Understanding the Capabilities and Limitations of Large Language Models for Cultural Commonsense

Large language models (LLMs) have demonstrated substantial commonsense understanding through numerous benchmark evaluations. However, their understanding of cultural commonsense remains largely unexamined. In this paper, we conduct a comprehensive examination of the capabilities and limitations of several state-of-the-art LLMs in the context of cultural commonsense tasks. Using several general and cultural commonsense benchmarks, we find that (1) LLMs have a significant discrepancy in performance when tested on culture-specific commonsense knowledge for different cultures; (2) LLMs' general commonsense capability is affected by cultural context; and (3) The language used to query the LLMs can impact their performance on cultural-related tasks. Our study points to the inherent bias in the cultural understanding of LLMs and provides insights that can help develop culturally aware language models.

Towards Dog Bark Decoding: Leveraging Human Speech Processing for Automated Bark Classification

Similar to humans, animals make extensive use of verbal and non-verbal forms of communication, including a large range of audio signals. In this paper, we address dog vocalizations and explore the use of self-supervised speech representation models pre-trained on human speech to address dog bark classification tasks that find parallels in human-centered tasks in speech recognition. We specifically address four tasks: dog recognition, breed identification, gender classification, and context grounding. We show that using speech embedding representations significantly improves over simpler classification baselines. Further, we also find that models pre-trained on large human speech acoustics can provide additional performance boosts on several tasks.

Cooperate or Collapse: Emergence of Sustainability Behaviors in a Society of LLM Agents

In the rapidly evolving field of artificial intelligence, ensuring safe decision-making of Large Language Models (LLMs) is a significant challenge. This paper introduces Governance of the Commons Simulation (GovSim), a simulation platform designed to study strategic interactions and cooperative decision-making in LLMs. Through this simulation environment, we explore the dynamics of resource sharing among AI agents, highlighting the importance of ethical considerations, strategic planning, and negotiation skills. GovSim is versatile and supports any text-based agent, including LLMs agents. Using the Generative Agent framework, we create a standard agent that facilitates the integration of different LLMs. Our findings reveal that within GovSim, only two out of 15 tested LLMs managed to achieve a sustainable outcome, indicating a significant gap in the ability of models to manage shared resources. Furthermore, we find that by removing the ability of agents to communicate, they overuse the shared resource, highlighting the importance of communication for cooperation. Interestingly, most LLMs lack the ability to make universalized hypotheses, which highlights a significant weakness in their reasoning skills. We open source the full suite of our research results, including the simulation environment, agent prompts, and a comprehensive web interface.

Cross-cultural Inspiration Detection and Analysis in Real and LLM-generated Social Media Data

Inspiration is linked to various positive outcomes, such as increased creativity, productivity, and happiness. Although inspiration has great potential, there has been limited effort toward identifying content that is inspiring, as opposed to just engaging or positive. Additionally, most research has concentrated on Western data, with little attention paid to other cultures. This work is the first to study cross-cultural inspiration through machine learning methods. We aim to identify and analyze real and AI-generated cross-cultural inspiring posts. To this end, we compile and make publicly available the InspAIred dataset, which consists of 2,000 real inspiring posts, 2,000 real non-inspiring posts, and 2,000 generated inspiring posts evenly distributed across India and the UK. The real posts are sourced from Reddit, while the generated posts are created using the GPT-4 model. Using this dataset, we conduct extensive computational linguistic analyses to (1) compare inspiring content across cultures, (2) compare AI-generated inspiring posts to real inspiring posts, and (3) determine if detection models can accurately distinguish between inspiring content across cultures and data sources.

MAiDE-up: Multilingual Deception Detection of GPT-generated Hotel Reviews

Deceptive reviews are becoming increasingly common, especially given the increase in performance and the prevalence of LLMs. While work to date has addressed the development of models to differentiate between truthful and deceptive human reviews, much less is known about the distinction between real reviews and AI-authored fake reviews. Moreover, most of the research so far has focused primarily on English, with very little work dedicated to other languages. In this paper, we compile and make publicly available the MAiDE-up dataset, consisting of 10,000 real and 10,000 AI-generated fake hotel reviews, balanced across ten languages. Using this dataset, we conduct extensive linguistic analyses to (1) compare the AI fake hotel reviews to real hotel reviews, and (2) identify the factors that influence the deception detection model performance. We explore the effectiveness of several models for deception detection in hotel reviews across three main dimensions: sentiment, location, and language. We find that these dimensions influence how well we can detect AI-generated fake reviews.

