Large Language Models Converge on Brain-Like Word Representations

One of the greatest puzzles of all time is how understanding arises from neural mechanics. Our brains are networks of billions of biological neurons transmitting chemical and electrical signals along their connections. Large language models are networks of millions or billions of digital neurons, implementing functions that read the output of other functions in complex networks. The failure to see how meaning would arise from such mechanics has led many cognitive scientists and philosophers to various forms of dualism -- and many artificial intelligence researchers to dismiss large language models as stochastic parrots or jpeg-like compressions of text corpora. We show that human-like representations arise in large language models. Specifically, the larger neural language models get, the more their representations are structurally similar to neural response measurements from brain imaging.Comment: Work in proces

Advances in Spectral Learning with Applications to Text Analysis and Brain Imaging

Spectral learning algorithms are becoming increasingly popular in data-rich domains, driven in part by recent advances in large scale randomized SVD, and in spectral estimation of Hidden Markov Models. Extensions of these methods lead to statistical estimation algorithms which are not only fast, scalable, and useful on real data sets, but are also provably correct. Following this line of research, we make two contributions. First, we propose a set of spectral algorithms for text analysis and natural language processing. In particular, we propose fast and scalable spectral algorithms for learning word embeddings -- low dimensional real vectors (called Eigenwords) that capture the “meaning” of words from their context. Second, we show how similar spectral methods can be applied to analyzing brain images. State-of-the-art approaches to learning word embeddings are slow to train or lack theoretical grounding; We propose three spectral algorithms that overcome these limitations. All three algorithms harness the multi-view nature of text data i.e. the left and right context of each word, and share three characteristics: 1). They are fast to train and are scalable. 2). They have strong theoretical properties. 3). They can induce context-specific embeddings i.e. different embedding for “river bank” or “Bank of America”. \end{enumerate} They also have lower sample complexity and hence higher statistical power for rare words. We provide theory which establishes relationships between these algorithms and optimality criteria for the estimates they provide. We also perform thorough qualitative and quantitative evaluation of Eigenwords and demonstrate their superior performance over state-of-the-art approaches. Next, we turn to the task of using spectral learning methods for brain imaging data. Methods like Sparse Principal Component Analysis (SPCA), Non-negative Matrix Factorization (NMF) and Independent Component Analysis (ICA) have been used to obtain state-of-the-art accuracies in a variety of problems in machine learning. However, their usage in brain imaging, though increasing, is limited by the fact that they are used as out-of-the-box techniques and are seldom tailored to the domain specific constraints and knowledge pertaining to medical imaging, which leads to difficulties in interpretation of results. In order to address the above shortcomings, we propose Eigenanatomy (EANAT), a general framework for sparse matrix factorization. Its goal is to statistically learn the boundaries of and connections between brain regions by weighing both the data and prior neuroanatomical knowledge. Although EANAT incorporates some neuroanatomical prior knowledge in the form of connectedness and smoothness constraints, it can still be difficult for clinicians to interpret the results in specific domains where network-specific hypotheses exist. We thus extend EANAT and present a novel framework for prior-constrained sparse decomposition of matrices derived from brain imaging data, called Prior Based Eigenanatomy (p-Eigen). We formulate our solution in terms of a prior-constrained l1 penalized (sparse) principal component analysis. Experimental evaluation confirms that p-Eigen extracts biologically-relevant, patient-specific functional parcels and that it significantly aids classification of Mild Cognitive Impairment when compared to state-of-the-art competing approaches

The ERP response to the amount of information conveyed by words in sentences

Contains fulltext : 132194.pdf (publisher's version ) (Open Access)Reading times on words in a sentence depend on the amount of information the words convey, which can be estimated by probabilistic language models. We investigate whether event-related potentials (ERPs), too, are predicted by information measures. Three types of language models estimated four different information measures on each word of a sample of English sentences. Six different ERP deflections were extracted from the EEG signal of participants reading the same sentences. A comparison between the information measures and ERPs revealed a reliable correlation between N400 amplitude and word surprisal. Language models that make no use of syntactic structure fitted the data better than did a phrase-structure grammar, which did not account for unique variance in N400 amplitude. These findings suggest that different information measures quantify cognitively different processes and that readers do not make use of a sentence’s hierarchical structure for generating expectations about the upcoming word.11 p

Decoding linguistic information from EEG signals

For many years, the fields of the cognitive neuroscience of language and natural language processing (NLP) have been relatively distinct and non-overlapping. Recent breakthrough research is starting to show that these two fields, in their common goal towards understanding and modelling language, have a lot to offer each other. As developments in machine learning continue to break into new ground, due largely in part to the successful development of novel classifiers that can be efficiently trained to model highly nonlinear dynamic systems, such as language, the open question is how well these models perform on human neural signals during language processing. Recent results are beginning to show that various types of human signals (eye-tracking, fMRI, MEG) can successfully model various linguistic aspects of what is being concurrently processed by the brain. EEG is a cheap and relatively accessible way to access neural signals and this thesis explores the extent to which decoding of EEG data, using state-of-the-art models common in NLP, to carry out this task. Critically, an important foundation needs to be in place that can fully explore the types of linguistic signal that is decodable with EEG. This thesis attempts to answer this question, setting the stage for joint modelling of text and neural signals to advance the field of NLP. This research is also of interest to cognitive neuroscientists as the data collected for this thesis will be openly accessible to all, with accompanying linguistic annotation, which can help to answer various questions about the spatiotemporal dynamics during the reading of naturalistic texts. In Chapter 1, I provide an overview of the major literature that has investigated the status of linguistic processing from neural signals, setting the research question in the correct historical context. This literature review serves as the basis for the two experimental chapters which follow and is thus subdivided into two main sections. Chapter 2 explores the various aspects of linguistic processing which are decodable from the novel EEG dataset collected for this thesis, with a strong emphasis on controlling for potential confounds as much as possible. Using a novel machine learning classifier, I show that with specialised training methods, generalisation to novel data relating to part-of-speech decoding is possible. In Chapter 3, the preprocessing steps involved in preparing the data are examined, in which I show that depending on the modelling goal, some steps are particularly useful to boost performance of linguistic decoding of EEG stimuli. Finally, in Chapter 4, a broad review of the results, their implications and limitations are considered