This paper explores the relationship between transformer language models and simpler n-gram models. It demonstrates that transformers, despite their complexity, implicitly learn n-gram statistics, and that these statistics significantly contribute to their performance. The authors introduce a method to extract these n-gram distributions from transformer models and show that using these extracted distributions in a simple n-gram model can achieve surprisingly strong performance, sometimes even exceeding the performance of the original transformer on certain tasks. This suggests that a substantial part of a transformer's knowledge is captured by these implicit n-gram representations, offering a new perspective on how transformers process and represent language. Furthermore, the study reveals that larger transformers effectively capture longer-range dependencies by learning longer n-gram statistics, providing a quantitative link between model size and the ability to model long-range contexts.
Researchers explored how AI perceives accent strength in spoken English. They trained a model on a dataset of English spoken by non-native speakers, representing 22 native languages. Instead of relying on explicit linguistic features, the model learned directly from the audio, creating a "latent space" where similar-sounding accents clustered together. This revealed relationships between accents not previously identified, suggesting accents are perceived based on shared pronunciation patterns rather than just native language. The study then used this model to predict perceived accent strength, finding a strong correlation between the model's predictions and human listener judgments. This suggests AI can accurately quantify accent strength and provides a new tool for understanding how accents are perceived and potentially how pronunciation influences communication.
HN users discussed the potential biases and limitations of AI accent detection. Several commenters highlighted the difficulty of defining "accent strength," noting its subjectivity and dependence on the listener's own linguistic background. Some pointed out the potential for such technology to be misused in discriminatory practices, particularly in hiring and immigration. Others questioned the methodology and dataset used to train the model, suggesting that limited or biased training data could lead to inaccurate and unfair assessments. The discussion also touched upon the complexities of accent perception, including the influence of factors like clarity, pronunciation, and prosody, rather than simply deviation from a "standard" accent. Finally, some users expressed skepticism about the practical applications of the technology, while others saw potential uses in areas like language learning and communication improvement.
This paper introduces a novel method for inferring the "phylogenetic" relationships between large language models (LLMs), treating their development like the evolution of species. By analyzing the outputs of various LLMs on a standardized set of tasks, the researchers construct a distance matrix reflecting the similarity of their behaviors. This matrix then informs the creation of a phylogenetic tree, visually representing the inferred evolutionary relationships. The resulting tree reveals clusters of models based on their architectural similarities and training data, providing insights into the influence of these factors on LLM behavior. This approach offers a new perspective on understanding the development and diversification of LLMs, moving beyond simple performance comparisons to explore the deeper connections between them.
Several Hacker News commenters express skepticism about the paper's methodology and conclusions. Some doubt the reliability of using log-likelihoods on cherry-picked datasets to infer relationships, suggesting it's more a measure of dataset similarity than true model ancestry. Others question the assumption that LLMs even have a meaningful "phylogeny" like biological organisms, given their development process. The idea of "model paleontology" is met with both interest and doubt, with some arguing that internal model parameters would offer more robust insights than behavioral comparisons. There's also discussion on the limitations of relying solely on public data and the potential biases introduced by fine-tuning. A few commenters raise ethical concerns around potential misuse of such analysis for IP infringement claims, highlighting the difference between code lineage and learned knowledge.
Google researchers investigated how well large language models (LLMs) can predict human brain activity during language processing. By comparing LLM representations of language with fMRI recordings of brain activity, they found significant correlations, especially in brain regions associated with semantic processing. This suggests that LLMs, despite being trained on text alone, capture some aspects of how humans understand language. The research also explored the impact of model architecture and training data size, finding that larger models with more diverse training data better predict brain activity, further supporting the notion that LLMs are developing increasingly sophisticated representations of language that mirror human comprehension. This work opens new avenues for understanding the neural basis of language and using LLMs as tools for cognitive neuroscience research.
Hacker News users discussed the implications of Google's research using LLMs to understand brain activity during language processing. Several commenters expressed excitement about the potential for LLMs to unlock deeper mysteries of the brain and potentially lead to advancements in treating neurological disorders. Some questioned the causal link between LLM representations and brain activity, suggesting correlation doesn't equal causation. A few pointed out the limitations of fMRI's temporal resolution and the inherent complexity of mapping complex cognitive processes. The ethical implications of using such technology for brain-computer interfaces and potential misuse were also raised. There was also skepticism regarding the long-term value of this particular research direction, with some suggesting it might be a dead end. Finally, there was discussion of the ongoing debate around whether LLMs truly "understand" language or are simply sophisticated statistical models.
The blog post demonstrates how to implement symbolic differentiation using definite clause grammars (DCGs) in Prolog. It leverages the elegant, declarative nature of DCGs to parse mathematical expressions represented as strings and simultaneously construct their derivative. By defining grammar rules for basic arithmetic operations (addition, subtraction, multiplication, division, and exponentiation), including the chain rule and handling constants and variables, the Prolog program can effectively differentiate a wide range of expressions. The post highlights the concise and readable nature of this approach, showcasing the power of DCGs for tackling symbolic computation tasks.
Hacker News users discussed the elegance and power of using definite clause grammars (DCGs) for symbolic differentiation, praising the conciseness and declarative nature of the approach. Some commenters pointed out the historical connection between Prolog and DCGs, highlighting their suitability for symbolic computation. A few users expressed interest in exploring further applications of DCGs beyond differentiation, such as parsing and code generation. The discussion also touched upon the performance implications of using DCGs and compared them to other parsing techniques. Some commenters raised concerns about the readability and maintainability of complex DCG-based systems.
