14 research outputs found
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 applications to automation of
web-based tasks, crawling, and browser-assisted retrieval -- have not been
fully explored. We contribute HTML understanding models (fine-tuned LLMs) and
an in-depth analysis of their capabilities under three tasks: (i) Semantic
Classification of HTML elements, (ii) Description Generation for HTML inputs,
and (iii) Autonomous Web Navigation of HTML pages. While previous work has
developed dedicated architectures and training procedures for HTML
understanding, we show that LLMs pretrained on standard natural language
corpora transfer remarkably well to HTML understanding tasks. For instance,
fine-tuned LLMs are 12% more accurate at semantic classification compared to
models trained exclusively on the task dataset. Moreover, when fine-tuned on
data from the MiniWoB benchmark, LLMs successfully complete 50% more tasks
using 192x less data compared to the previous best supervised model. Out of the
LLMs we evaluate, we show evidence that T5-based models are ideal due to their
bidirectional encoder-decoder architecture. To promote further research on LLMs
for HTML understanding, we create and open-source a large-scale HTML dataset
distilled and auto-labeled from CommonCrawl
Large Language Models Encode Clinical Knowledge
Large language models (LLMs) 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. There
is no standard to evaluate model predictions and reasoning across a breadth of
tasks. To address this, we present MultiMedQA, a benchmark combining six
existing open question answering datasets spanning professional medical exams,
research, and consumer queries; and HealthSearchQA, a new free-response dataset
of medical questions searched online. We propose a framework for human
evaluation of model answers along multiple axes including factuality,
precision, possible harm, and bias. In addition, we evaluate PaLM (a
540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on
MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves
state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA,
MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US
Medical License Exam questions), surpassing prior state-of-the-art by over 17%.
However, human evaluation reveals key gaps in Flan-PaLM responses. To resolve
this we introduce instruction prompt tuning, a parameter-efficient approach for
aligning LLMs to new domains using a few exemplars. The resulting model,
Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show
that comprehension, recall of knowledge, and medical reasoning improve with
model scale and instruction prompt tuning, suggesting the potential utility of
LLMs in medicine. Our human evaluations reveal important limitations of today's
models, reinforcing the importance of both evaluation frameworks and method
development in creating safe, helpful LLM models for clinical applications
Towards Generalist Biomedical AI
Medicine is inherently multimodal, with rich data modalities spanning text,
imaging, genomics, and more. Generalist biomedical artificial intelligence (AI)
systems that flexibly encode, integrate, and interpret this data at scale can
potentially enable impactful applications ranging from scientific discovery to
care delivery. To enable the development of these models, we first curate
MultiMedBench, a new multimodal biomedical benchmark. MultiMedBench encompasses
14 diverse tasks such as medical question answering, mammography and
dermatology image interpretation, radiology report generation and
summarization, and genomic variant calling. We then introduce Med-PaLM
Multimodal (Med-PaLM M), our proof of concept for a generalist biomedical AI
system. Med-PaLM M is a large multimodal generative model that flexibly encodes
and interprets biomedical data including clinical language, imaging, and
genomics with the same set of model weights. Med-PaLM M reaches performance
competitive with or exceeding the state of the art on all MultiMedBench tasks,
often surpassing specialist models by a wide margin. We also report examples of
zero-shot generalization to novel medical concepts and tasks, positive transfer
learning across tasks, and emergent zero-shot medical reasoning. To further
probe the capabilities and limitations of Med-PaLM M, we conduct a radiologist
evaluation of model-generated (and human) chest X-ray reports and observe
encouraging performance across model scales. In a side-by-side ranking on 246
retrospective chest X-rays, clinicians express a pairwise preference for
Med-PaLM M reports over those produced by radiologists in up to 40.50% of
cases, suggesting potential clinical utility. While considerable work is needed
to validate these models in real-world use cases, our results represent a
milestone towards the development of generalist biomedical AI systems
PaLM: Scaling Language Modeling with Pathways
Large language models have been shown to achieve remarkable performance
across a variety of natural language tasks using few-shot learning, which
drastically reduces the number of task-specific training examples needed to
adapt the model to a particular application. To further our understanding of
the impact of scale on few-shot learning, we trained a 540-billion parameter,
densely activated, Transformer language model, which we call Pathways Language
Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML
system which enables highly efficient training across multiple TPU Pods. We
demonstrate continued benefits of scaling by achieving state-of-the-art
few-shot learning results on hundreds of language understanding and generation
benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough
performance, outperforming the finetuned state-of-the-art on a suite of
multi-step reasoning tasks, and outperforming average human performance on the
recently released BIG-bench benchmark. A significant number of BIG-bench tasks
showed discontinuous improvements from model scale, meaning that performance
steeply increased as we scaled to our largest model. PaLM also has strong
capabilities in multilingual tasks and source code generation, which we
demonstrate on a wide array of benchmarks. We additionally provide a
comprehensive analysis on bias and toxicity, and study the extent of training
data memorization with respect to model scale. Finally, we discuss the ethical
considerations related to large language models and discuss potential
mitigation strategies
PaLM 2 Technical Report
We introduce PaLM 2, a new state-of-the-art language model that has better
multilingual and reasoning capabilities and is more compute-efficient than its
predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture
of objectives. Through extensive evaluations on English and multilingual
language, and reasoning tasks, we demonstrate that PaLM 2 has significantly
improved quality on downstream tasks across different model sizes, while
simultaneously exhibiting faster and more efficient inference compared to PaLM.
This improved efficiency enables broader deployment while also allowing the
model to respond faster, for a more natural pace of interaction. PaLM 2
demonstrates robust reasoning capabilities exemplified by large improvements
over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable
performance on a suite of responsible AI evaluations, and enables
inference-time control over toxicity without additional overhead or impact on
other capabilities. Overall, PaLM 2 achieves state-of-the-art performance
across a diverse set of tasks and capabilities.
When discussing the PaLM 2 family, it is important to distinguish between
pre-trained models (of various sizes), fine-tuned variants of these models, and
the user-facing products that use these models. In particular, user-facing
products typically include additional pre- and post-processing steps.
Additionally, the underlying models may evolve over time. Therefore, one should
not expect the performance of user-facing products to exactly match the results
reported in this report