Thailand

The main challenge in learning image-conditioned robotic policies is acquiring a visual representation conducive to low-level control. Due to the high dimensionality of the image space, learning a good visual representation requires a considerable amount of visual data. However, when learning in the real world, data is expensive. Sim2Real is a promising paradigm for overcoming data scarcity in the real-world target domain by using a simulator to collect large amounts of cheap data closely related to the target task. However, it is difficult to transfer an image-conditioned policy from sim to real when the domains are very visually dissimilar. To bridge the sim2real visual gap, we propose using natural language descriptions of images as a unifying signal across domains that captures the underlying task-relevant semantics. Our key insight is that if two image observations from different domains are labeled with similar language, the policy should predict similar action distributions for both images. We demonstrate that training the image encoder to predict the language description or the distance between descriptions of a sim or real image serves as a useful, data-efficient pretraining step that helps learn a domain-invariant image representation. We can then use this image encoder as the backbone of an IL policy trained simultaneously on a large amount of simulated and a handful of real demonstrations. Our approach outperforms widely used prior sim2real methods and strong vision-language pretraining baselines by 25 to 40%. See additional videos and materials at https://robin-lab.cs.utexas.edu/lang4sim2real/.

ACL 2024

Natural Language Can Facilitate Sim2Real Transfer

sim2real

robot learning

imitation learning

representation learning

natural language

[![](https://assets.underline.io/uploads/markdown_image/1/image/21d13e03491d7e9acf6b8c46c7979c25.png)](https://docs.google.com/spreadsheets/d/1SCDDfkQk7es4J6RULKHVx1RzbB0Mutla--Sle5SVfb0/edit?usp=sharing)

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RoboCup 2021

The 24th RoboCup International Symposium will be held in conjunction with RoboCup 2021 in a purely online setting. Scientific papers will be presented reporting innovative, original research with relevance to robotics and artificial intelligence. Within the described scope of topics we also encouraged submissions of high-quality overview articles, papers describing real-world research, and papers reporting theoretical results. In addition to the main track with regular research papers, there will be a special track focused on systems, data, and benchmarks featuring papers describing novel, open-source hardware and software systems, as well as datasets and benchmarks relevant to the community. In addition to research paper presentations, the RoboCup Symposium 2021 includes keynote talks by Dieter Fox, Jean-Paul Laumond, and Stefanie Tellex, and several interactive sessions.

workshop paper

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Please register!

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

Verbs describe the dynamics of interactions between people, objects, and their environments. They play a crucial role in language formation and understanding. Nonetheless, recent vision-language models like CLIP predominantly rely on nouns and have a limited account of verbs. This limitation affects their performance in tasks requiring action recognition and scene understanding. In this work, we introduce VerbCLIP, a verb-centric vision-language model which learns meanings of verbs based on a compositional approach to statistical machine learning. Our methods significantly outperform CLIP in zero-shot performance on the VALSE, VL-Checklist, and SVO-Probes datasets, with improvements of +2.38%, +3.14%, and +1.47%, without fine-tuning. Fine-tuning resulted in further improvements, with gains of +2.85% and +9.2% on the VALSE and VL-Checklist datasets.

VerbCLIP: Improving Verb Understanding in Vision-Language Models with Compositional Structures

As the scaling of Large Language Models (LLMs) has dramatically enhanced their capabilities, there has been a growing focus on the alignment problem to ensure their responsible and ethical use. While existing alignment efforts predominantly concentrate on universal values such as the HHH principle, the aspect of culture, which is inherently pluralistic and diverse, has not received adequate attention. This work introduces a new benchmark, CDEval, aimed at evaluating the cultural dimensions of LLMs. CDEval is constructed by incorporating both GPT-4's automated generation and human verification, covering six cultural dimensions across seven domains. Our comprehensive experiments provide intriguing insights into the culture of mainstream LLMs, highlighting both consistencies and variations across different dimensions and domains. The findings underscore the importance of integrating cultural considerations in LLM development, particularly for applications in diverse cultural settings. This benchmark serves as a valuable resource for cultural studies in LLMs, paving the way for more culturally aware and sensitive models.

CDEval: A Benchmark for Measuring the Cultural Dimensions of Large Language Models

This study explores the sources of instability in maintaining cultural personas and opinions within multi-agent LLM systems. Drawing on simulations of inter-cultural collaboration and debate, we analyze agents' pre- and post-discussion private responses alongside chat transcripts to assess the stability of cultural personas and the impact of opinion diversity on group outcomes. Our findings suggest that multi-agent discussions can encourage collective decisions that reflect diverse perspectives, yet this benefit is tempered by the agents' susceptibility to conformity due to perceived peer pressure and challenges in maintaining consistent personas and opinions. Counterintuitively, instructions that encourage debate in support of one's opinions increase the rate of instability. Without addressing the factors we identify, the full potential of multi-agent frameworks for producing more culturally diverse AI outputs will remain untapped.

Conformity, Confabulation, and Impersonation: Persona Inconstancy in Multi-Agent LLM Collaboration

The difference in culture between the U.S. and Japan is a popular subject for Western vs. Eastern cultural comparison for researchers. One particular challenge is to obtain and annotate multilingual datasets. In this study, we utilized COVID-19 tweets from the two countries as a case study, focusing particularly on discussions concerning masks. The annotation task was designed to gain insights into societal attitudes toward the mask policies implemented in both countries. The aim of this study is to provide a practical approach for the annotation task by thoroughly documenting how we aligned the multilingual annotation guidelines to obtain a comparable dataset. We proceeded to document the effective practices during our annotation process to synchronize our multilingual guidelines. Furthermore, we discussed difficulties caused by differences in expression style and culture, and potential strategies that helped improve our agreement scores and reduce discrepancies between the annotation results in both languages. These findings offer an alternative method for synchronizing multilingual annotation guidelines and achieving feasible agreement scores for cross-cultural annotation tasks. This study resulted in a multilingual guideline in English and Japanese to annotate topics related to public discourses about COVID-19 masks in the U.S. and Japan.