Word2Vec's efficiency stems from two key optimizations: negative sampling and subsampling frequent words. Negative sampling simplifies the training process by only updating a small subset of weights for each training example. Instead of updating all output weights to reflect the true context words, it updates a few weights corresponding to the actual context words and a small number of randomly selected "negative" words that aren't in the context. This dramatically reduces computation. Subsampling frequent words like "the" and "a" further improves efficiency and leads to better representations for less frequent words by preventing the model from being overwhelmed by common words that provide less contextual information. These two techniques, combined with clever use of hierarchical softmax for even larger vocabularies, allow Word2Vec to train on massive datasets and produce high-quality word embeddings.
Hacker News users discuss the surprising effectiveness of seemingly simple techniques in word2vec. Several commenters highlight the importance of the negative sampling trick, not only for computational efficiency but also for its significant impact on the quality of the resulting word vectors. Others delve into the mathematical underpinnings, noting that the model implicitly factorizes a shifted Pointwise Mutual Information (PMI) matrix, offering a deeper understanding of its function. Some users question the "secret" framing of the article, suggesting these details are well-known within the NLP community. The discussion also touches on alternative approaches and the historical context of word embeddings, including older methods like Latent Semantic Analysis.
"ELIZA Reanimated" revisits the classic chatbot ELIZA, not to replicate it, but to explore its enduring influence and analyze its underlying mechanisms. The paper argues that ELIZA's effectiveness stems from exploiting vulnerabilities in human communication, specifically our tendency to project meaning onto vague or even nonsensical responses. By systematically dissecting ELIZA's scripts and comparing it to modern large language models (LLMs), the authors demonstrate that ELIZA's simple pattern-matching techniques, while superficially mimicking conversation, actually expose deeper truths about how we construct meaning and perceive intelligence. Ultimately, the paper encourages reflection on the nature of communication and warns against over-attributing intelligence to systems, both past and present, based on superficial similarities to human interaction.
The Hacker News comments on "ELIZA Reanimated" largely discuss the historical significance and limitations of ELIZA as an early chatbot. Several commenters point out its simplistic pattern-matching approach and lack of true understanding, while acknowledging its surprising effectiveness in mimicking human conversation. Some highlight the ethical considerations of such programs, especially regarding the potential for deception and emotional manipulation. The technical implementation using regex is also mentioned, with some suggesting alternative or updated approaches. A few comments draw parallels to modern large language models, contrasting their complexity with ELIZA's simplicity, and discussing whether genuine understanding has truly been achieved. A notable comment thread revolves around Joseph Weizenbaum's, ELIZA's creator's, later disillusionment with AI and his warnings about its potential misuse.
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https://news.ycombinator.com/item?id=44016564
HN commenters discuss the paper's approach to analyzing transformer behavior through the lens of n-gram statistics. Some find the method insightful, suggesting it simplifies understanding complex transformer operations and offers a potential bridge between statistical language models and neural networks. Others express skepticism, questioning whether the observed n-gram behavior is a fundamental aspect of transformers or simply a byproduct of training data. The debate centers around whether this analysis genuinely reveals something new about transformers or merely restates known properties in a different framework. Several commenters also delve into specific technical details, discussing the implications for tasks like machine translation and the potential for improving model efficiency. Some highlight the limitations of n-gram analysis, acknowledging its inability to fully capture the nuanced behavior of transformers.
The Hacker News post titled "Understanding Transformers via N-gram Statistics" (https://news.ycombinator.com/item?id=44016564) discussing the arXiv paper (https://arxiv.org/abs/2407.12034) has several comments exploring the paper's findings and their implications.
One commenter points out the seemingly paradoxical observation that while transformers are theoretically capable of handling long-range dependencies better than n-grams, in practice, they appear to rely heavily on short-range n-gram statistics. They express interest in understanding why this is the case and whether it points to limitations in current training methodologies or a fundamental aspect of how transformers learn.
Another comment builds on this by suggesting that the reliance on n-gram statistics might be a consequence of the data transformers are trained on. They argue that if the training data exhibits strong short-range correlations, the model will naturally learn to exploit these correlations, even if it has the capacity to capture longer-range dependencies. This raises the question of whether transformers would behave differently if trained on data with different statistical properties.
A further comment discusses the practical implications of these findings for tasks like machine translation. They suggest that the heavy reliance on n-grams might explain why transformers sometimes struggle with long, complex sentences where understanding the overall meaning requires considering long-range dependencies. They also speculate that this limitation might be mitigated by incorporating explicit mechanisms for handling long-range dependencies into the transformer architecture or training process.
Another commenter raises the issue of interpretability. They suggest that the dominance of n-gram statistics might make transformers more interpretable, as it becomes easier to understand which parts of the input sequence are influencing the model's output. However, they also acknowledge that this interpretability might be superficial if the true underlying mechanisms of the model are more complex.
Finally, a commenter expresses skepticism about the generalizability of the paper's findings. They argue that the specific tasks and datasets used in the study might have influenced the results and that further research is needed to determine whether the observed reliance on n-gram statistics is a general property of transformers or a specific artifact of the experimental setup. They suggest exploring different architectures, training regimes, and datasets to gain a more comprehensive understanding of the role of n-gram statistics in transformer behavior.