Ensuring LLM Accuracy in Subjective Question Responses

Explaining the Evaluation method LLM-as-a-Judge (LLMaaJ). Token-based metrics like BLEU or ROUGE are still useful for structured tasks like translation or summarization. But for open-ended answers, RAG copilots, or complex enterprise prompts, they often miss the bigger picture. That’s where LLMaaJ changes the game. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗶𝘁? You use a powerful LLM as an evaluator, not a generator. It’s given: - The original question - The generated answer - And the retrieved context or gold answer 𝗧𝗵𝗲𝗻 𝗶𝘁 𝗮𝘀𝘀𝗲𝘀𝘀𝗲𝘀: ✅ Faithfulness to the source ✅ Factual accuracy ✅ Semantic alignment—even if phrased differently 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: LLMaaJ captures what traditional metrics can’t. It understands paraphrasing. It flags hallucinations. It mirrors human judgment, which is critical when deploying GenAI systems in the enterprise. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗟𝗟𝗠𝗮𝗮𝗝-𝗯𝗮𝘀𝗲𝗱 𝗺𝗲𝘁𝗿𝗶𝗰𝘀: - Answer correctness - Answer faithfulness - Coherence, tone, and even reasoning quality 📌 If you’re building enterprise-grade copilots or RAG workflows, LLMaaJ is how you scale QA beyond manual reviews. To put LLMaaJ into practice, check out EvalAssist; a new tool from IBM Research. It offers a web-based UI to streamline LLM evaluations: - Refine your criteria iteratively using Unitxt - Generate structured evaluations - Export as Jupyter notebooks to scale effortlessly A powerful way to bring LLM-as-a-Judge into your QA stack. - Get Started guide: https://lnkd.in/g4QP3-Ue - Demo Site: https://lnkd.in/gUSrV65s - Github Repo: https://lnkd.in/gPVEQRtv - Whitepapers: https://lnkd.in/gnHi6SeW

Small variations in prompts can lead to very different LLM responses. Research that measures LLM prompt sensitivity uncovers what matters, and the strategies to get the best outcomes. A new framework for prompt sensitivity, ProSA, shows that response robustness increases with factors including higher model confidence, few-shot examples, and larger model size. Some strategies you should consider given these findings: 💡 Understand Prompt Sensitivity and Test Variability: LLMs can produce different responses with minor rephrasings of the same prompt. Testing multiple prompt versions is essential, as even small wording adjustments can significantly impact the outcome. Organizations may benefit from creating a library of proven prompts, noting which styles perform best for different types of queries. 🧩 Integrate Few-Shot Examples for Consistency: Including few-shot examples (demonstrative samples within prompts) enhances the stability of responses, especially in larger models. For complex or high-priority tasks, adding a few-shot structure can reduce prompt sensitivity. Standardizing few-shot examples in key prompts across the organization helps ensure consistent output. 🧠 Match Prompt Style to Task Complexity: Different tasks benefit from different prompt strategies. Knowledge-based tasks like basic Q&A are generally less sensitive to prompt variations than complex, reasoning-heavy tasks, such as coding or creative requests. For these complex tasks, using structured, example-rich prompts can improve response reliability. 📈 Use Decoding Confidence as a Quality Check: High decoding confidence—the model’s level of certainty in its responses—indicates robustness against prompt variations. Organizations can track confidence scores to flag low-confidence responses and identify prompts that might need adjustment, enhancing the overall quality of outputs. 📜 Standardize Prompt Templates for Reliability: Simple, standardized templates reduce prompt sensitivity across users and tasks. For frequent or critical applications, well-designed, straightforward prompt templates minimize variability in responses. Organizations should consider a “best-practices” prompt set that can be shared across teams to ensure reliable outcomes. 🔄 Regularly Review and Optimize Prompts: As LLMs evolve, so may prompt performance. Routine prompt evaluations help organizations adapt to model changes and maintain high-quality, reliable responses over time. Regularly revisiting and refining key prompts ensures they stay aligned with the latest LLM behavior. Link to paper in comments.

LLM pro tip to reduce hallucinations and improve performance: instruct the language model to ask clarifying questions in your prompt. Add a directive like "If any part of the question/task is unclear or lacks sufficient context, ask clarifying questions before providing an answer" to your system prompt. This will: (1) Reduce ambiguity - forcing the model to acknowledge knowledge gaps rather than filling them with hallucinations (2) Improve accuracy - enabling the model to gather necessary details before committing to an answer (3) Enhance interaction - creating a more natural, iterative conversation flow similar to human exchanges This approach was validated in the 2023 CALM paper, which showed that selectively asking clarifying questions for ambiguous inputs increased question-answering accuracy without negatively affecting responses to unambiguous queries https://lnkd.in/gnAhZ5zM

