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Text of the page (random words):
me and now it ran from me but whatever mood the apparition evinced whatever aspect it wore it failed not for seven successive nights to meet me the moment i entered the land of slumber assistant replies 1 the narrator recalls a saying about dreaming of children being a sign of trouble and remembers an incident from her childhood she then describes her recent dreams of infants over the past week 2 the passage mentions the narrator s childhood memory which happened when she was six years old and the past week of dreams the time elapsed in the passage is the week of dreams 3 1 week has elapsed at seven days a week that s 7 days 24 hours in a day so 168 hours 60 minutes in an hour so 10080 minutes 4 10080 minutes 5 moderate confidence as the childhood memory is not counted as time passing in the scene what no model can do while i think large language models will be tremendously useful assistants in content analysis i don t think we can dispense with multiple human readers as david bamman explains in a good twitter thread the core challenges of this work are a coming up with a new construct to measure b demonstrating that it can be measured and c showing that there s value in doing so intersubjective agreement between human beings is still the only way to know that we have addressed those questions that s why i wouldn t feel comfortable just making up my own definition of say suspense teaching a model to measure it and then running the model across a thousand books the problem with that approach is not that llms have measurement error as we ve seen in greg yauney s paper measurement methods with much more error can be productive the problem is that it isn t clear what a single researcher s construct means in the first place to be confident that we re measuring something called suspense we need to show that multiple people recognize it as suspense and in spite of all the rhetorical personification in this blog post which i trust you have understood rhetorically a model is not a separate person so a project of this kind still needs to start by getting several human beings to read systematically and compare notes before the human codebook is translated into a prompt on the other hand i do think language models will allow us to pose questions that are currently hard to pose at scale questions about plot and character for instance require reading a whole book while applying delicate decision criteria that may be hard to remember and keep stable across many books language models will help here not just because they re fast but because they can provide a relatively stable yardstick by which to measure slippery concepts even at a modest scale of analysis this is a preliminary report on a very strange world for me the most surprising take away from this experiment was not that deep learning is more accurate than statistical nlp but that it may also be in some ways more interpretable because a language model has to think out loud it tends to automatically document its own reasoning this is useful even when or especially when the model doesn t think you ve defined the construct it s supposed to measure clearly enough references 1 gérard genette n arrative discourse an essay in method trans jane e lewin ithaca cornell univ press 1980 97 2 ted underwood why literary time is measured in minutes new literary history 85 2 2018 351 65 3 greg yauney ted underwood and david mimno computational prediction of elapsed narrative time 2019 4 simon willison a simple python wrapper for the chatgpt api 2023 tags fiction gpt 4 llms time categories interpretive theory machine learning social effects of machine learning transformer models mapping the latent spaces of culture post author by tedunderwood post date october 21 2021 on tues oct 26 the center for digital humanities at princeton will sponsor a roundtable on the implications of stochastic parrots for the humanities to prepare for that roundtable they asked three humanists to write position papers on the topic mine follows i ll give a 5 min 500 word précis at the event itself this is the 2000 word version with pictures it also has a doi if you want a stable version to cite the technology at the center of this roundtable doesn t yet have a consensus name some observers point to an architecture the transformer 1 on the dangers of stochastic parrots focuses on size and discusses large language models 2 a paper from stanford emphasizes applications foundation models are those that can adapt to a wide range of downstream tasks 3 each definition identifies a different feature of recent research as the one that matters to keep that question open i ll refer here to deep neural models of language a looser category however we define them neural models of language are already changing the way we search the web write code and even play games academics outside computer science urgently need to discuss their role on the dangers of stochastic parrots deserves credit for starting the discussion especially since publication required tenacity and courage i am honored to be part of an event exploring its significance for the humanities the argument that bender et al advance has two parts first that large language models pose social risks and second that they will turn out to be misdirected research effort anyway since they pretend to perform natural language understanding but do not have access to meaning 615 i agree that the trajectory of recent research is dangerous but to understand the risks language models pose i think we will need to understand how they produce meaning the premise that they simply do not have access to meaning tends to prevent us from grasping the models social role i hope humanists can help here by offering a wider range of ways to think about the work language does it is true that language models don t yet represent their own purposes or an interlocutor s state of mind these are important aspects of language and for stochastic parrots they are the whole story the article defines meaning as meaning conveyed between individuals and grounded in communicative intent 616 but in historical disciplines it is far from obvious that all meaning boils down to intentional communication between individuals historians often use meaning to describe something more collective because the meaning of a literary work for example is not circumscribed by intent it is common for debates about the meaning of a text to depend more on connections to books published a century earlier or later than on reconstructing the author s conscious plan 4 i understand why researchers in a field named artificial intelligence would associate meaning with mental activity and see writing as a dubious proxy for it but historical disciplines rarely have access to minds or even living subjects we work mostly with texts and other traces for this reason i m not troubled by the part of stochastic parrots that warns about the human tendency to attribute meaning to text even when the text is not grounded in communicative intent 618 616 historians are already in the habit of finding meaning in genres nursery rhymes folktale motifs ruins political trends and other patterns that never had a single author with a clear purpose 5 if we could only find meaning in intentional communication we wouldn t find much meaning in the past at all so not all historical researchers will be scandalized when we hear that a model is merely stitching together