6 research outputs found
In-Context Learning in Large Language Models Learns Label Relationships but Is Not Conventional Learning
The performance of Large Language Models (LLMs) on downstream tasks often
improves significantly when including examples of the input-label relationship
in the context. However, there is currently no consensus about how this
in-context learning (ICL) ability of LLMs works: for example, while Xie et al.
(2021) liken ICL to a general-purpose learning algorithm, Min et al. (2022b)
argue ICL does not even learn label relationships from in-context examples. In
this paper, we study (1) how labels of in-context examples affect predictions,
(2) how label relationships learned during pre-training interact with
input-label examples provided in-context, and (3) how ICL aggregates label
information across in-context examples. Our findings suggests LLMs usually
incorporate information from in-context labels, but that pre-training and
in-context label relationships are treated differently, and that the model does
not consider all in-context information equally. Our results give insights into
understanding and aligning LLM behavior
Detecting hallucinations in large language models using semantic entropy
Large language model (LLM) systems, such as ChatGPT1 or Gemini2, can show impressive reasoning and question-answering capabilities but often ‘hallucinate’ false outputs and unsubstantiated answers3, 4. Answering unreliably or without the necessary information prevents adoption in diverse fields, with problems including fabrication of legal precedents5 or untrue facts in news articles6 and even posing a risk to human life in medical domains such as radiology7. Encouraging truthfulness through supervision or reinforcement has been only partially successful8. Researchers need a general method for detecting hallucinations in LLMs that works even with new and unseen questions to which humans might not know the answer. Here we develop new methods grounded in statistics, proposing entropy-based uncertainty estimators for LLMs to detect a subset of hallucinations—confabulations—which are arbitrary and incorrect generations. Our method addresses the fact that one idea can be expressed in many ways by computing uncertainty at the level of meaning rather than specific sequences of words. Our method works across datasets and tasks without a priori knowledge of the task, requires no task-specific data and robustly generalizes to new tasks not seen before. By detecting when a prompt is likely to produce a confabulation, our method helps users understand when they must take extra care with LLMs and opens up new possibilities for using LLMs that are otherwise prevented by their unreliability
Three Towers: Flexible Contrastive Learning with Pretrained Image Models
We introduce Three Towers (3T), a flexible method to improve the contrastive
learning of vision-language models by incorporating pretrained image
classifiers. While contrastive models are usually trained from scratch, LiT
(Zhai et al., 2022) has recently shown performance gains from using pretrained
classifier embeddings. However, LiT directly replaces the image tower with the
frozen embeddings, excluding any potential benefits of contrastively training
the image tower. With 3T, we propose a more flexible strategy that allows the
image tower to benefit from both pretrained embeddings and contrastive
training. To achieve this, we introduce a third tower that contains the frozen
pretrained embeddings, and we encourage alignment between this third tower and
the main image-text towers. Empirically, 3T consistently improves over LiT and
the CLIP-style from-scratch baseline for retrieval tasks. For classification,
3T reliably improves over the from-scratch baseline, and while it underperforms
relative to LiT for JFT-pretrained models, it outperforms LiT for ImageNet-21k
and Places365 pretraining
Global green hydrogen-based steel opportunities surrounding high quality renewable energy and iron ore deposits
Abstract The steel sector currently accounts for 7% of global energy-related CO2 emissions and requires deep reform to disconnect from fossil fuels. Here, we investigate the market competitiveness of one of the widely considered decarbonisation routes for primary steel production: green hydrogen-based direct reduction of iron ore followed by electric arc furnace steelmaking. Through analysing over 300 locations by combined use of optimisation and machine learning, we show that competitive renewables-based steel production is located nearby the tropic of Capricorn and Cancer, characterised by superior solar with supplementary onshore wind, in addition to high-quality iron ore and low steelworker wages. If coking coal prices remain high, fossil-free steel could attain competitiveness in favourable locations from 2030, further improving towards 2050. Large-scale implementation requires attention to the abundance of suitable iron ore and other resources such as land and water, technical challenges associated with direct reduction, and future supply chain configuration
Structured Object-Aware Physics Prediction for Video Modeling and Planning
When humans observe a physical system, they can easily locate objects,
understand their interactions, and anticipate future behavior, even in settings
with complicated and previously unseen interactions. For computers, however,
learning such models from videos in an unsupervised fashion is an unsolved
research problem. In this paper, we present STOVE, a novel state-space model
for videos, which explicitly reasons about objects and their positions,
velocities, and interactions. It is constructed by combining an image model and
a dynamics model in compositional manner and improves on previous work by
reusing the dynamics model for inference, accelerating and regularizing
training. STOVE predicts videos with convincing physical behavior over hundreds
of timesteps, outperforms previous unsupervised models, and even approaches the
performance of supervised baselines. We further demonstrate the strength of our
model as a simulator for sample efficient model-based control in a task with
heavily interacting objects.Comment: Published as a conference paper at 2020 International Conference for
Learning Representation