The Problem of Alignment
- URL: http://arxiv.org/abs/2401.00210v1
- Date: Sat, 30 Dec 2023 11:44:59 GMT
- Title: The Problem of Alignment
- Authors: Tsvetelina Hristova, Liam Magee, Karen Soldatic
- Abstract summary: Large Language Models produce sequences learned as statistical patterns from large corpora.
After initial training models must be aligned with human values, prefer certain continuations over others.
We examine this practice of structuration as a two-way interaction between users and models.
- Score: 1.2277343096128712
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Large Language Models produce sequences learned as statistical patterns from
large corpora. In order not to reproduce corpus biases, after initial training
models must be aligned with human values, preferencing certain continuations
over others. Alignment, which can be viewed as the superimposition of normative
structure onto a statistical model, reveals a conflicted and complex
interrelationship between language and technology. This relationship shapes
theories of language, linguistic practice and subjectivity, which are
especially relevant to the current sophistication in artificially produced
text. We examine this practice of structuration as a two-way interaction
between users and models by analysing how ChatGPT4 redacts perceived
`anomalous' language in fragments of Joyce's Ulysses and the new linguistic
practice of prompt engineering. We then situate this alignment problem
historically, revisiting earlier postwar linguistic debates which counterposed
two views of meaning: as discrete structures, and as continuous probability
distributions. We discuss the largely occluded work of the Moscow Linguistic
School, which sought to reconcile this opposition. Our attention to the Moscow
School and later related arguments by Searle and Kristeva casts the problem of
alignment in a new light: as one involving attention to the social
structuration of linguistic practice, including structuration of anomalies
that, like the Joycean text, exist in defiance of expressive conventions. These
debates around the communicative orientation toward language can help explain
some of the contemporary behaviours and interdependencies that take place
between users and LLMs.
Related papers
- Constructive Approach to Bidirectional Causation between Qualia Structure and Language Emergence [5.906966694759679]
This paper presents a novel perspective on the bidirectional causation between language emergence and relational structure of subjective experiences.
We hypothesize that languages with distributional semantics, e.g., syntactic-semantic structures, may have emerged through the process of aligning internal representations among individuals.
arXiv Detail & Related papers (2024-09-14T11:03:12Z) - Foundational Models Defining a New Era in Vision: A Survey and Outlook [151.49434496615427]
Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world.
The models learned to bridge the gap between such modalities coupled with large-scale training data facilitate contextual reasoning, generalization, and prompt capabilities at test time.
The output of such models can be modified through human-provided prompts without retraining, e.g., segmenting a particular object by providing a bounding box, having interactive dialogues by asking questions about an image or video scene or manipulating the robot's behavior through language instructions.
arXiv Detail & Related papers (2023-07-25T17:59:18Z) - BabySLM: language-acquisition-friendly benchmark of self-supervised
spoken language models [56.93604813379634]
Self-supervised techniques for learning speech representations have been shown to develop linguistic competence from exposure to speech without the need for human labels.
We propose a language-acquisition-friendly benchmark to probe spoken language models at the lexical and syntactic levels.
We highlight two exciting challenges that need to be addressed for further progress: bridging the gap between text and speech and between clean speech and in-the-wild speech.
arXiv Detail & Related papers (2023-06-02T12:54:38Z) - Natural Language Decompositions of Implicit Content Enable Better Text
Representations [56.85319224208865]
We introduce a method for the analysis of text that takes implicitly communicated content explicitly into account.
We use a large language model to produce sets of propositions that are inferentially related to the text that has been observed.
Our results suggest that modeling the meanings behind observed language, rather than the literal text alone, is a valuable direction for NLP.
arXiv Detail & Related papers (2023-05-23T23:45:20Z) - Cross-Align: Modeling Deep Cross-lingual Interactions for Word Alignment [63.0407314271459]
The proposed Cross-Align achieves the state-of-the-art (SOTA) performance on four out of five language pairs.
Experiments show that the proposed Cross-Align achieves the state-of-the-art (SOTA) performance on four out of five language pairs.
arXiv Detail & Related papers (2022-10-09T02:24:35Z) - The Whole Truth and Nothing But the Truth: Faithful and Controllable
Dialogue Response Generation with Dataflow Transduction and Constrained
Decoding [65.34601470417967]
We describe a hybrid architecture for dialogue response generation that combines the strengths of neural language modeling and rule-based generation.
Our experiments show that this system outperforms both rule-based and learned approaches in human evaluations of fluency, relevance, and truthfulness.
arXiv Detail & Related papers (2022-09-16T09:00:49Z) - Improving Neural Cross-Lingual Summarization via Employing Optimal
Transport Distance for Knowledge Distillation [8.718749742587857]
Cross-lingual summarization models rely on the self-attention mechanism to attend among tokens in two languages.
We propose a novel Knowledge-Distillation-based framework for Cross-Lingual Summarization.
Our method outperforms state-of-the-art models under both high and low-resourced settings.
arXiv Detail & Related papers (2021-12-07T03:45:02Z) - Schr\"odinger's Tree -- On Syntax and Neural Language Models [10.296219074343785]
Language models have emerged as NLP's workhorse, displaying increasingly fluent generation capabilities.
We observe a lack of clarity across numerous dimensions, which influences the hypotheses that researchers form.
We outline the implications of the different types of research questions exhibited in studies on syntax.
arXiv Detail & Related papers (2021-10-17T18:25:23Z) - Comparative Error Analysis in Neural and Finite-state Models for
Unsupervised Character-level Transduction [34.1177259741046]
We compare the two model classes side by side and find that they tend to make different types of errors even when achieving comparable performance.
We investigate how combining finite-state and sequence-to-sequence models at decoding time affects the output quantitatively and qualitatively.
arXiv Detail & Related papers (2021-06-24T00:09:24Z) - Modelling Compositionality and Structure Dependence in Natural Language [0.12183405753834563]
Drawing on linguistics and set theory, a formalisation of these ideas is presented in the first half of this thesis.
We see how cognitive systems that process language need to have certain functional constraints.
Using the advances of word embedding techniques, a model of relational learning is simulated.
arXiv Detail & Related papers (2020-11-22T17:28:50Z) - Structured Attention for Unsupervised Dialogue Structure Induction [110.12561786644122]
We propose to incorporate structured attention layers into a Variational Recurrent Neural Network (VRNN) model with discrete latent states to learn dialogue structure in an unsupervised fashion.
Compared to a vanilla VRNN, structured attention enables a model to focus on different parts of the source sentence embeddings while enforcing a structural inductive bias.
arXiv Detail & Related papers (2020-09-17T23:07:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.