19 research outputs found
Know your audience: specializing grounded language models with listener subtraction
Effective communication requires adapting to the idiosyncrasies of each
communicative context--such as the common ground shared with each partner.
Humans demonstrate this ability to specialize to their audience in many
contexts, such as the popular game Dixit. We take inspiration from Dixit to
formulate a multi-agent image reference game where a (trained) speaker model is
rewarded for describing a target image such that one (pretrained) listener
model can correctly identify it among distractors, but another listener cannot.
To adapt, the speaker must exploit differences in the knowledge it shares with
the different listeners. We show that finetuning an attention-based adapter
between a CLIP vision encoder and a large language model in this contrastive,
multi-agent setting gives rise to context-dependent natural language
specialization from rewards only, without direct supervision. Through
controlled experiments, we show that training a speaker with two listeners that
perceive differently, using our method, allows the speaker to adapt to the
idiosyncracies of the listeners. Furthermore, we show zero-shot transfer of the
specialization to real-world data. Our experiments demonstrate a method for
specializing grounded language models without direct supervision and highlight
the interesting research challenges posed by complex multi-agent communication.Comment: 28 pages, 9 figure
Know your audience: specializing grounded language models with listener subtraction
Effective communication requires adapting
to the idiosyncrasies of each communicative
context—such as the common ground shared
with each partner. Humans demonstrate this
ability to specialize to their audience in many
contexts, such as the popular game Dixit. We
take inspiration from Dixit to formulate a multiagent image reference game where a (trained)
speaker model is rewarded for describing a target image such that one (pretrained) listener
model can correctly identify it among distractors, but another listener cannot. To adapt, the
speaker must exploit differences in the knowledge it shares with the different listeners. We
show that finetuning an attention-based adapter
between a CLIP vision encoder and a large language model in this contrastive, multi-agent
setting gives rise to context-dependent natural language specialization from rewards only,
without direct supervision. Through controlled experiments, we show that training a speaker with two listeners that perceive differently, using our method, allows the speaker to adapt to the idiosyncracies of the listeners. Furthermore, we show zero-shot transfer of the specialization to real-world data. Our experiments demonstrate a method for specializing grounded language models without direct supervision and highlight the interesting research challenges posed by complex multi-agent communicatio
Evaluating Spatial Understanding of Large Language Models
Large language models (LLMs) show remarkable capabilities across a variety of
tasks. Despite the models only seeing text in training, several recent studies
suggest that LLM representations implicitly capture aspects of the underlying
grounded concepts. Here, we explore LLM representations of a particularly
salient kind of grounded knowledge -- spatial relationships. We design
natural-language navigation tasks and evaluate the ability of LLMs, in
particular GPT-3.5-turbo, GPT-4, and Llama2 series models, to represent and
reason about spatial structures, and compare these abilities to human
performance on the same tasks. These tasks reveal substantial variability in
LLM performance across different spatial structures, including square,
hexagonal, and triangular grids, rings, and trees. We also discover that,
similar to humans, LLMs utilize object names as landmarks for maintaining
spatial maps. Finally, in extensive error analysis, we find that LLMs' mistakes
reflect both spatial and non-spatial factors. These findings suggest that LLMs
appear to capture certain aspects of spatial structure implicitly, but room for
improvement remains
Improving neural network representations using human similarity judgments
Deep neural networks have reached human-level performance on many computer
vision tasks. However, the objectives used to train these networks enforce only
that similar images are embedded at similar locations in the representation
space, and do not directly constrain the global structure of the resulting
space. Here, we explore the impact of supervising this global structure by
linearly aligning it with human similarity judgments. We find that a naive
approach leads to large changes in local representational structure that harm
downstream performance. Thus, we propose a novel method that aligns the global
structure of representations while preserving their local structure. This
global-local transform considerably improves accuracy across a variety of
few-shot learning and anomaly detection tasks. Our results indicate that human
visual representations are globally organized in a way that facilitates
learning from few examples, and incorporating this global structure into neural
network representations improves performance on downstream tasks.Comment: Published as a conference paper at NeurIPS 202
Language models show human-like content effects on reasoning
Abstract reasoning is a key ability for an intelligent system. Large language
models achieve above-chance performance on abstract reasoning tasks, but
exhibit many imperfections. However, human abstract reasoning is also
imperfect, and depends on our knowledge and beliefs about the content of the
reasoning problem. For example, humans reason much more reliably about logical
rules that are grounded in everyday situations than arbitrary rules about
abstract attributes. The training experiences of language models similarly
endow them with prior expectations that reflect human knowledge and beliefs. We
therefore hypothesized that language models would show human-like content
effects on abstract reasoning problems. We explored this hypothesis across
three logical reasoning tasks: natural language inference, judging the logical
validity of syllogisms, and the Wason selection task (Wason, 1968). We find
that state of the art large language models (with 7 or 70 billion parameters;
Hoffman et al., 2022) reflect many of the same patterns observed in humans
across these tasks -- like humans, models reason more effectively about
believable situations than unrealistic or abstract ones. Our findings have
implications for understanding both these cognitive effects, and the factors
that contribute to language model performance
Can language models learn from explanations in context?
Large language models can perform new tasks by adapting to a few in-context
examples. For humans, rapid learning from examples can benefit from
explanations that connect examples to task principles. We therefore investigate
whether explanations of few-shot examples can allow language models to adapt
more effectively. We annotate a set of 40 challenging tasks from BIG-Bench with
explanations of answers to a small subset of questions, as well as a variety of
matched control explanations. We evaluate the effects of various zero-shot and
few-shot prompts that include different types of explanations, instructions,
and controls on the performance of a range of large language models. We analyze
these results using statistical multilevel modeling techniques that account for
the nested dependencies among conditions, tasks, prompts, and models. We find
that explanations of examples can improve performance. Adding untuned
explanations to a few-shot prompt offers a modest improvement in performance;
about 1/3 the effect size of adding few-shot examples, but twice the effect
size of task instructions. We then show that explanations tuned for performance
on a small validation set offer substantially larger benefits; building a
prompt by selecting examples and explanations together substantially improves
performance over selecting examples alone. Hand-tuning explanations can
substantially improve performance on challenging tasks. Furthermore, even
untuned explanations outperform carefully matched controls, suggesting that the
benefits are due to the link between an example and its explanation, rather
than lower-level features of the language used. However, only large models can
benefit from explanations. In summary, explanations can support the in-context
learning abilities of large language models o