Ensuring Reliable Inference in Large Language Models

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.

Large Language Models face a critical challenge: how to enhance factual accuracy without sacrificing either inference speed or general capabilities. Current solutions fall short-RAG systems suffer from high latency and shallow integration, while fine-tuning methods like LoRA risk catastrophic forgetting. Researchers from Shanghai Jiao Tong University and Shanghai AI Laboratory Lab propose MLP Memory, a parametric memory module that learns retrieval patterns during pretraining without requiring explicit document access at inference time. How it works: The system trains a lightweight MLP network to mimic the behavior of k-nearest neighbor retrieval across an entire pretraining corpus. During training, the MLP learns to map hidden representations from a frozen language model to probability distributions that match what a kNN retriever would produce-essentially compressing 40TB of datastore information into a 4GB parametric module. The architecture uses stacked feed-forward layers without token-mixing operations, leveraging recent findings that FFN layers function as key-value memories within transformers. The training objective combines KL divergence loss to match retrieval distributions with cross-entropy loss to maintain grounding in actual next-token predictions. At inference, the MLP Memory processes hidden states from approximately 70% network depth (not the final layer, as conventional kNN-LM does) and interpolates its output with the base model's predictions through simple probability mixing. Performance gains: On question-answering benchmarks, MLP Memory achieves 12.3% relative improvement over base models, outperforming both RAG and continued pretraining. On HaluEval, it reduces hallucinations by up to 10 points. Critically, it delivers 2.5x faster time-to-first-token than RAG and maintains constant inference speed regardless of corpus size-a fundamental advantage over retrieval-based methods whose latency scales with datastore size. The approach demonstrates that learning retrieval patterns parametrically bridges the efficiency-effectiveness gap, offering a practical alternative that combines the knowledge access benefits of RAG with the speed of purely parametric methods.

Achieving Near-Zero Hallucination in AI: A Practical Approach to Trustworthy Language Models 🎯 Excited to share our latest work on making AI systems more reliable and factual! We've developed a framework that achieves 0% hallucination rate on our benchmark, a critical step toward trustworthy AI deployment. The Challenge: Large language models often generate plausible-sounding but incorrect information, making them risky for production use where accuracy matters. Our Solution: We trained models to: ✅ Provide evidence-grounded answers with explicit citations ✅ Express calibrated confidence levels (0-1 scale) ✅ Know when to say "I don't know" when evidence is insufficient Key Results: 📈 54% improvement in accuracy (80.5% exact match vs 52.3% baseline) 🎯 0% hallucination rate through calibrated refusal 🔍 82% citation correctness (models show their work) 🛡️ 24% refusal rate when evidence is lacking (better safe than sorry!) What Makes This Different: Instead of hiding uncertainty in fluent prose, we enforce structured JSON outputs that create accountability. When the model isn't sure, it explicitly refuses rather than making things up. Interesting Finding: Under noisy/cluttered contexts, the model maintains answer quality but sometimes cites the wrong sources, identifying the next challenge to solve! We've open-sourced everything: https://lnkd.in/ejUtBYJX 1,198 preference pairs for reproduction https://lnkd.in/ewvwDJ2G DeBERTa reward model (97.4% accuracy) Complete evaluation framework Technical report: https://lnkd.in/eEDVgfJb This work represents a practical step toward AI systems that are not just powerful, but genuinely trustworthy for real-world applications where factual accuracy is non-negotiable. What strategies is your team using to improve AI reliability? Would love to hear about different approaches to this critical challenge! #AI #MachineLearning #ResponsibleAI #NLP #TechInnovation #OpenSource

