Scoring Methods for Large Language Model Evaluation

LLM-as-a-Judge (LaaJ) and reward models (RMs) are similar concepts, but understanding their nuanced differences is important for applying them correctly in practice… LLM-as-a-Judge is a reference-free evaluation metric that assesses model outputs by simply prompting a powerful language model to perform the evaluation for us. In the standard setup, we ask the model to either: - Provide a direct assessment score (e.g., binary or Likert score) of a model’s output. - Compare the relative quality of multiple outputs (i.e., pairwise scoring). There are many choices for the LLM judge we use. For example, we can use an off-the-shelf foundation model, fine-tune our own model, or form a "jury" of several LLM judges. Reward models are specialized LLMs—usually derived from the LLM we are currently training—that are trained to predict a human preference score given a prompt and a candidate completion as input. A higher score from the RM indicates higher human preference. Similarities between LaaJ and RMs: Both LaaJ and RMs can provide direct assessment and pairwise (preference) scores. Therefore, both techniques can be used for evaluation. Given these similarities, recent research has explored combining RMs and LaaJ into a single model with both capabilities. Differences between LaaJ and RMs: Despite their surface similarities, these two techniques have many fundamental differences: - RMs are fine-tuned using a preference learning or ranking objective, whereas fine-tuned LaaJ models usually learn via standard language modeling objectives. - LaaJ models are often based on off-the-shelf or foundation LLMs, whereas RMs are always fine-tuned. - LaaJ is based on a standard LLM architecture, while RMs typically add an additional classification head to predict a preference score. - RMs only score single model outputs (though we can derive a preference score by plugging multiple RM scores into a preference model like Bradley-Terry), whereas LaaJ can support arbitrary scoring setups (i.e., is more flexible). Where should we use each technique? Given these differences, recent research has provided insights into where LaaJ and RMs are most effective. LaaJ should be used for evaluation purposes (both direct assessment and pairwise). This is an incredibly powerful evaluation technique that is used almost universally. When we compare the evaluation accuracy of LaaJ (assuming correct setup and tuning) to RMs, LaaJ models tend to have superior scoring accuracy; for example, in RewardBench2, LaaJ models achieve the highest accuracy on pairwise preference scoring. Despite LaaJ’s strengths, RMs are still more useful for RL-based training with LLMs (e.g., PPO-based RLHF). Interestingly, even though LaaJ models provide more accurate preference scores, they cannot be directly used as RMs for RL training. It is important that the RM is derived from the policy currently being trained, meaning we must train a custom RM based on our current policy for RLHF to work properly.

Unlocking the Next Era of RAG System Evaluation: Insights from the Latest Comprehensive Survey Retrieval-Augmented Generation (RAG) has become a cornerstone for enhancing large language models (LLMs), especially when accuracy, timeliness, and factual grounding are critical. However, as RAG systems grow in complexity-integrating dense retrieval, multi-source knowledge, and advanced reasoning-the challenge of evaluating their true effectiveness has intensified. A recent survey from leading academic and industrial research organizations delivers the most exhaustive analysis yet of RAG evaluation in the LLM era. Here are the key technical takeaways: 1. Multi-Scale Evaluation Frameworks The survey dissects RAG evaluation into internal and external dimensions. Internal evaluation targets the core components-retrieval and generation-assessing not just their standalone performance but also their interactions. External evaluation addresses system-wide factors like safety, robustness, and efficiency, which are increasingly vital as RAG systems are deployed in real-world, high-stakes environments. 2. Technical Anatomy of RAG Systems Under the hood, a typical RAG pipeline is split into two main sections: - Retrieval: Involves document chunking, embedding generation, and sophisticated retrieval strategies (sparse, dense, hybrid, or graph-based). Preprocessing such as corpus construction and intent recognition is essential for optimizing retrieval relevance and comprehensiveness. - Generation: The LLM synthesizes retrieved knowledge, leveraging advanced prompt engineering and reasoning techniques to produce contextually faithful responses. Post-processing may include entity recognition or translation, depending on the use case. 3. Diverse and Evolving Evaluation Metrics The survey catalogues a wide array of metrics: - Traditional IR Metrics: Precision@K, Recall@K, F1, MRR, NDCG, MAP for retrieval quality. - NLG Metrics: Exact Match, ROUGE, BLEU, METEOR, BertScore, and Coverage for generation accuracy and semantic fidelity. - LLM-Based Metrics: Recent trends show a rise in LLM-as-judge approaches (e.g., RAGAS, Databricks Eval), semantic perplexity, key point recall, FactScore, and representation-based methods like GPTScore and ARES. These enable nuanced, context-aware evaluation that better aligns with real-world user expectations. 4. Safety, Robustness, and Efficiency The survey highlights specialized benchmarks and metrics for: - Safety: Evaluating robustness to adversarial attacks (e.g., knowledge poisoning, retrieval hijacking), factual consistency, privacy leakage, and fairness. - Efficiency: Measuring latency (time to first token, total response time), resource utilization, and cost-effectiveness-crucial for scalable deployment.