Synchronizing Approach in Designing Annotation Guidelines for Multilingual Datasets: A COVID-19 Case Study Using English and Japanese Tweets

Large language models (LLMs) have rapidly evolved as the foundation of various natural language processing (NLP) applications. Despite their wide use cases, their understanding of culturally-related concepts and reasoning remains limited. Meantime, there is a significant need to enhance these models' cultural reasoning capabilities, especially concerning underrepresented regions. This paper introduces a novel pipeline for extracting high-quality, culturally-related instruction tuning datasets from vast unstructured corpora. We utilize a self-instruction generation pipeline to identify cultural concepts and trigger instruction. By integrating with a general-purpose instruction tuning dataset, our model demonstrates enhanced capabilities in recognizing and understanding regional cultural nuances, thereby enhancing its reasoning capabilities. We conduct experiments across three regions: Singapore, the Philippines, and the United States, achieving performance improvement of up to 6%. Our research opens new avenues for extracting cultural instruction tuning sets directly from unstructured data, setting a precedent for future innovations in the field.

CRAFT: Extracting and Tuning Cultural Instructions from the Wild

Alignment of the language model with human preferences is a common approach to making a language model useful to end users.
However, most alignment work is done in English, and human preference datasets are dominated by English, reflecting only the preferences of English-speaking annotators.
Nevertheless, it is common practice to use the English preference data, either directly or by translating it into the target language, when aligning a multilingual language model.
The question is whether such an alignment strategy marginalizes the preference of non-English speaking users.
To this end, we investigate the effect of aligning Japanese language models with (mostly) English resources.
In particular, we focus on evaluating whether the commonsense morality of the resulting fine-tuned models is aligned with Japanese culture using the JCommonsenseMorality (JCM) and ETHICS datasets.
The experimental results show that the fine-tuned model outperforms the SFT model. However, it does not demonstrate the same level of improvement as a model fine-tuned using the JCM, suggesting that while some aspects of commonsense morality are transferable, others may not be.

Does Cross-Cultural Alignment Change the Commonsense Morality of Language Models?

Integrating Large Language Models (LLMs) into various global cultures presents a cultural challenge: they must respect social norms, and avoid transgressing cultural boundaries. This calls for LLMs to be able to $\textit{adapt}$ their outputs to diverse cultural norms. However, the extent of this $\textit{cultural adaptability}$ remains unclear. To this end, we introduce $NormAd$, a dataset of 2.6k stories that represent social and cultural norms from 75 countries to assess the ability of LLMs to adapt to different granular contexts. Evaluation on $NormAd$ suggests that LLMs struggle with cultural reasoning across all contextual granularities -- Mistral-7b-Instruct, one of the top performing models, achieves only 81.8\% accuracy versus 95.6\% achieved by humans. Additionally, LLMs show stronger adaptability to English-centric cultures over those from the Global South, and find it considerably easier to assess the social acceptability of stories that adhere to cultural norms than those that deviate from them. Our benchmark emphasizes the potential to make models more equitable towards global audiences.

NormAd: A Benchmark for Measuring the Cultural Adaptability of Large Language Models

While preliminary findings indicate that multilingual LLMs exhibit reduced bias compared to monolingual ones, a comprehensive understanding of the effect of multilingual training on bias mitigation, is lacking. This study addresses this gap by systematically training six LLMs of identical size (2.6B parameters) and architecture: five monolingual models (English, German, French, Italian, and Spanish) and one multilingual model trained on an equal distribution of data across these languages, all using publicly available data. To ensure robust evaluation, standard bias benchmarks were automatically translated into the five target languages and verified for both translation quality and bias preservation by human annotators. Our results consistently demonstrate that multilingual training effectively mitigates bias. Moreover, we observe that multilingual models achieve not only lower bias but also superior prediction accuracy when compared to monolingual models with the same amount of training data, model architecture, and size.

Do Multilingual Large Language Models Mitigate Stereotype Bias?

Large language models (LLMs) often face knowledge conflicts when addressing questions that can yield different answers based on the cultural or regional context. In this work, we create the **QARV** benchmark to evaluate LLM's proficiency in managing these conflicts between two distinct countries, the US and Korea. The benchmark consists of 671 handwritten questions, each accompanied by two answers from each nation. Our evaluation of 42 different LLMs, each tested with nine unique prompts, reveals that most models inherently respond with an American bias. Additionally, we observe that prompting, and instruction-tuning, are all practical measures to enhance cultural alignment.

Assessing LLMs' Ability to Navigate Cultural Knowledge Conflicts

Cultural differences in common ground may result in pragmatic failure and misunderstandings. We develop our method Rational Speech Acts for Cross-Cultural Communication (RSA+C3) to resolve cross-cultural differences in common ground. To measure the success of our method, we study RSA+C3 in the collaborative referential game of Codenames Duet and show that our method successfully improves collaboration between simulated players of different cultures. Our contributions are threefold: (1) creating Codenames players using contrastive learning of an embedding space and LLM prompting that are aligned with human patterns of play, (2) studying culturally induced differences in common ground reflected in our trained models, and (3) demonstrating that our method RSA+C3 can ease cross-cultural communication in gameplay by inferring sociocultural context from interaction.

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