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Anthropomorphism in Large Language Models

Talking About Large Language Models

We live in an era of rapid progress in artificial intelligence, both within the field and in the public sphere.

The more adept large language models become at mimicking human language, the more vulnerable we become to anthropomorphism, to seeing the systems in which they are embedded as more human-like than they really are.

The Role of Meaning in Large Language Models

Meaning without reference in large language models

Large language models (llm) have become a popular tool for modeling human language.

The widespread success of large language models has been met with skepticism that they possess anything like human concepts or meanings.

Pre-training Large Language Models with Inductive Biases

What do Large Language Models Learn beyond Language?

Large language models (lm) have rapidly become a mainstay in natural language processing.

These models are known to acquire rich linguistic knowledge from training on large amounts of text.

A Comparison of Human and Large Language Models for Zero-Shot Explanations

Emergent Analogical Reasoning in Large Language Models

Large language models-large neural networks trained on a simple predictive objective over a massive corpus of natural language-have reinvigorated debate over whether human cognitive capacities might emerge in such generic models given sufficient training data.

Of particular interest is the ability of these models to reason about novel problems zero-shot, without any direct training on those problems.

Understanding HTML with Large Language Models

Large language models (LLMs) have shown exceptional performance on a variety of natural language tasks. Yet, their capabilities for HTML understanding -- i.e., parsing the raw HTML of a webpage, with

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Neural and Evolutionary Computing

Level Generation Through Large Language Models

Large Language Models (LLMs) are powerful tools, capable of leveraging their training on natural language to write stories, generate code, and answer questions. But can they generate functional video

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Multi-step Large Language Models

Faithful Reasoning Using Large Language Models

We show how large language models can be made to perform faithful multi-step reasoning via a process whose causal structure mirrors the underlying logical structure of the problem.

Our approach works by chaining together reasoning steps, where each step results from calls to two fine-tuned large language models, one for selection and one for inference, to produce a valid reasoning trace.

Bookmarked byLuheng

Program Synthesis with Large Language Models

Programming Languages

This paper explores the limits of the current generation of large language models for program synthesis in general purpose programming languages. We evaluate a collection of such models (with between

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Liked byParas

MultiMedQA: A Benchmark for Human Evaluation of Large Language Models

Large Language Models Encode Clinical Knowledge

Large language models (llm) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications is high.

Today, attempts to assess models'clinical knowledge typically rely on automated evaluations on limited benchmarks.

In-Context Learning for Real-Time Adaptive Machine Translation

Adaptive Machine Translation with Large Language Models

Machine translation (mt) has achieved significant progress in the area of domain adaptation, especially for high-resource languages.

However, real-time adaptation remains challenging.

Measuring Conceptual Consistency in Large Language Models

Unpacking Large Language Models with Conceptual Consistency

Large language models (llm) are increasingly able to answer questions like these accurately, but that ability does not necessarily imply a general understanding of concepts relevant to the anchor query.

We propose conceptual consistency to measure a llm s understanding of relevant concepts.

Controllability and Robustness in Large Language Models

Large Language Models with Controllable Working Memory

We undertake a joint study of controllability and robustness in the context of large language models (llm).

We demonstrate that state-of-the-art state-of-the-art t5 and palm (both pretrained and finetuned) could exhibit poor controllability and robustness, which do not scale with increasing model size.

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LoRA: Low-Rank Adaptation of Large Language Models

The dominant paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train larger models, conventio

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MathPrompter: Mathematical Reasoning using Large Language Models

Large Language Models (LLMs) have limited performance when solving arithmetic reasoning tasks and often provide incorrect answers. Unlike natural language understanding, math problems typically have a

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Training Compute-Optimal Large Language Models

We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertra

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Sentence Simplification via Large Language Models

Sentence Simplification aims to rephrase complex sentences into simpler sentences while retaining original meaning. Large Language models (LLMs) have demonstrated the ability to perform a variety of n

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