Are your LLM apps still hallucinating? Zep used to as well—a lot. Here’s how we worked to solve Zep's hallucinations. We've spent a lot of cycles diving into why LLMs hallucinate and experimenting with the most effective techniques to prevent it. Some might sound familiar, but it's the combined approach that really moves the needle. First, why do hallucinations happen? A few core reasons: 🔍 LLMs rely on statistical patterns, not true understanding. 🎲 Responses are based on probabilities, not verified facts. 🤔 No innate ability to differentiate truth from plausible fiction. 📚 Training datasets often include biases, outdated info, or errors. Put simply: LLMs predict the next likely word—they don’t actually "understand" or verify what's accurate. When prompted beyond their knowledge, they creatively fill gaps with plausible (but incorrect) info. ⚠️ Funny if you’re casually chatting—problematic if you're building enterprise apps. So, how do you reduce hallucinations effectively? The #1 technique: grounding the LLM in data. - Use Retrieval-Augmented Generation (RAG) to anchor responses in verified data. - Use long-term memory systems like Zep to ensure the model is always grounded in personalization data: user context, preferences, traits etc - Fine-tune models on domain-specific datasets to improve response consistency and style, although fine-tuning alone typically doesn't add substantial new factual knowledge. - Explicit, clear prompting—avoid ambiguity or unnecessary complexity. - Encourage models to self-verify conclusions when accuracy is essential. - Structure complex tasks with chain-of-thought prompting (COT) to improve outputs or force "none"/unknown responses when necessary. - Strategically tweak model parameters (e.g., temperature, top-p) to limit overly creative outputs. - Post-processing verification for mission-critical outputs, for example, matching to known business states. One technique alone rarely solves hallucinations. For maximum ROI, we've found combining RAG with a robust long-term memory solution (like ours at Zep) is the sweet spot. Systems that ground responses in factual, evolving knowledge significantly outperform. Did I miss any good techniques? What are you doing in your apps?

Tackling Hallucination in LLMs: Mitigation & Evaluation Strategies As Large Language Models (LLMs) redefine how we interact with AI, one critical challenge is hallucination—when models generate false or misleading responses. This issue affects the reliability of LLMs, particularly in high-stakes applications like healthcare, legal, and education. To ensure trustworthiness, it’s essential to adopt robust strategies for mitigating and evaluating hallucination. The workflow outlined above presents a structured approach to addressing this challenge: 1️⃣ Hallucination QA Set Generation Starting with a raw corpus, we process knowledge bases and apply weighted sampling to create diverse, high-quality datasets. This includes generating baseline questions, multi-context queries, and complex reasoning tasks, ensuring a comprehensive evaluation framework. Rigorous filtering and quality checks ensure datasets are robust and aligned with real-world complexities. 2️⃣ Hallucination Benchmarking By pre-processing datasets, answers are categorized as correct or hallucinated, providing a benchmark for model performance. This phase involves tools like classification models and text generation to assess reliability under various conditions. 3️⃣ Hallucination Mitigation Strategies In-Context Learning: Enhancing output reliability by incorporating examples directly in the prompt. Retrieval-Augmented Generation: Supplementing model responses with real-time data retrieval. Parameter-Efficient Fine-Tuning: Fine-tuning targeted parts of the model for specific tasks. By implementing these strategies, we can significantly reduce hallucination risks, ensuring LLMs deliver accurate and context-aware responses across diverse applications. 💡 What strategies do you employ to minimize hallucination in AI systems? Let’s discuss and learn together in the comments!

🧠 Using psychology to get more out of small, cheap models? "A bat and a ball cost $1.10. The bat costs $1 more than the ball. How much does the ball cost?" - Most people instinctively say 10 cents. - But with a pause, or a pen and paper, you’ll realize it’s 5 cents. This classic example comes from Daniel Kahneman’s "Thinking, Fast and Slow", which describes our two modes of thinking: System 1: Fast, intuitive, and error-prone System 2: Slow, deliberate, and accurate Turns out, LLMs behave in a surprisingly similar way. Inspired by this, I ran an experiment. I created a bank of 120 reasoning-based True/False questions and tested them on a small 4B model (Gemma 3). With a plain prompt ("Answer True or False"), accuracy was 54% — basically a coin toss. But with a subtle nudge ("Think through the steps and double-check your work"), accuracy rose to 71%. Even with more token usage, it can beat larger models on price-perf. Same model. Same questions. Just a different mental framing. This aligns with insights from the meta-prompting paper (https://lnkd.in/g6zVJU5M), which shows how prompting can simulate System 2 thinking in LLMs. It’s intuitive once you see it, but still… very powerful!

Stop asking LLMs to "check for accuracy." >> Make the models work instead. There are ways to improve the accuracy of chatbot answers. Instead of accepting it's initial output, you can force it to reevaluate its work in meaningful ways. You can get to truth by forcing your LLM to transform, not give a wink and a nod to the answer it already generated. Have it reprocess your draft. And provide evidence. Some sweet tactics you can try: 🔹Rebuild: "Recreate this answer from fresh sources only. Return what changed." 🔹Cite everything: "Attach a source and short quote after every claim." 🔹Diff it: "Compare the rebuild to the original. List conflicts and missing pieces." 🔹Justify: "For each bullet, add ‘Because: [evidence] >> [claim]’." 🔹Expand: "Add 1 example, 1 edge case, 1 failure mode for each item." 🔹Pros and cons: "Give tradeoffs for each. Note who benefits and who loses." 🔹Disprove: "Try to falsify each point. Provide counterexamples." 🔹Contradiction scan: "Find claims that conflict with each other." 🔹Freshness check: "Verify dates, versions, and timelines. Flag anything stale." 🔹Triangulate: "Give 3 independent passes, then merge them with a rationale." 🔹Referee mode: "Score another LLM’s output with a rubric and evidence." Try using multiple LLMs to cross-check each other. Bottom line: don’t ask "Accurate?" Make the model to work.