Today, there are a variety of commercial LLMs that are available to users at relatively low cost. One of the questions that come to mind for different usecases is what API is best fit for the problem. Currently, evaluating LLMs involves assessing their performance using either automated tests or human evaluations, but both methods are quite general and don't consider the specific skills needed for each instruction given by users. This research paper discusses a new method called FLASK (Fine-grained Language Model Evaluation based on Alignment Skill Sets) to evaluate different LLMs and their outputs. The researchers defined 12 fine-grained skills that LLMs need to follow open-ended instructions effectively. They created an evaluation set by assigning a set of skills for each instruction and also annotated the target domains and difficulty levels for each case. This approach allows a comprehensive analysis of a model's performance based on specific skills, domains, and difficulty levels. Using FLASK, the researchers compared various LLMs, both open-source and proprietary, and found highly correlated results between the model-based and human-based evaluations. FLASK helps developers better understand a model's strengths and weaknesses by analyzing the factors that contribute to their proficiency in different skills. Link to paper: https://lnkd.in/e6_r8SxE Link to github: https://lnkd.in/eV5bQFs9

Don't just blindly use LLMs, evaluate them to see if they fit into your criteria. Not all LLMs are created equal. Here’s how to measure whether they’re right for your use case👇 Evaluating LLMs is critical to assess their performance, reliability, and suitability for specific tasks. Without evaluation, it would be impossible to determine whether a model generates coherent, relevant, or factually correct outputs, particularly in applications like translation, summarization, or question-answering. Evaluation ensures models align with human expectations, avoid biases, and improve iteratively. Different metrics cater to distinct aspects of model performance:  Perplexity quantifies how well a model predicts a sequence (lower scores indicate better familiarity with the data), making it useful for gauging fluency. ROUGE-1 measures unigram (single-word) overlap between model outputs and references, ideal for tasks like summarization where content overlap matters. BLEU focuses on n-gram precision (e.g., exact phrase matches), commonly used in machine translation to assess accuracy. METEOR extends this by incorporating synonyms, paraphrases, and stemming, offering a more flexible semantic evaluation. Exact Match (EM) is the strictest metric, requiring verbatim alignment with the reference, often used in closed-domain tasks like factual QA where precision is paramount. Each metric reflects a trade-off: EM prioritizes literal correctness, while ROUGE and BLEU balance precision with recall. METEOR and Perplexity accommodate linguistic diversity, rewarding semantic coherence over exact replication. Choosing the right metric depends on the task—e.g., EM for factual accuracy in trivia, ROUGE for summarization breadth, and Perplexity for generative fluency. Collectively, these metrics provide a multifaceted view of LLM capabilities, enabling developers to refine models, mitigate errors, and align outputs with user needs. The table’s examples, such as EM scoring 0 for paraphrased answers, highlight how minor phrasing changes impact scores, underscoring the importance of context-aware metric selection. Know more about how to evaluate LLMs: https://lnkd.in/gfPBxrWc Here is my complete in-depth guide on evaluating LLMs: https://lnkd.in/gjWt9jRu Follow me on my YouTube channel so you don't miss any AI topic: https://lnkd.in/gMCpfMKh

