Self-Questioning Techniques for Large Language Models

Still combating LLM hallucination-holic? 🍷 Researchers at #DeepMind have developed a method to #mitigate #hallucinations in large language models (LLMs), which are prone to generating incoherent or incorrect responses. Their innovative method involves equipping LLMs with the ability to self-evaluate their own responses, assessing the similarity between their generated text and the intended output. By incorporating conformal prediction techniques, the models can then make the crucial decision to abstain from providing an answer when the likelihood of hallucination is high. This approach has demonstrated remarkable results, outperforming baseline methods in maintaining a lower abstention rate on datasets with long responses, while still achieving comparable performance on datasets with shorter answers. In other words, the models are now better equipped to distinguish reliable responses from those that are likely to be inaccurate or misleading. The implications of this breakthrough are far-reaching. As LLMs continue to be integrated into various professional fields, from healthcare and education to finance and beyond, the ability to mitigate hallucinations is paramount. Imagine a world where AI-powered assistants can provide trustworthy information, or where autonomous systems can make decisions without the risk of catastrophic errors. Share your thoughts! https://lnkd.in/gEUqavXY

Excited to announce my new (free!) white paper: “Self-Improving LLM Architectures with Open Source” – the definitive guide to building AI systems that continuously learn and adapt. If you’re curious how Large Language Models can critique, refine, and upgrade themselves in real-time using fully open source tools, this is the resource you’ve been waiting for. I’ve put together a comprehensive deep dive on: Foundation Models (Llama 3, Mistral, Google Gemma, Falcon, MPT, etc.): How to pick the right LLM as your base and unlock reliable instruction-following and reasoning capabilities. Orchestration & Workflow (LangChain, LangGraph, AutoGen): Turn your model into a self-improving machine with step-by-step self-critiques and automated revisions. Knowledge Storage (ChromaDB, Qdrant, Weaviate, Neo4j): Seamlessly integrate vector and graph databases to store semantic memories and advanced knowledge relationships. Self-Critique & Reasoning (Chain-of-Thought, Reflexion, Constitutional AI): Empower LLMs to identify errors, refine outputs, and tackle complex reasoning by exploring multiple solution paths. Evaluation & Feedback (LangSmith Evals, RAGAS, W&B): Monitor and measure performance continuously to guide the next cycle of improvements. ML Algorithms & Fine-Tuning (PPO, DPO, LoRA, QLoRA): Transform feedback into targeted model updates for faster, more efficient improvements—without catastrophic forgetting. Bias Amplification: Discover open source strategies for preventing unwanted biases from creeping in as your model continues to adapt. In this white paper, you’ll learn how to: Architect a complete self-improvement workflow, from data ingestion to iterative fine-tuning. Deploy at scale with optimized serving (vLLM, Triton, TGI) to handle real-world production needs. Maintain alignment with human values and ensure continuous oversight to avoid rogue outputs. Ready to build the next generation of AI? Download the white paper for free and see how these open source frameworks come together to power unstoppable, ever-learning LLMs. Drop a comment below or send me a DM for the link! Let’s shape the future of AI—together. #AI #LLM #OpenSource #SelfImproving #MachineLearning #LangChain #Orchestration #VectorDatabases #GraphDatabases #SelfCritique #BiasMitigation #Innovation #aiagents

Reinforcement learning (RL) is becoming a core strategy for improving how language models reason. Historically, RL in LLMs was used at the final stage of training to align model behavior with human preferences. That helped models sound more helpful or polite, but it did not expand their ability to solve complex problems. RL is now being applied earlier and more deeply, not just to tune outputs, but to help models learn how to think, adapt, and generalize across different kinds of reasoning challenges. Here are 3 papers that stood out. 𝗣𝗿𝗼𝗥𝗟 (𝗡𝗩𝗜𝗗𝗜𝗔) applies RL over longer time horizons using strategies like entropy control, KL regularization, and reference policy resets. A 1.5B model trained with this setup outperforms much larger models on tasks like math, code, logic, and scientific reasoning. What is more interesting is that the model begins to solve problems it had not seen before, suggesting that RL, when structured and sustained, can unlock new reasoning capabilities that pretraining alone does not reach. 𝗥𝗲𝗳𝗹𝗲𝗰𝘁, 𝗥𝗲𝘁𝗿𝘆, 𝗥𝗲𝘄𝗮𝗿𝗱 𝗯𝘆 𝗪𝗿𝗶𝘁𝗲𝗿 𝗜𝗻𝗰. introduces a lightweight self-improvement loop. When the model fails a task, it generates a reflection, retries, and is rewarded only if the new attempt succeeds. Over time, the model learns to write better reflections and improves even on first-try accuracy. Because it relies only on a binary success signal and needs no human-labeled data, it provides a scalable way for models to self-correct. 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗣𝗿𝗲-𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗮𝗻𝗱 𝗣𝗲𝗸𝗶𝗻𝗴 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 proposes a new way to pretrain models. Rather than predicting the next token as passive pattern completion, each token prediction is treated as a decision with a reward tied to correctness. This turns next-token prediction into a large-scale RL process. It results in better generalization, especially on hard tokens, and shows that reasoning ability can be built into the model from the earliest stages of training without relying on curated prompts or handcrafted rewards. Taken together, these papers show that RL is becoming a mechanism for growing the model’s ability to reflect, generalize, and solve problems it was not explicitly trained on. What they also reveal is that the most meaningful improvements come from training at moments of uncertainty. Instead of compressing more knowledge into a frozen model, we are beginning to train systems that can learn how to improve mid-process and build reasoning as a capability. This changes how we think about scaling. The next generation of progress may not come from larger models, but from models that are better at learning through feedback, self-reflection, and structured trial and error.

