Company: ILLRW
Filing Date: 2025-01-24
Form Type: S-1
Source: 0001213900-25-006210
Chunk: 229

Company: Triller Group Inc.
Filing Date: 2025-01-24
Form: S-1
Chunk 229
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 text and data. Natural language recognition and natural language generation are types of NLP.
By giving each Creator and Brand an AI-powered “factory of assistants” to help them identify superfans, up-and-comers, key
topics and trends to respond to (while filtering out spam, hate- speech and noise), they are better able to deepen relationships and loyalty,
optimize their scarce time and resources, and ultimately increase conversions and monetization through a mix of brand partnerships and
direct commerce.

For our LLMs, we currently use a mix of open source
code for embeddings (for example, open source code such as SBERT with models from HuggingFace) and optionally support embedding models
including GPT-4 from OpenAI, PaLM from Google and other models from Cohere. Embeddings models offer an approach to ML where high-dimensional
data (data in which the number of features or variables observed are close to or larger than the number of observations, or data points)
is converted into low-dimensional data (where the number of observations far outnumbers the number of features) while preserving relevant
information. This process of dimensionality reduction helps simplify the data and make it easier to process by ML algorithms. The appeal
of embeddings is that they can capture the underlying structure and semantics of the data. For instance, in NLP, words with similar meanings
will have similar embeddings. This provides a way to quantify the ‘similarity’ between different words or entities, which
is highly valuable when building complex models. We have purposefully designed our systems to give us the flexibility to be independent
of any one provider or partner. We periodically evaluate the cost, latency and quality of models because we operate in a rapidly evolving
industry. We believe we get superior performance compared to “off-the-shelf” use of LLMs through (a) injecting relevant historical
data into prompts (via the standard “Retrieval-augmented generation” pattern) and (b) pre-and post-processing the data to
better address customer-specific vernaculars, including the use of acronyms, emojis and non-traditional spellings. We also fine-tune open
source and third-party models with proprietary labeled data to improve performance on tasks like extracting relevant profile data from
content that end-users or consumers have shared in conversations with our conversation AI systems or classifying fan engagement data as
genuine versus originating from bots or spam. While unlabeled data consists of raw inputs with no designated outcome, labeled data is
care