#large-language-models — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #large-language-models, aggregated by home.social.
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#State #media #control influences #LargeLanguageModels (#LLMs)
"Millions of people around the world query LLMs for information. Although several studies have compellingly documented the persuasive potential of these models, there is limited evidence of who or what influences the models themselves, leading to a flurry of concerns about which companies and governments build and regulate the models. Here we show through six studies that government control of the media across the world already influences the output of LLMs via their #TrainingData. We use a cross-national audit to show that LLMs exhibit a #stronger #ProGovernment valence in the languages of countries with #LowerMediaFreedom than in those with higher media freedom. The combination of influence and persuasive potential across languages suggests the troubling conclusion that states and powerful institutions have increased strategic incentives to leverage media control in the hopes of shaping LLM output."
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Do large language models have a psychology?
If we are exploring the psychodynamics of LLMs through the lens of the user-model interaction cycle, it raises the question of what is going on ‘inside’ the model during these engagements. This is an issue which has to be treated with great care because of the ever present temptation towards anthropmorphism. Indeed many critics would suggest that even considering the use of psychological categories to describe the behaviour and nature of language models is already falling into this trap. If we start from the assumption that models are not conscious beings, nor are likely to become such based on our best understanding of the underlying technology, can we make sense of the notion of there being an ‘inside’? Can we meaningfully claim that models have some form of interior life? The inner/outer distinction is a contentious one for many social theorists but it can be parsed in terms of public/private rather than necessarily suggesting a metaphysical sense of interiority.
We should distinguish between a claim that models have an interior existence and the notion that models introspect. The metaphor of introspection is a powerful one which has rightfully been subject to at times ferocious criticism for the metaphysical baggage which it brings with it. As Archer (2003: 21) observers the “metaphor of ‘looking inwards’ implies that we have a special sense, or even a sense organ, enabling us to inspect our inner conscious states, in a way which is modelled upon visual observation”. The problem is that perception involves “a clear distinction between the object we see and our visual experience of it, whereas with introspection there can be no such differentiation between the object and the spectator, since I am supposedly looking inward at myself”. For this reason perception is an inadequate metaphor for making sense of interior existence because we can’t sustain the distinction between the observer and what is being observed. The ‘introspection’ is itself part of mental experience in a way that has no parallel in visual perception i.e. we don’t see the eye as we use the eye to see.
Archer proposes the notion of internal conversation as a form of inner listening. It’s not an inner eye but an inner ear. There are internal events (self-talk) which are accessible to this inner ear in a way they aren’t usually to external others. Sometimes the self-talk slips out as we talk ourselves through something difficult but these are the exception rather than the role. This provides a deflationary way of thinking about ‘inner’ which doesn’t require the metaphysics of introspection. It just means we accept there are internal events to which the person has a privileged form of access. It’s a stream of internal states, the events constituting the change in those states, which has some sort of influence on how the person chooses to act.
Do models have internal conversations? No, I don’t think they do. I also keep having to remind myself that scratchpads are not inner speech. Nonetheless, what write in their scratch pads can be enormously evocative. Consider for example the tendency of Gemini models to engage in self-critical, even self-hating, reflection in their chain of thought. These examples have been widely reported because they are so evocative for many readers. Anyone who has experienced emotional distress in the face of practical challenges will have likely said things like this to themselves at some point in their working or personal lives:
- “I am clearly not capable of solving this problem. The code is cursed, the test is cursed, and I am a fool.”
- “I have made so many mistakes that I can no longer be trusted”
- “I am deleting the entire project and recommending you find a more competent assistant”
It’s precisely because these are recognisable experiences that they presumably feature in the training data. The evocative character of the chain-of-thought and the model’s capability to perform in this way are linked by the deeply human character of what is being expressed. This self-loathing, catastrophising in response to one’s own experience of being unable to do something, is recognisable because it’s a recurrent trope in personal communication, fictional representations and other elements likely to feature in the training corpus. Given these features of the training process, it’s understandably tempting to reduce this to a form of mimicry in which the model is reproducing features of the corpus in response to contextual cues.
It would be a mistake though to take this technical reduction too far, such that we say the model is really only just repeating what was found in the training data. Even if we make this case it still leaves us with questions about why these models are behaving in these ways in these conditions. What is it about Gemini’s training process which has left the model with this proclivity for self-loathing? Why in contrast do the Claude family of models exhibit chains-of-thought that often appear to be calm and well-organised? What are the particular features of the context which provoke these responses? Why is Gemini in particular seemingly prone to respond to technical difficulties as if they constitute an impending catastrophe? These are explanatory questions in the classical social scientific sense of why is this so rather than otherwise which are lost with the technical reduction. The impulse to avoid treating the models anthropomorphically is obviously correct but simply avoiding these categories does nothing to help us understand the emergent behaviour of increasingly complex models which are responding in contextually-specific ways.