🚀 Large language models understand facts. But can they cook up a plan when the recipe is brand‑new? Welcome “Analogy‑Augmented Generation (AAG)”: 👉Retrieval is good – retrieval plus analogy is better. AAG pairs a procedural memory of past examples with an “analogy engine” that rewrites the user query into 4 focused sub‑questions, grabs similar procedures, then lets the LLM remix them. On an unseen LangChain dataset (LCStep), AAG beat classic RAG, few‑shot and zero‑shot prompts in 70‑98 % of pairwise comparisons, even trouncing a ReAct agent 98 % of the time. 👉Self‑critique closes the loop. After drafting an answer, the same model plays critic for up to three cycles, suggesting edits and re‑writing itself. This iterative pass lifts quality another 7‑10 %, showing that “judge‑and‑fix” can trump ever‑larger prompts. 👉 It scales from code to cooking — and maths. Besides LCStep (276 LangChain tutorials), the team tests on 10 k RecipeNLG meals and 270 CHAMP math problems. Despite very different vocabularies, AAG was still preferred in 56% vs 16% of blind human ratings for recipes, and edged out RAG on competition‑level maths. Why it matters 🤔 * 🔧 Domain adaptation: Need instructions for a library released after your model’s cutoff? Just drop its docs into procedural memory. * 🏗️ Autonomous agents: Clearer, step‑level plans mean safer code execution and robotics. * 🏥 Industry verticals:Think troubleshooting guides for new medical devices or compliance workflows that change monthly. 🤚Limitations & open questions AAG stores procedures as linear step chains. Real‑world tasks often branch and loop — the paper authors plan to add explicit dependency graphs next. And five retrieval‑and‑rewrite passes mean ~7× the latency of vanilla RAG. Will the next wave of LLM tooling come from teaching models to reason by analogy rather than just memorize? Full paper link in the comments. #AIResearch #MachineLearning #RetrievalAugmentedGeneration #LangChain #LLM

Researchers at Google DeepMind have developed a groundbreaking approach called SCoRe (Self-Correction via Reinforcement Learning) that enables large language models to effectively correct their own mistakes without any external feedback. Here are the steps on how SCoRe (Self-Correction via Reinforcement Learning) works: 1. Initialization - Start with a pre-trained large language model (LLM). - Prepare a dataset of problems and their solutions. 2. Stage I: Training a Model Initialization - Fine-tune the LLM to produce high-reward revisions on the second attempt. - Constrain the first-attempt responses to be similar to the base model. - This stage helps prevent the model from coupling first and second attempts too closely. 3. Stage II: Multi-Turn Reinforcement Learning - Use the model from Stage I as a starting point. - Train the model to optimize responses at both the first and second attempts. - Apply a reward shaping technique to encourage self-correction behavior. 4. Reward Shaping - Provide a higher reward for responses that correct mistakes from the first attempt to the second. - This biases the model towards learning a self-correction strategy. 5. Iterative Training - Alternate between Stage I and Stage II multiple times. - This process gradually improves the model's self-correction abilities. 6. Evaluation - Test the model on new problems, giving it two attempts to solve each one. - Measure the improvement between the first and second attempts. 7. Inference - When using the final model, allow it to make an initial attempt at solving a problem. - Then prompt it to review and potentially correct its first attempt. 8. Scaling - For better results, generate multiple solutions and use majority voting. - Combine parallel sampling with sequential self-correction for optimal performance.