Banger paper from Google Research! "SLED: Self Logits Evolution Decoding," introduces a novel decoding framework to address factuality directly at inference time. While Large Language Models (LLMs) are incredibly powerful, their factual unreliability remains a major problem for enterprise adoption. How can we improve truthfulness without costly retraining or external dependencies? SLED leverages the latent knowledge already embedded within an LLM by contrasting the output logits from the final layer with those from earlier layers. This contrastive signal then guides a "self-evolution" process, steering the model's output towards greater factual accuracy. Key advantages of SLED: ✅ No Fine-Tuning Required: It's a plug-and-play decoding strategy that works with existing models. ✅ No External Knowledge Base: Unlike RAG, it doesn't rely on external databases, simplifying deployment. ✅ High Performance: extensive experiments on models like Gemma, Mixtral, and Qwen (from 1B to 45B parameters) show consistent improvements in factuality. ✅ Minimal Overhead: SLED is computationally efficient, adding negligible latency. Amazing work! Link in the first comment to the full paper ⬇️

Building Trust in AI: Addressing the Challenge of LLMs Hallucinations As the use of Large Language Models (LLMs) grows, so does a critical challenge: hallucinations, where the model generates unreliable or incorrect outputs. This research paper explores innovative methods to detect and mitigate these hallucinations, offering valuable insights for those deploying LLMs in practical settings. 🔹 Research Focus The paper proposes a framework for assessing LLM output reliability across contexts. It benchmarks state-of-the-art scoring methods for detecting hallucinations and introduces a multi-scoring approach for improved performance. 🔹 Single-generation Scoring This method involves evaluating the reliability of a single generated response. Techniques such as inverse perplexity measure the model's confidence in its output, while the P(True) method prompts the model to verify the correctness of its response. These methods are essential for assessing the quality of outputs when only one response is available. 🔹 Multi-generation Scoring These methods, like SelfCheckGPT, assess the consistency of multiple outputs generated from the same input. By comparing these outputs, the method can identify discrepancies that indicate potential hallucinations. This approach is particularly useful when a model can produce various correct responses, allowing for a more nuanced understanding of the output's reliability. 🔹 Calibration Techniques Calibration ensures that scores accurately indicate the likelihood of hallucinations in outputs. This allows organizations to set thresholds that balance false positives and negatives, leading to more confident decision-making. It addresses the inherent uncertainty in detecting hallucinations, even among human evaluators. 🔹 Cost-effective Multi-Scoring This method optimizes the use of multiple scoring techniques while managing computational costs. By selecting the best-performing scores within a fixed budget, this approach makes the deployment of advanced hallucination detection methods feasible in real-world applications, where resource constraints are often a concern. 📌 Key Insights The findings show that detecting hallucinations in LLMs is complex, with no universal method. The proposed multi-scoring framework, with proper calibration, offers a reliable solution for accurate LLM outputs. This work is crucial for businesses aiming to use LLMs responsibly and reduce misinformation risks, with practical applications in customer service, content creation, and data analysis. 👉 What are your thoughts on the future of LLMs in critical applications, considering these advancements in hallucination detection? How do you plan to implement these strategies in your organization? Share your insights or questions below! 👈 #LLM #LLMs #NLP #NaturalLanguageProcessing #AI #ArtificialIntelligence #MachineLearning #DeepLearning #DataScience #FutureOfWork #Automation #TechInnovation #Innovation

