storage for submitted model artifacts (S3 or object store). CORS/webhooks for notifications. Reuse OpenAI Evals registry (via Git-LFS) for test definitions (GitHub - openai/evals: Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.). Possibly use LangChain models to call HuggingFace or other APIs on behalf of external teams (Evaluation Quick Start | LangSmith).

• References: OpenAI’s Eval framework supports private evals with your own data (GitHub - openai/evals: Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.) – mimic this by allowing user-defined eval definitions. LangChain’s openEvals also enables custom evaluators via code (Evaluation Quick Start | LangSmith). Ensure compliance (no model snooping). LMSYS FastChat demo shows “OpenAI-compatible RESTful APIs” for chatbots (GitHub - lm-sys/FastChat: An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.) – our endpoints should be similarly intuitive.

• Best Practices: Rate-limit and isolate external submissions. Provide usage examples. Log all submissions and link to results for reproducibility. Regularly update API docs and version the API.

Block 12 (Days 166–180): Final Integration, Multi-modal & Multi-lingual Support

• Topics: Extend the system to multimodal inputs and outputs. Add tasks like image captioning, visual QA, code-to-image generation, and support non-English prompts (translation evaluation). Ensure the pipeline can handle binary data (images). Conduct final system testing and polish. Publish user guides and final reports.

• Deliverables: Demo multimodal evaluation: e.g. send an image prompt and compare model captions. Support at least one multilingual task (e.g. evaluate translations with BLEU or Whisper transcription). A consolidated technical report or blog post summarizing the 6-month build, lessons learned, and demos. Demo video or interactive presentation.

• Tools & Data: Vision models/APIs (e.g. OpenAI’s image API, CLIP for scoring, or HuggingFace vision tasks). Datasets: COCO captions, VQA (Visual Question Answering Website), multilingual benchmarks (XNLI, WMT). Use LangChain’s Vision modules or custom handlers. Update the UI to display images and multi-turn chat.

• References: Multimodal benchmarks include VQA (Visual Question Answering Website), AI2D, etc. LlamaGuard3-Vision (Harvard) (Llama Guard 3 Vision: Safeguarding Human-AI Image ...) shows how to apply LLMs to image safety (for inspiration). Emphasize supporting the full stack: text, image, and code evaluations.

• Best Practices: Validate that all data pipelines (text and binary) are version-controlled. Test language settings and encoding (UTF-8) thoroughly. Finalize documentation of the entire architecture and code.

Tech Stack

• Backend: Python 3.9+, FastAPI (REST), Celery (async task queue), Redis/RabbitMQ (broker), PostgreSQL (results DB), Docker/Kubernetes containers.
• Evaluation Tools: LangChain (chains and prompts), OpenAI Evals (registry of evals) (GitHub - openai/evals: Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.), DeepEval (guardrails/safety library) (LLM Guardrails for Data Leakage, Prompt Injection, and More - Confident AI), HuggingFace Transformers/Datasets, lm-eval-harness (BigBench tasks).
• Model APIs: OpenAI GPT-4/ChatGPT, Anthropic Claude, Mistral (HuggingFace), local Llama/Alpaca-type models.
• Frontend/UX: Gradio or Streamlit for quick UI prototyping; React/Vue.js or Jinja2 for leaderboard/dashboard; Chart.js or VegaLite for visuals.
• CI/CD & DevOps: Git/GitHub (code + issues), GitHub Actions (tests, Docker build), DVC (data and model versioning) (Version Control & CI/CD for LLM Projects | Operations), Docker, (optionally Helm/K8s for deployment).
• Monitoring & Logging: Prometheus/Grafana (metrics), ELK or Sentry (logs), Weights & Biases or MLFlow for experiment tracking (optional).

• Miscellaneous: Pandas/Numpy, NLTK/ROUGE, code execution sandbox (pytest/Docker), fairness/toxicity libraries (Perspective API, Detoxify), OAuth2/JWT for auth.

This comprehensive roadmap ensures a robust, reproducible LLM evaluation platform with end-to-end coverage—from automated scoring and human annotation to leaderboards and safety—drawing on state-of-the-art benchmarks and frameworks. All code and data pipelines are version-controlled and documented for transparency.