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author:
(1) Andrew J. Peterson, University of Betiors (Andrew.peterson@univ-Poiteers.FR).
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Links table
Abstract
Related work
Media, liquidation bubbles and echo rooms
Network effects and consequently information
The collapse of the model
The well -known biases in llms
A model for the collapse of knowledge
results
Discussion and references
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Excessive
Comparison of tails
Determine the collapse of knowledge
The well -known biases in llms
More artificial intelligence models such as LLMS are not immune to specified bias problems and their measurement in the algorithms of automated learning (Nazer et al Chapter 2). In an attempt to generate human -like texts, such as the views of non -representative minorities and reduce the broad concept of the “positive” text to this simply to express “joy”.
The last work tries to address these problems through a variety of methods, for example by overcoming the features of an active actress, which is the forecast otherwise below the optimal level (GESI et al Karlas et al ˇ. However, the work of the mechanical interpretation on LLMS so far indicates that our understanding, with improvement, is still very limited (for example Kramar et al ´., 2024; wu et al., 2023). As such, the direct methods of overcoming such biases are, at least, do not approach at hand. Finally, while a lot of focus is naturally on ethnic and public biases, there may also be widespread but less observed in the content and shape of the output. Windler and others. (2024), for example, providing evidence that the current LLMS trained on large amounts of English text “depends on” the English language in its inherent representations, as if it were a kind of reference language. \ n
One of the specific fields in which the diversity of LLM outputs was analyzed at the level of a symbol through the distinctive symbol in the context of the decoding strategies. In some situations, the use of the search for the beam can probably choose the distinctive symbol to create frequently hanging phrases (SU et al., 2022). Moreover, the Thelonious Monk lines are a melody, humans do not connect sequences of the most likely words, but sometimes they are trying to surprise the listener by taking samples from the words of low possibilities, moving agreements, etc. Holtzman et al. (2020) (referring to GRICE, 1975).
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