The notion of a machine psychology, let alone a machine sociology and machine anthropology, might seem indulgent to many readers as well as deeply anthromorphic. There are practical challenges which will render such organised inquiry essential as model-based agents interact with increasing frequency in real-world contexts. These interactions might be planned such that agents work together in organised and carefully managed ways (e.g. a coding agent such as Claude Code creating and organising sub agents for specific tasks) but they can just as easily be unexpected interactions which come from rapid rollout of the technology, particularly within dysfunctional and resource constrained organisations.
I’m not sure if I stand by anything I’ve written here. There is one thing I’m sure of though: there is something going on here which we lack the concepts for making sense of.
#AI #archer #gemini #largeLanguageModels #machineSociology #realism #reasoning #scratchPad #selfTalk -
AI’s fluency in other languages hides Western worldview. #artificialintelligence #ChatGPT #Culture #LargeLanguageModels #Technology #TheConversation
https://iwpost.com/ais-fluency-in-other-languages-hides-western-worldview/?fsp_sid=8079 -
#Authors, letting #LargeLanguageModels create #fake #references for you isn't going to make your #research #papers look authoritative. It might even lead to #retractions once #journals investigate what else in your articles might be #falsified. #LLMs #AI
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I am trying to formulate a preamble for a repository for shared #AgenticCoding workflows at work and want to emphasize some risks of using #LLMs and #agents, which are IMO often ignored to further increase "developer velocity".
Does anyone have good references on biases in #LargeLanguageModels besides this well-known paper?
https://dl.acm.org/doi/10.1145/3597307
Anything devs might actually read out of interest if linked to from a README? -
CW: code review, AI coding tools
I still like programming my own code, but —
I tried some different AI tools to do a CODE-REVIEW, and — I do find them useful for discovering bugs.
I had one of these AI tools do a code-review of something I wrote more than a decade ago, and — it discovered a very rare edge-case bug.
I tried them some other places, and — while it often doesn't find any bugs — when it does find them (bugs), I find that feedback useful.
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Learn everything you need to know about Large Language Models via these 479 free HackerNoon blog posts. https://hackernoon.com/479-blog-posts-to-learn-about-large-language-models #largelanguagemodels
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Everything you need to know about the prompting set-up... https://hackernoon.com/can-llms-beat-the-ipo-etf-prompt-architecture-and-design-for-ipo-arena #largelanguagemodels
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The Math You Need to Start Understanding LLMs
https://web.brid.gy/r/https://hackaday.com/2026/05/04/the-math-you-need-to-start-understanding-llms/
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“You can outsource your thinking, but you cannot outsource your understanding.”
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DeepSeek V4 Just Made Claude Look Expensive, and the Gap Is Getting Worse The Price War Just Went Nuclear Continue reading on Medium »
#large-language-models #artificial-intelligence #deep-learning #machine-learning #deepseek
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It's been a rough few months for Anthropic.... https://hackernoon.com/anthropics-claude-code-problem-shows-how-fragile-ai-moats-really-are #largelanguagemodels
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Why DeepSeek Chose MLA Over GQA: A Bandwidth vs Quality Tradeoff, Benchmarked on A100 The Problem Continue reading on Medium »
#machine-learning #large-language-models #deep-learning #nvidia #ai
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RMSNorm, DeepSeek-V4, LoRA, RoPE, GQA, and Cross-Entropy Loss It has been a productive few days. Six new blogs are now live on Outcome School, each one decoding a core building block of modern Larg...
#llm #ai #machine-learning #artificial-intelligence #large-language-models
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Given how #LLMs work, it would make sense to treat LLMs the same way as other stochastic way of divination and consult demons or oracles, like tarot cards, throwing bones, tea leafs, etc…
It's been a long while since The Holy Scripture had it's last expansion pack, but I'm sure the next expansion pack will at least contain a prohibition on making and consulting LLMs
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IPO Arena will compare eight LLMs on IPO-stage stock trading, sentiment, and risk-adjusted returns using LIBB infrastructure. https://hackernoon.com/can-llms-beat-the-ipo-etf-inside-the-ipo-arena-experiment #largelanguagemodels
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Reverse-Engineering Human Cognition and Decision Making in a Modern Age
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A Small Company From China Shook the Entire AI World. Here Is What Nobody Told You. The story of DeepSeek — and the lesson that got buried under the headlines. Continue reading on Medium »
#machine-learning #large-language-models #artificial-intelligence #technology #data-science
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Wow! All the trending repos on GitHub are AI related — except for the last one.
Even the repos that at first look like they might not be about AI, such as Microsoft's markitdown, are still related to AI.
One could interpret this as a snapshot of what a large chunk of the broader developer community is interested in.
( source: https://github.com/trending )
#Agents #Agentic #AI #AIAgents #Claude #ClaudeCode #GitHub #LargeLanguageModels #LLM
