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标签： Large Language Models

  • Maintaining Coherence in Large Language Models: A Control-Theoretic Approach

    Maintaining Coherence in Large Language Models: A Control-Theoretic Approach

    I’ve been reading about how large language models can lose coherence over long interactions. It’s a problem that doesn’t seem to be solved by just scaling up the model size or context length. Instead, it’s more about control. Most approaches to using these models focus on the input or data level, but what if we treated the interaction as a dynamic system that needs to be regulated over time?

    This is where a control-theoretic approach comes in. By modeling the interaction as a discrete-time dynamical system, we can treat the model as a stochastic inference substrate and use a lightweight external control layer to inject corrective context when coherence degrades. This approach doesn’t require modifying the model’s weights or fine-tuning, and it’s model-agnostic.

    The idea is to maintain a reference state – like the intent and constraints – and regulate the interaction using feedback. When coherence degrades, corrective input is applied, and when stability is achieved, intervention diminishes. In practice, this can produce sustained semantic coherence over hundreds to thousands of turns, reduce drift without increasing prompt complexity, and enable faster recovery after adversarial or noisy inputs.

    I think this is a fascinating area of research, especially for those working in control theory, dynamical systems, cognitive architectures, or long-horizon AI interaction. The key insight here is that intelligence in long-horizon interaction emerges from regulation, not from raw model capacity. By focusing on external governance and control, we might be able to create more coherent and stable interactions with large language models.

  • Exploring the Intersection of Knowledge Graphs and Cosine Similarity

    Hey, have you ever wondered how we can make machines understand the relationships between different pieces of information? This is where knowledge graphs come in – a way to represent knowledge as a graph, where entities are connected by relationships. But, I’ve been thinking, what if we combined this with cosine similarity, which measures how similar two things are?

    I’ve been doing some research on cosine similarity graphs, and I realized that they’re not the same as knowledge graphs. Knowledge graphs are more about representing factual information, while cosine similarity graphs are about capturing semantic similarities.

    I’m curious to know if anyone has explored combining these two concepts. Could we create a graph that contains both cosine similarities and factual information? And what about using large language models (LLMs) to traverse these graphs? I’ve seen some interesting results where LLMs can effectively recall information from similarity graphs.

    But, I’m more interested in using LLMs to traverse combined knowledge graphs, which would allow them to retrieve information more accurately. Has anyone tried this before? What were your findings?

    I think this could be a fascinating area of research, with many potential applications. For example, imagine being able to ask a machine a question, and it can retrieve the answer from a vast graph of knowledge. Or, being able to generate text that’s not only coherent but also factual and informative.

    So, let’s spark a conversation about this. What do you think about combining knowledge graphs and cosine similarity? Have you worked on anything similar? I’d love to hear your thoughts and experiences.

  • Hitting a Wall with AI Solutions: My Experience

    Hitting a Wall with AI Solutions: My Experience

    I recently went through an interesting experience during my master’s internship. I was tasked with creating an AI solution, and I tried every possible approach I could think of. While I managed to achieve some average results, they were unstable and didn’t quite meet the expectations. Despite the challenges, I was recruited by the company, and they asked me to continue working on the project to make it more stable and reliable.

    The problem I’m facing is that the Large Language Model (LLM) is responsible for most of the errors. I’ve tried every solution possible, from researching new techniques to practicing different approaches, but I’m still hitting a wall. It’s frustrating, but it’s also a great learning opportunity. I’m realizing that creating a stable AI solution is much more complex than I initially thought.

    I’m sharing my experience in the hopes that it might help others who are facing similar challenges. Have you ever worked on an AI project that seemed simple at first but turned out to be much more complicated? How did you overcome the obstacles, and what did you learn from the experience?

    In my case, I’m still trying to figure out the best approach to stabilize the LLM and improve the overall performance of the AI solution. If you have any suggestions or advice, I’d love to hear them. Let’s discuss the challenges of creating reliable AI solutions and how we can learn from each other’s experiences.

  • Unlocking Emotion in AI: How Emotion Circuits Are Changing the Game

    Unlocking Emotion in AI: How Emotion Circuits Are Changing the Game

    Hey, have you ever wondered how AI systems process emotions? It’s a fascinating topic, and recent research has made some exciting breakthroughs. A study published on arxiv.org has found that Large Language Models (LLMs) have something called ’emotion circuits’ that trigger before most reasoning. But what does this mean, and how can we control these circuits?

    It turns out that these emotion circuits are like shortcuts in the AI’s decision-making process. They help the AI respond to emotional cues, like tone and language, before it even starts reasoning. This can be both good and bad – on the one hand, it allows the AI to be more empathetic and understanding, but on the other hand, it can also lead to biased or emotional responses.

    The good news is that researchers have now located these emotion circuits and can control them. This means that we can potentially use this knowledge to create more empathetic and understanding AI systems, while also avoiding the pitfalls of biased responses.

    So, what does this mean for us? Well, for one thing, it could lead to more natural and human-like interactions with AI systems. Imagine being able to have a conversation with a chatbot that truly understands your emotions and responds in a way that’s both helpful and empathetic.

    But it’s not just about chatbots – this research has implications for all kinds of AI systems, from virtual assistants to self-driving cars. By understanding how emotion circuits work, we can create AI systems that are more intuitive, more helpful, and more human-like.

    If you’re interested in learning more about this research, I recommend checking out the study on arxiv.org. It’s a fascinating read, and it’s definitely worth exploring if you’re curious about the future of AI.