sequences of linguistic forms it has observed in its vast training data 617 that s often what we do too and we could use help a willingness to find meaning in collective patterns may be especially necessary for disciplines that study the past but this flexibility is not limited to scholars the writers and artists who borrow language models for creative work likewise appreciate that their instructions to the model acquire meaning from a training corpus the phrase unreal engine for instance encourages clip to select pictures with a consistent cartoonified style but this has nothing to do with the dictionary definition of unreal it s just a helpful side effect of the fact that many pictures are captioned with the name of the game engine that produced them in short i think people who use neural models of language typically use them for a different purpose than stochastic parrots assumes the immediate value of these models is often not to mimic individual language understanding but to represent specific cultural practices like styles or expository templates so they can be studied and creatively remixed this may be disappointing for disciplines that aspire to model general intelligence but for historians and artists cultural specificity is not disappointing intelligence only starts to interest us after it mixes with time to become a biased limited pattern of collective life models of culture are exactly what we need while i m skeptical that language models are devoid of meaning i do share other concerns in stochastic parrots for instance i agree that researchers will need a way to understand the subset of texts that shape a model s response to a given prompt culture is historically specific so models will never be free of omission and bias but by the same token we need to know which practices they represent if companies want to offer language models as a service to the public say in web search they will need to do even more than know what the models represent somehow a single model will need to produce a picture of the world that is acceptable to a wide range of audiences without amplifying harmful biases or filtering out minority discourses bender et al 614 that s a delicate balancing act historians don t have to compress their material as severely since history is notoriously a story of conflict and our sources were interested participants few people expect historians to represent all aspects of the past with one correctly balanced model on the contrary historical inquiry is usually about comparing perspectives machine learning is not the only way to do this but it can help for instance researchers can measure differences of perspective by training multiple models on different publication venues or slices of the timeline 6 when research is organized by this sort of comparative purpose the biases in data are not usually a reason to refrain from modeling but a reason to create more corpora and train models that reflect a wider range of biases on the other hand training a variety of models becomes challenging when each job requires thousands of gpus tech companies might have the resources to train many models at that scale but will universities there are several ways around this impasse one is to develop lighter weight models 7 another is to train a single model that can explicitly distinguish multiple perspectives at present researchers create this flexibility in a rough and ready way by fine tuning bert on different samples a more principled approach might design models to recognize the social structure in their original training data one recent paper associates each text with a date stamp for instance to train models that respond differently to questions about different years 8 similar approaches might produce models explicitly conditioned on variables like venue or nationality models that could associate each statement or prediction they make with a social vantage point if neural language models are to play a constructive role in research universities will also need alternatives to material dependence on tech giants in 2020 it seemed that only the largest corporations could deploy enough silicon to move this field forward in october 2021 things are starting to look less dire coalitions like eleutherai are reverse engineering language models 9 smaller corporations like huggingface are helping to cover underrepresented languages nsf is proposing new computing resources 10 the danger of oligopoly is by no means behind us but we can at least begin to see how scholars might train models that represent a wider range of perspectives of course scholars are not the only people who matter what about the broader risks of language modeling outside universities i agree with the authors of stochastic parrots that neural language models are dangerous but i am not sure that critical discourse has alerted us to the most important dangers yet critics often prefer to say that these models are dangerous only because they don t work and are devoid of meaning that may seem to be the strongest rhetorical position since it concedes nothing to the models but i suspect this hard line also prevents critics from envisioning what the models might be good for and how they re likely to be mis used consider the surprising art scene that sprang up when clip was released openai still hasn t released the dall e model that translates clip s embeddings of text into images 11 but that didn t stop graduate students and interested amateurs from duct taping clip to various generative image models and using the contraption to explore visual culture in dizzying ways the angel of air unreal engine vqgan clip aran komatsukaki may 31 2021 will the emergence of this subculture make any sense if we assume that clip is just a failed attempt to reproduce individual language use in practice the people tinkering with clip don t expect it to respond like a human reader more to the point they don t want it to they re fascinated because clip uses language differently than a human individual would mashing together the senses and overtones of words and refracting them into the potential space of internet images like a new kind of synesthesia 12 the pictures produced are fascinating but at least for now too glitchy to impress most people as art they re better understood as postcards from an unmapped latent space 13 the point of a postcard after all is not to be itself impressive but to evoke features of a larger region that looks fun to explore here the region is a particular visual culture artists use clip to find combinations of themes and styles that could have occurred within it although they never quite did the clockwork angel of air trending on artstation diffusion clip rivershavewings katherine crowson september 14 2021 will models of this kind also have negative effects absolutely the common observation that they could reinforce existing biases is the mildest possible example if we approach neural models as machines for mapping and rewiring collective behavior we will quickly see that they could do much worse for instance deepfakes could create new hermetically sealed subcultures and beliefs that are impossible to contest i m not trying to decide whether neural language models are good or bad in this essay just trying to clarify what s being modeled why people care and what kinds of good or bad effects we might expect reaching a comprehensive judgment is likely to take decades after all models are easy to distribute so this was never a problem like ...