On the Causal Nature of Sentiment Analysis

Sentiment analysis (SA) aims to identify the sentiment expressed in a text, such as a product review. Given a review and the sentiment associated with it, this paper formulates SA as a combination of two tasks: (1) a causal discovery task that distinguishes whether a review "primes" the sentiment (Causal Hypothesis C1), or the sentiment "primes" the review (Causal Hypothesis C2); and (2) the traditional prediction task to model the sentiment using the review as input. Using the peak-end rule in psychology, we classify a sample as C1 if its overall sentiment score approximates an average of all the sentence-level sentiments in the review, and C2 if the overall sentiment score approximates an average of the peak and end sentiments. For the prediction task, we use the discovered causal mechanisms behind the samples to improve the performance of LLMs by proposing causal prompts that give the models an inductive bias of the underlying causal graph, leading to substantial improvements by up to 32.13 F1 points on zero-shot five-class SA. Our code is at https://github.com/cogito233/causal-sa

Tango 2: Aligning Diffusion-based Text-to-Audio Generations through Direct Preference Optimization

Generative multimodal content is increasingly prevalent in much of the content creation arena, as it has the potential to allow artists and media personnel to create pre-production mockups by quickly bringing their ideas to life. The generation of audio from text prompts is an important aspect of such processes in the music and film industry. Many of the recent diffusion-based text-to-audio models focus on training increasingly sophisticated diffusion models on a large set of datasets of prompt-audio pairs. These models do not explicitly focus on the presence of concepts or events and their temporal ordering in the output audio with respect to the input prompt. Our hypothesis is focusing on how these aspects of audio generation could improve audio generation performance in the presence of limited data. As such, in this work, using an existing text-to-audio model Tango, we synthetically create a preference dataset where each prompt has a winner audio output and some loser audio outputs for the diffusion model to learn from. The loser outputs, in theory, have some concepts from the prompt missing or in an incorrect order. We fine-tune the publicly available Tango text-to-audio model using diffusion-DPO (direct preference optimization) loss on our preference dataset and show that it leads to improved audio output over Tango and AudioLDM2, in terms of both automatic- and manual-evaluation metrics.

The Generation Gap:Exploring Age Bias in Large Language Models

In this paper, we explore the alignment of values in Large Language Models (LLMs) with specific age groups, leveraging data from the World Value Survey across thirteen categories. Through a diverse set of prompts tailored to ensure response robustness, we find a general inclination of LLM values towards younger demographics. Additionally, we explore the impact of incorporating age identity information in prompts and observe challenges in mitigating value discrepancies with different age cohorts. Our findings highlight the age bias in LLMs and provide insights for future work.

Towards Algorithmic Fidelity: Mental Health Representation across Demographics in Synthetic vs. Human-generated Data

Synthetic data generation has the potential to impact applications and domains with scarce data. However, before such data is used for sensitive tasks such as mental health, we need an understanding of how different demographics are represented in it. In our paper, we analyze the potential of producing synthetic data using GPT-3 by exploring the various stressors it attributes to different race and gender combinations, to provide insight for future researchers looking into using LLMs for data generation. Using GPT-3, we develop HEADROOM, a synthetic dataset of 3,120 posts about depression-triggering stressors, by controlling for race, gender, and time frame (before and after COVID-19). Using this dataset, we conduct semantic and lexical analyses to (1) identify the predominant stressors for each demographic group; and (2) compare our synthetic data to a human-generated dataset. We present the procedures to generate queries to develop depression data using GPT-3, and conduct analyzes to uncover the types of stressors it assigns to demographic groups, which could be used to test the limitations of LLMs for synthetic data generation for depression data. Our findings show that synthetic data mimics some of the human-generated data distribution for the predominant depression stressors across diverse demographics.

Dynamic Reward Adjustment in Multi-Reward Reinforcement Learning for Counselor Reflection Generation

In this paper, we study the problem of multi-reward reinforcement learning to jointly optimize for multiple text qualities for natural language generation. We focus on the task of counselor reflection generation, where we optimize the generators to simultaneously improve the fluency, coherence, and reflection quality of generated counselor responses. We introduce two novel bandit methods, DynaOpt and C-DynaOpt, which rely on the broad strategy of combining rewards into a single value and optimizing them simultaneously. Specifically, we employ non-contextual and contextual multi-arm bandits to dynamically adjust multiple reward weights during training. Through automatic and manual evaluations, we show that our proposed techniques, DynaOpt and C-DynaOpt, outperform existing naive and bandit baselines, showcasing their potential for enhancing language models.