24 research outputs found
Uncertainty modeling in affective computing
This disclosure describes techniques that capture the uncertainty in machine-vision based affect (emotion) perception. The techniques are capable of predicting aleatoric, epistemic, and annotation uncertainty. Measures of uncertainty are important to safety-critical and subjective assessment tasks such as those found in the perception of affective expressions
Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models
Language models learn a great quantity of factual information during
pretraining, and recent work localizes this information to specific model
weights like mid-layer MLP weights. In this paper, we find that we can change
how a fact is stored in a model by editing weights that are in a different
location than where existing methods suggest that the fact is stored. This is
surprising because we would expect that localizing facts to specific model
parameters would tell us where to manipulate knowledge in models, and this
assumption has motivated past work on model editing methods. Specifically, we
show that localization conclusions from representation denoising (also known as
Causal Tracing) do not provide any insight into which model MLP layer would be
best to edit in order to override an existing stored fact with a new one. This
finding raises questions about how past work relies on Causal Tracing to select
which model layers to edit. Next, we consider several variants of the editing
problem, including erasing and amplifying facts. For one of our editing
problems, editing performance does relate to localization results from
representation denoising, but we find that which layer we edit is a far better
predictor of performance. Our results suggest, counterintuitively, that better
mechanistic understanding of how pretrained language models work may not always
translate to insights about how to best change their behavior. Our code is
available at https://github.com/google/belief-localizationComment: NeurIPS 2023 (Spotlight). 26 pages, 22 figure
Engineering Music to Slow Breathing and Invite Relaxed Physiology
We engineered an interactive music system that influences a user's breathing
rate to induce a relaxation response. This system generates ambient music
containing periodic shifts in loudness that are determined by the user's own
breathing patterns. We evaluated the efficacy of this music intervention for
participants who were engaged in an attention-demanding task, and thus
explicitly not focusing on their breathing or on listening to the music. We
measured breathing patterns in addition to multiple peripheral and cortical
indicators of physiological arousal while users experienced three different
interaction designs: (1) a "Fixed Tempo" amplitude modulation rate at six beats
per minute; (2) a "Personalized Tempo" modulation rate fixed at 75\% of each
individual's breathing rate baseline, and (3) a "Personalized Envelope" design
in which the amplitude modulation matches each individual's breathing pattern
in real-time. Our results revealed that each interactive music design slowed
down breathing rates, with the "Personalized Tempo" design having the largest
effect, one that was more significant than the non-personalized design. The
physiological arousal indicators (electrodermal activity, heart rate, and slow
cortical potentials measured in EEG) showed concomitant reductions, suggesting
that slowing users' breathing rates shifted them towards a more calmed state.
These results suggest that interactive music incorporating biometric data may
have greater effects on physiology than traditional recorded music.Comment: Accepted at 2019 8th International Conference on Affective Computing
and Intelligent Interaction (ACII
Hierarchical Reinforcement Learning for Open-Domain Dialog
Open-domain dialog generation is a challenging problem; maximum likelihood
training can lead to repetitive outputs, models have difficulty tracking
long-term conversational goals, and training on standard movie or online
datasets may lead to the generation of inappropriate, biased, or offensive
text. Reinforcement Learning (RL) is a powerful framework that could
potentially address these issues, for example by allowing a dialog model to
optimize for reducing toxicity and repetitiveness. However, previous approaches
which apply RL to open-domain dialog generation do so at the word level, making
it difficult for the model to learn proper credit assignment for long-term
conversational rewards. In this paper, we propose a novel approach to
hierarchical reinforcement learning, VHRL, which uses policy gradients to tune
the utterance-level embedding of a variational sequence model. This
hierarchical approach provides greater flexibility for learning long-term,
conversational rewards. We use self-play and RL to optimize for a set of
human-centered conversation metrics, and show that our approach provides
significant improvements -- in terms of both human evaluation and automatic
metrics -- over state-of-the-art dialog models, including Transformers
Post Hoc Explanations of Language Models Can Improve Language Models
Large Language Models (LLMs) have demonstrated remarkable capabilities in
performing complex tasks. Moreover, recent research has shown that
incorporating human-annotated rationales (e.g., Chain-of- Thought prompting)
during in-context learning can significantly enhance the performance of these
models, particularly on tasks that require reasoning capabilities. However,
incorporating such rationales poses challenges in terms of scalability as this
requires a high degree of human involvement. In this work, we present a novel
framework, Amplifying Model Performance by Leveraging In-Context Learning with
Post Hoc Explanations (AMPLIFY), which addresses the aforementioned challenges
by automating the process of rationale generation. To this end, we leverage
post hoc explanation methods which output attribution scores (explanations)
capturing the influence of each of the input features on model predictions.
More specifically, we construct automated natural language rationales that
embed insights from post hoc explanations to provide corrective signals to
LLMs. Extensive experimentation with real-world datasets demonstrates that our
framework, AMPLIFY, leads to prediction accuracy improvements of about 10-25%
over a wide range of tasks, including those where prior approaches which rely
on human-annotated rationales such as Chain-of-Thought prompting fall short.
Our work makes one of the first attempts at highlighting the potential of post
hoc explanations as valuable tools for enhancing the effectiveness of LLMs.
Furthermore, we conduct additional empirical analyses and ablation studies to
demonstrate the impact of each of the components of AMPLIFY, which, in turn,
lead to critical insights for refining in-context learning
“Kind and Grateful”: A Context-Sensitive Smartphone App Utilizing Inspirational Content to Promote Gratitude
Background
Previous research has shown that gratitude positively influences psychological wellbeing and physical health. Grateful people are reported to feel more optimistic and happy, to better mitigate aversive experiences, and to have stronger interpersonal bonds. Gratitude interventions have been shown to result in improved sleep, more frequent exercise and stronger cardiovascular and immune systems. These findings call for the development of technologies that would inspire gratitude. This paper presents a novel system designed toward this end.
Methods
We leverage pervasive technologies to naturally embed inspiration to express gratitude in everyday life. Novel to this work, mobile sensor data is utilized to infer optimal moments for stimulating contextually relevant thankfulness and appreciation. Sporadic mood measurements are inventively obtained through the smartphone lock screen, investigating their interplay with grateful expressions. Both momentary thankful emotion and dispositional gratitude are measured. To evaluate our system, we ran two rounds of randomized control trials (RCT), including a pilot study (N = 15, 2 weeks) and a main study (N = 27, 5 weeks). Studies’ participants were provided with a newly developed smartphone app through which they were asked to express gratitude; the app displayed inspirational content to only the intervention group, while measuring contextual cues for all users.
Results
In both rounds of the RCT, the intervention was associated with improved thankful behavior. Significant increase was observed in multiple facets of practicing gratitude in the intervention groups. The average frequency of practicing thankfulness increased by more than 120 %, comparing the baseline weeks with the intervention weeks of the main study. In contrast, the control group of the same study exhibited a decrease of 90 % in the frequency of thankful expressions. In the course of the study’s 5 weeks, increases in dispositional gratitude and in psychological wellbeing were also apparent. Analyzing the relation between mood and gratitude expressions, our data suggest that practicing gratitude increases the probability of going up in terms of emotional valence and down in terms of emotional arousal. The influences of inspirational content and contextual cues on promoting thankful behavior were also analyzed: We present data suggesting that the more successful times for eliciting expressions of gratitude tend to be shortly after a social experience, shortly after location change, and shortly after physical activity.
Conclusions
The results support our intervention as an impactful method to promote grateful affect and behavior. Moreover, they provide insights into design and evaluation of general behavioral intervention technologies.Robert Wood Johnson FoundationMIT Media Lab Consortiu