Throw out the old #RAG approaches; use Corrective RAG instead! Corrective RAG introduces the additional layer of checking and correcting retrieved documents, ensuring more accurate and relevant information before generating a final response. This approach enhances the reliability of the generated answers by refining or correcting the retrieved context dynamically. The key idea here is to retrieve document chunks from the vector database as usual and then use an LLM to check if each retrieved document chunk is relevant to the input question. The process roughly goes as below, ⮕ Step 1: Retrieve context documents from vector database from the input query. ⮕ Step 2: Use an LLM to check if retrieved documents are relevant to the input question. ⮕ Step 3: If all documents are relevant (Correct), no specific action is needed. ⮕ Step 4: If some or all documents are not relevant (Ambiguous or Incorrect), rephrase the query and search the web to get relevant context information. ⮕ Step 5: Send rephrased query and context documents or information to the LLM for response generation. I have made a complete video on corrective RAG using LangGraph: https://lnkd.in/gKaEjEvk Know more in-depth about corrective RAG in this paper: https://lnkd.in/g8FkrMzS

Whether you're using RAG or AI agents -- you want to make sure they respond with "I don't know" instead of answering incorrectly. Cleanlab has come up with "TLM" which does this pretty well -- - The Trustworthy Language Model (TLM) uses a scoring system to evaluate LLM responses based on their trustworthiness. It flags answers that may be incorrect, letting you know when to ignore them. - TLM works in real-time, assessing the responses of models like GPT-4o. When the trustworthiness score drops below a threshold of 0.25, TLM overrides the response with a standard "I don’t know" answer to prevent misinformation. - The system doesn’t just stop at filtering. TLM also improves responses automatically, making the output less error-prone without modifying the LLM or its prompts, which saves time in the revision process. - For high-stakes applications, a stricter threshold of 0.8 can be set, which drastically drops incorrect responses by over 84%. But this has to be balanced, because a higher threshold means that some correct responses will also be filtered. - This approach allows for a more reliable interaction with LLMs, especially when dealing with fact-based queries, which helps maintain user trust and enhances the overall quality of responses. Link to the article: https://lnkd.in/gdM5BE9M #AI #LLMs #RAG

Evaluating LLMs accurately/reliably is difficult, but we can usually automate the evaluation process with another (more powerful) LLM... Automatic metrics: Previously, generative text models were most commonly evaluated using automatic metrics like ROUGE and BLEU, which simply compare how well a model’s output matches a human-written target resopnse. In particular, BLEU score was commonly used to evaluatate machine translation models, while ROUGE was most often used for evaluating summarization models. Serious limitations: With modern LLMs, researchers began to notice that automatic metrics did a poor job of comprehensively capturing the quality of an LLM’s generations. Oftentimes, ROUGE scores were poorly correlated with human preferences—higher scores don’t seem to indicate a better generation/summary [1]. This problem is largely due to the open-ended nature of most tasks solved with LLMs. There can be many good responses to a prompt. LLM-as-a-judge [2] leverages a powerful LLM (e.g., GPT-4) to evaluate the quality of an LLM’s output. To evaluate an LLM with another LLM, there are three basic structures or strategies that we can employ: (1) Pairwise comparison: The LLM is shown a question with two responses and asked to choose the better response (or declare a tie). This approach was heavily utilized by models like Alpaca/Vicuna to evaluate model performance relative to proprietary LLMS like ChatGPT. (2) Single-answer grading: The LLM is shown a response with a single answer and asked to provide a score for the answer. This strategy is less reliable than pairwise comparison due to the need to assign an absolute score to the response. However, authors in [2] observe that GPT-4 can nonetheless assign relatively reliable/meaningful scores to responses. (3) Reference-guided grading: The LLM is provided a reference answer to the problem when being asked to grade a response. This strategy is useful for complex problems (e.g., reasoning or math) in which even GPT-4 may struggle with generating a correct answer. In these cases, having direct access to a correct response may aid the grading process. “LLM-as-a-judge offers two key benefits: scalability and explainability. It reduces the need for human involvement, enabling scalable benchmarks and fast iterations.” - from [2] Using MT-bench, authors in [2] evaluate the level of agreement between LLM-as-a-judge and humans (58 expert human annotators), where we see that there is a high level of agreement between these strategies. Such a finding caused this evaluation strategy to become incredibly popular for LLMs—it is currently the most widely-used and effective alternative to human evaluation. However, LLM-as-a-judge does suffer from notable limititations (e.g., position bias, verbosity bias, self-enhancement bias, etc.) that should be considered when interpretting data.