As the AI landscape evolves, so does the challenge of effectively evaluating Large Language Models (LLMs). I've been exploring various frameworks, metrics, and approaches that span from statistical to model-based evaluations. Here's a categorical overview: 🛠️ 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀: 1. Cloud Provider Platforms (e.g., AWS Bedrock, Azure AI Studio, Vertex AI Studio) 2. LLM-specific Tools (e.g., DeepEval, LangSmith, Helm, Weights & Biases, TruLens, Parea AI, Prompt Flow, EleutherAI, Deepchecks, MLflow LLM Evaluation, Evidently AI, OpenAI Evals, Hugging Face Evaluate) 3. Benchmarking Tools (e.g., BIG-bench, (Super)GLUE, MMLU, HumanEval) 📈 𝗞𝗲𝘆 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: 1. Text Generation & Translation (e.g., BLEU, ROUGE, BERTScore, METEOR, MoverScore, BLEURT) 2. LLM-specific (e.g., GPTScore, SelfCheckGPT, GEval, EvalGen) 3. Question-Answering (e.g., QAG Score, SQuAD2.0) 4. Natural Language Inference (e.g., MENLI, AUC-ROC, MCC, Precision-Recall AUC, Confusion Matrix, Cohen's Kappa, Cross-entropy Loss) 5. Sentiment Analysis (e.g., Precision, Recall, F-measure, Accuracy) 6. Named Entity Recognition (e.g., F1 score, F-beta score) 7. Contextual Word Embedding & Similarity (e.g., Cosine similarity, (Damerau-)Levenshtein Distance, Euclidean distance, Hamming distance, Jaccard similarity, Jaro(-Winkler) similarity, N-gram similarity, Overlap similarity, Smith-Waterman similarity, Sørensen-Dice similarity, Tversky similarity) IMO, these "objective" metrics should be balanced with human evaluation for a comprehensive assessment, which would include the subjective eye-test for relevance, fluency, coherence, diversity, and simply someone "trying to break it." 🤔 What are your thoughts on LLM evaluation? Any frameworks or metrics you'd add to this list? Would you like me to explain any changes or provide further suggestions? #AIEvaluation #LLM #MachineLearning #DataScience

A Survey on LLM as a Judge: Quick Tips I always like to walk away from something with turn-key, actionable advice I can use right away, heres what I learned from this: - Use a different model for the Judge than your generator (Claude vs GPT) - Don't use absolute scoring alone (45/100) - Pairwise comparisons win. Ask the Judge to compare two outputs together instead of separately - Don't use complex scoring systems without clear criteria (I learned this the hard way) - Majority voting works great (3 specialized LLM Judges agree on final eval) - Do multiple runs with the LLM Judge (think consistency) - Measure LLM Judge efficacy against human domain experts - If LLM Judge eval is too complex, break up into smaller evals (think of using one prompt to review PhD dissertation vs checking if it contains a certain sentence) - LLM Judge Prompt Design: - use structured outputs - use examples of good/bad evals in the prompt (few-shot applies here too) - Set temperature to 0 Let me know if you have any experience with these techniques or have come across something better yourself. I am waiting to see an eval with 9 LLM Judges for each model provider (Gemini, GPT, Claude, Llama, Mistral, etc.) Paper Here: https://lnkd.in/eB5hdyyB

Continuing my evaluation rants :) The best evaluations are going to take advantage of a mix of these three methods: 1. Computation-Based (e.g., ROUGE, Embedding Similarity) These are fast, scalable, and cheap. Good for syntactic/semantic similarity checks, monitoring tuning progress, evaluating specific components (like function call params). However, this method needs reference data, often correlates poorly with nuanced human judgment, and is sensitive to reference choice & text preprocessing mechanisms. 2. Human Evaluation Known as the "gold standard" for quality because it captures nuance that computational methods miss. However, this method is expensive, slow, requires careful setup (training, calibration, inter-rater reliability), and expertise matters. Best used for final checks or calibrating AutoRaters. 3. AutoRaters (LLM-as-Judge) AutoRaters are scalable, faster/cheaper than humans, can provide rationale, and support complex criteria & reference-free eval. Highly customizable via prompting/tuning. Just like any LLM though, these can have biases (position, length, self-preference), require careful prompting, and meta-evaluation is a MUST to ensure quality. We'll talk more about meta-evaluation soon. I've seen a lot of people using LLM-as-judge without thinking about meta-eval. #evaluation #agents #llms #autorater #llmsasjudge