7,969 research outputs found
A Review of Verbal and Non-Verbal Human-Robot Interactive Communication
In this paper, an overview of human-robot interactive communication is
presented, covering verbal as well as non-verbal aspects of human-robot
interaction. Following a historical introduction, and motivation towards fluid
human-robot communication, ten desiderata are proposed, which provide an
organizational axis both of recent as well as of future research on human-robot
communication. Then, the ten desiderata are examined in detail, culminating to
a unifying discussion, and a forward-looking conclusion
Using ChatGPT and other LLMs in Professional Environments
Large language models like ChatGPT, Google’s Bard, and Microsoft’s new Bing, to name a few, are developing rapidly in recent years, becoming very popular in different environments, and supporting a wide range of tasks. A deep look into their outcomes reveals several limitations and challenges that can be further improved. The main challenge of these models is the possibility of generating biased or inaccurate results, since these models rely on large amounts of data with no access to unpublic information. Moreover, these language models need to be properly monitored and trained to prevent generating inappropriate or offensive content and to ensure that they are used ethically and safely. This study investigates the use of ChatGPT and other large language models such as Blender, and BERT in professional environments. It has been found that none of the large language models, including ChatGPT, have been used in unstructured dialogues. Moreover, involving the models in professional environments requires extensive training and monitoring by domain professionals or fine-tuning through API
Ziya-Visual: Bilingual Large Vision-Language Model via Multi-Task Instruction Tuning
Recent advancements enlarge the capabilities of large language models (LLMs)
in zero-shot image-to-text generation and understanding by integrating
multi-modal inputs. However, such success is typically limited to English
scenarios due to the lack of large-scale and high-quality non-English
multi-modal resources, making it extremely difficult to establish competitive
counterparts in other languages. In this paper, we introduce the Ziya-Visual
series, a set of bilingual large-scale vision-language models (LVLMs) designed
to incorporate visual semantics into LLM for multi-modal dialogue. Composed of
Ziya-Visual-Base and Ziya-Visual-Chat, our models adopt the Querying
Transformer from BLIP-2, further exploring the assistance of optimization
schemes such as instruction tuning, multi-stage training and low-rank
adaptation module for visual-language alignment. In addition, we stimulate the
understanding ability of GPT-4 in multi-modal scenarios, translating our
gathered English image-text datasets into Chinese and generating
instruction-response through the in-context learning method. The experiment
results demonstrate that compared to the existing LVLMs, Ziya-Visual achieves
competitive performance across a wide range of English-only tasks including
zero-shot image-text retrieval, image captioning, and visual question
answering. The evaluation leaderboard accessed by GPT-4 also indicates that our
models possess satisfactory image-text understanding and generation
capabilities in Chinese multi-modal scenario dialogues. Code, demo and models
are available at
~\url{https://huggingface.co/IDEA-CCNL/Ziya-BLIP2-14B-Visual-v1}
IndoToD: A Multi-Domain Indonesian Benchmark For End-to-End Task-Oriented Dialogue Systems
Task-oriented dialogue (ToD) systems have been mostly created for
high-resource languages, such as English and Chinese. However, there is a need
to develop ToD systems for other regional or local languages to broaden their
ability to comprehend the dialogue contexts in various languages. This paper
introduces IndoToD, an end-to-end multi domain ToD benchmark in Indonesian. We
extend two English ToD datasets to Indonesian, comprising four different
domains by delexicalization to efficiently reduce the size of annotations. To
ensure a high-quality data collection, we hire native speakers to manually
translate the dialogues. Along with the original English datasets, these new
Indonesian datasets serve as an effective benchmark for evaluating Indonesian
and English ToD systems as well as exploring the potential benefits of
cross-lingual and bilingual transfer learning approaches.Comment: 2023 1st Workshop in South East Asian Language Processing (SEALP),
Co-located with AACL 202
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