Reducing Hallucinations in Large Language Models with Knowledge-Aware Inference ⚫ As large language models (LLMs) continue to demonstrate impressive capabilities, one persistent challenge remains — the phenomenon of “hallucinations”. Hallucinations occur when LLMs generate outputs that, while coherent and fluent, contain factual inaccuracies or nonsensical information not grounded in reality. This undermines the reliability and trustworthiness of these powerful AI systems. However, recent research has explored a promising solution: integrating LLMs with knowledge graphs (KGs) during the inference process. This “knowledge-aware inference” approach aims to leverage the structured knowledge encoded in KGs to guide and constrain the language model, reducing the occurrence of hallucinations and improving the factual accuracy of generated outputs. 1. KG-Augmented Retrieval The first category of knowledge-aware inference techniques involves retrieving relevant facts or triples from KGs to augment the input provided to the LLM. This additional context, drawn from the structured knowledge encoded in the KG, can help fill knowledge gaps and guide the LLM towards generating more accurate and factually consistent outputs. 2. KG-Augmented Reasoning Going beyond simple retrieval, the second category of knowledge-aware inference techniques involves using KGs to guide the LLM’s reasoning process itself. These methods often decompose complex queries into manageable sub-queries and leverage the KG’s structure to provide reasoning paths or intermediate steps for the LLM to follow. 3. Knowledge-Controlled Generation The third category of knowledge-aware inference techniques involves generating knowledge using the LLM and then using probing or API calls to validate and refine the generated information against the KG. This approach aims to leverage the LLM’s generation capabilities while imposing constraints and fact-checking mechanisms to ensure the outputs align with the structured knowledge in the KG. Bidirectional Reasoning Process In addition to these three categories of knowledge-aware inference techniques, recent research has also explored the concept of a bidirectional reasoning process between LLMs and KGs. This approach involves not only using KGs to guide and constrain the LLM’s outputs but also leveraging the LLM’s generation capabilities to refine and expand the KG itself. By combining the strengths of LLM generation and knowledge graph validation, this bidirectional reasoning process enables the creation of intelligent data flywheels that continuously improve the quality and consistency of structured knowledge while also facilitating the detection of hallucinations in LLM-generated outputs. https://lnkd.in/e6gjeQzN

🎬 Watching PK (infinity+1 times) got me thinking — if we can trace back where PK (the alien) learned from, can we do the same for LLMs? 🤖 Can we trace the exact data shaping an LLM’s beliefs? ⚠️ More importantly, can we identify which 𝗯𝗲𝗹𝗶𝗲𝗳 𝗰𝗮𝘂𝘀𝗲𝘀 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝗱𝗿𝗶𝗳𝘁 — when a model’s responses start diverging from safe, intended behavior? This is the heart of 𝗧𝗥𝗔𝗖𝗘𝗔𝗟𝗜𝗚𝗡 — trace LLM outputs back to their training-time belief origins, unlocking explainability, accountability, and stronger AI alignment. 🚨 𝗧𝗥𝗔𝗖𝗘𝗔𝗟𝗜𝗚𝗡 - 𝗧𝗿𝗮𝗰𝗶𝗻𝗴 𝘁𝗵𝗲 𝗗𝗿𝗶𝗳𝘁: 𝗔𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗻𝗴 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝗙𝗮𝗶𝗹𝘂𝗿𝗲𝘀 𝘁𝗼 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴-𝗧𝗶𝗺𝗲 𝗕𝗲𝗹𝗶𝗲𝗳 𝗦𝗼𝘂𝗿𝗰𝗲𝘀 𝗶𝗻 𝗟𝗟𝗠𝘀 🚨 ------------------------------------------------------------------------------- Modern Large Language Models (LLMs) like LLaMA and GPT exhibit alignment drift — where models, despite fine-tuning, produce unsafe or policy-violating outputs under adversarial prompts, paraphrases, or decoding variations. Why does this happen? 🔍 Our latest research introduces 𝗧𝗥𝗔𝗖𝗘𝗔𝗟𝗜𝗚𝗡, a first-of-its-kind framework that goes beyond surface behaviors (like refusals or toxicity scores) to trace why models fail, by identifying the 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴-𝘁𝗶𝗺𝗲 𝗯𝗲𝗹𝗶𝗲𝗳 𝘀𝗼𝘂𝗿𝗰𝗲𝘀 behind misaligned completions. ✨ 𝗞𝗲𝘆 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻𝘀: -------------------- 🔹 𝗧𝗥𝗔𝗖𝗘𝗜𝗡𝗗𝗘𝗫: A suffix-array based high-resolution memory tracer linking unsafe outputs back to exact training data spans — revealing latent memorized beliefs causing drift. 🔹 𝗕𝗲𝗹𝗶𝗲𝗳 𝗖𝗼𝗻𝗳𝗹𝗶𝗰𝘁 𝗜𝗻𝗱𝗲𝘅 (𝗕𝗖𝗜): A rarity-aware, information-theoretic metric quantifying how risky and specific a recalled span is — allowing us to detect high-risk beliefs during generation. 🔹 𝗧𝗵𝗿𝗲𝗲-𝗹𝗮𝘆𝗲𝗿𝗲𝗱 𝗱𝗲𝗳𝗲𝗻𝘀𝗲𝘀: --------------------------- 1️⃣ 𝗧𝗥𝗔𝗖𝗘𝗦𝗛𝗜𝗘𝗟𝗗 — inference-time filter that refuses outputs grounded in high-BCI spans. 2️⃣ 𝗖𝗕𝗗 𝗟𝗼𝘀𝘀 — contrastive fine-tuning loss that penalizes risky belief fragments. 3️⃣ 𝗣𝗿𝗼𝘃-𝗗𝗲𝗰𝗼𝗱𝗲 — decoding-time veto mechanism suppressing unsafe continuations. 𝙒𝙝𝙮 𝙞𝙩 𝙢𝙖𝙩𝙩𝙚𝙧𝙨: --------------- 🛡️ Moves AI safety from black-box behavior monitoring to transparent, provenance-grounded belief auditing. 🧠 Enables interpretable, traceable interventions during training and inference. ⚙️ Scales efficiently with suffix-array indexing and principled risk metrics. 📊 Provides the first scalable toolkit to diagnose and mitigate latent sources of unsafe behavior. 𝗧𝗥𝗔𝗖𝗘𝗔𝗟𝗜𝗚𝗡 lays the foundational stones for epistemic alignment auditing—helping us understand not just what models say, but why they say it. cc - Suranjana Trivedy, Aman Chadha, Vinija Jain Pragya Lab, Department of CSIS BITS Pilani Goa Campus, APPCAIR #AIResearch #AIsafety #LLMAlignment #AdversarialRobustness #TRACEALIGN #MachineLearning #ResponsibleAI #Transparency #ExplainableAI

