How to Prevent Large Language Model Performance Degradation

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

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!

💡 RAG and fine-tuning are often viewed as mutually exclusive choices, but a combined approach can often benefit many applications! For instance, this paper introduces a fine-tuning method using a dataset that focuses on numerical key-value retrieval tasks. The results show that fine-tuning large language models on this dataset significantly improves their ability to find information and make decisions in longer contexts. Details: 👉 The paper proposes a novel approach of fine-tuning LLMs using a synthetic dataset designed for numerical key-value retrieval tasks. This dataset aims to address the limitations of LLMs in handling long-context tasks effectively. 👉 Fine-tuning LLMs on the synthetic dataset, including models like GPT-3.5 Turbo and Mistral 7B, significantly enhances their information retrieval and reasoning capabilities in longer-context settings. Results: 👉Analysis shows a notable transfer of skills from synthetic to real task evaluations. For instance, GPT-3.5 Turbo demonstrates a 10.5% improvement on MDQA at position 10. 👉 Fine-tuned models maintain stable performance on general benchmarks like MMLU and HellaSwag, indicating minimal degradation in overall model capabilities. 👉 In contrast to fine-tuning on other baseline long-context augmentation data, which may induce hallucinations and performance drops (e.g., on TriviaQA), the synthetic dataset shows either no degradation or minimal performance impact. A point to note is that the synthetic dataset used in this study does not include factual information, reducing the risk of hallucinations found in previous research. This makes it a safer option for improving LLMs' abilities in retrieval and reasoning. Link: https://lnkd.in/eyJ3B2SP