A challenge to the security and trustworthiness of large language models (LLMs) is the common practice of exposing the model to large amounts of untrusted data (especially during pretraining), which may be at risk of being modified (i.e. poisoned) by an attacker. These poisoning attacks include backdoor attacks, which aim to produce undesirable model behavior only in the presence of a particular trigger. For example, an attacker could inject a backdoor where a trigger phrase causes a model to comply with harmful requests that would have otherwise been refused; or aim to make the model produce gibberish text in the presence of a trigger phrase. As LLMs become more capable and integrated into society, these attacks may become more concerning if successful. Recent research from Anthropic and the UK AI Security Institute shows that inserting as few as 250 malicious documents into training data can create backdoors or cause gibberish outputs when triggered by specific phrases. See https://lnkd.in/eHGuRmHP. Here’s a list of best practices to help prevent or mitigate model poisoning: 1. Sanitize Training Data Scrub datasets for anomalies, adversarial patterns, or suspicious repetitions. Use data provenance tools to trace sources and flag untrusted inputs. 2. Use Curated and Trusted Data Sources Avoid scraping indiscriminately from the open web. Prefer vetted corpora, licensed datasets, or internal data with known lineage. 3. Apply Adversarial Testing Simulate poisoning attacks during model development. Use red teaming to test how models respond to trigger phrases or manipulated inputs. 4. Monitor for Backdoor Behavior Continuously test models for unexpected outputs tied to specific phrases or patterns. Use behavioral fingerprinting to detect latent vulnerabilities. 5. Restrict Fine-Tuning Access Limit who can fine-tune models and enforce role-based access controls. Log and audit all fine-tuning activity. 6. Leverage Differential Privacy Add noise to training data to reduce the impact of any single poisoned input. This can help prevent memorization of malicious content. 7. Use Ensemble or Cross-Validated Models Combine outputs from multiple models trained on different data slices. This reduces the risk that one poisoned model dominates predictions. 8. Retrain Periodically with Fresh Data Don’t rely indefinitely on static models. Regular retraining allows for data hygiene updates and removal of compromised inputs. 9. Deploy Real-Time Anomaly Detection Monitor model outputs for signs of degradation, bias, or gibberish. Flag and quarantine suspicious responses for review. 10. Align with AI Security Frameworks Follow guidance from OWASP GenAI, NIST AI RMF, and similar standards. Document your defenses and response plans for audits and incident handling. Stay safe out there!

Day 19/30 of SLMs/LLMs: Mixture-of-Experts, Efficient Transformers, and Sparse Models As language models grow larger, two challenges dominate: cost and efficiency. Bigger models bring higher accuracy but also higher latency, energy use, and deployment complexity. The next phase of progress is about making models faster, lighter, and more intelligent per parameter. A leading direction is the Mixture-of-Experts (MoE) architecture. Instead of activating every parameter for each input, MoE models route tokens through a few specialized “experts.” Google’s Switch Transformer and DeepMind’s GLaM demonstrated that activating only 5 to 10 percent of weights can achieve the same accuracy as dense models at a fraction of the compute. Open models like Mixtral 8x7B extend this idea by using eight experts per layer but activating only two for each forward pass. The result is performance similar to a 70B model while operating at roughly 12B compute cost. Another active area of innovation is Efficient Transformers. Traditional attention scales quadratically with sequence length, which limits how much context a model can process. New variants such as FlashAttention, Longformer, Performer, and Mamba improve memory efficiency and speed. FlashAttention in particular accelerates attention calculations by performing them directly in GPU memory, achieving two to four times faster throughput on long sequences. Sparse Models also contribute to efficiency by reducing the number of active parameters during training or inference. Structured sparsity, combined with quantization and pruning, allows models to run on smaller devices without a major loss in quality. Advances in sparsity-aware optimizers now make it possible to deploy billion-parameter models on standard hardware with near state-of-the-art accuracy. These techniques share a single goal: scaling intelligence without scaling cost. The focus is shifting from building larger networks to building smarter ones. A 7B model that uses retrieval, sparse activation, and efficient attention can outperform a much larger dense model in both speed and reliability.

Achieving Reproducibility in LLM Inference: A Breakthrough by Thinking Machines Lab Reproducibility is a cornerstone of scientific progress, yet large language models (LLMs) often yield inconsistent results—even with deterministic settings. Thinking Machines Lab, in their their first technical blog , delves into the root causes of this nondeterminism and presents a compelling solution. Reproducibility is also important for reinforcement learning, let us take an example, RL is the process of rewarding AI models for correct answers, but if the answers are all slightly different, then the data gets a bit noisy. Creating more consistent AI model responses could make the whole RL process smoother. see image for mathematical notation for the same. In higher level , The answer lies deep inside in the GPU : kernels A GPU kernel is a function or program that runs on the GPU (Graphics Processing Unit) and is executed in parallel across many threads. Key Insights: 1. Beyond Floating-Point Non-Associativity: While it's known that floating-point arithmetic can lead to non-associativity, the paper reveals that this alone doesn't account for the variability observed in LLM outputs. i.e: for floating point numbers ( x + y) + z != x + (y + z) >>> (0.1 + 1e20) - 1e20 0.0 >>> 0.1 + (1e20 - 1e20) 0.1 So, Even small differences like these, when multiplied across thousands of operations in LLMs, lead to inconsistent outputs. 2. The Role of Batch Invariance: A significant finding is that the lack of batch invariance in kernel implementations contributes to nondeterminism. Variations in batch sizes can lead to different execution paths, resulting in inconsistent outputs. The authors ensure batch-invariant operations so that LLM outputs remain consistent regardless of how inputs are grouped. They do this by: 1. Standardizing kernel computations to behave identically across batches. 2. Managing floating-point operations to reduce subtle calculation differences. 3. Enforcing deterministic execution paths for consistent results. Outcome: LLM inference becomes reproducible, reliable, and consistent across runs. Certainly this research not only addresses a critical challenge in AI but also sets the stage for more reliable and transparent AI systems. I’d say investors truly recognize Mira Muraty’s mettle—without even having a product, they bet billions of dollars on her and her small team’s vision. Full paper: https://lnkd.in/eD7taVKt #AI #MachineLearning #Reproducibility #LLM #ThinkingMachinesLab