Sparse Sinkhorn Token Translation (S2T2) for Adapting Language Models to New Domains

Core Concepts

Pre-trained language models can be adapted to new domains by learning a translation between the source domain tokens and new target domain tokens using Sparse Sinkhorn Token Translation (S2T2), which improves compression, perplexity, and semantic alignment without requiring parallel data.

Abstract

Research Paper Summary: Adapting Language Models via Token Translation

Bibliographic Information: Feng, Z., Marwah, T., Mackey, L., Alvarez-Melis, D., & Fusi, N. (2024). Adapting Language Models via Token Translation. arXiv preprint arXiv:2411.00593v1.

Research Objective: This paper introduces a novel method called Sparse Sinkhorn Token Translation (S2T2) to adapt pre-trained language models (LLMs) to new domains without requiring parallel data, addressing the limitations of existing tokenization approaches when applied to out-of-domain text.

Methodology: S2T2 leverages a sparse optimal transport (OT) algorithm to learn a translation between the tokens of the source domain (on which the LLM is pre-trained) and the tokens of the target domain. This translation is represented as a sparse probability matrix, enabling the model to map target domain tokens to a distribution over source domain tokens and vice versa. The method is evaluated by adapting an English LLM to the domain of protein sequences.

Key Findings:

• S2T2 significantly improves both the perplexity and compression of protein sequences compared to directly fine-tuning the pre-trained model with either the source or target tokenizer.
• The token translations learned for smaller, less computationally expensive models can be directly transferred to larger, more powerful models, demonstrating weak-to-strong model transferability and enabling efficient adaptation.

Main Conclusions: S2T2 offers a promising approach for adapting LLMs to new domains without the need for parallel data, leading to improved performance in terms of perplexity, compression, and semantic alignment. The method's ability to transfer learned translations across models of different sizes presents a significant advantage for efficient adaptation.

Significance: This research contributes to the field of natural language processing by addressing the challenge of domain adaptation for LLMs, particularly in scenarios where parallel data is scarce or unavailable. The proposed S2T2 method and its demonstrated effectiveness have the potential to broaden the applicability of LLMs across diverse domains and tasks.

Limitations and Future Research: The study focuses on adapting an English LLM to protein sequences. Future research could explore the effectiveness of S2T2 in adapting LLMs to other modalities, such as code and images, and investigate the potential of combining source and target token vocabularies for multi-domain LLM development.

Stats

The new BPE tokenizer reduces the length of protein sequences by a factor of 1.82× on average.
S2T2 improves perplexity and bits-per-byte (BpB) compared to whole-model fine-tuning with the original tokenizer.
Baseline 3 (fine-tuning with the original tokenizer) has significantly worse BpB due to longer sequence length.
S2T2 initialization outperforms both dense Sinkhorn and unconstrained token translation in perplexity and BpB.
S2T2, after fine-tuning, surpasses the perplexity and BpB of directly fine-tuning with a new tokenizer.
The translator P learned using OLMo-1B can be transferred to OLMo-7B, yielding better performance than random guessing or using the original or truncated new tokenizer.

Quotes

"When faced with text from a new target domain, compression quality drops, context length and inference costs increase, and learned semantic alignment deteriorates."
"S2T2 learns a translation between training domain tokens and new target domain tokens just using a sample data from the target domain and the pretrained LLM weights."
"S2T2 enables weak-to-strong model transferability: Translations learned for smaller, less expensive models can be transferred to larger, more powerful models to reap the benefits at lower cost."

Key Insights Distilled From

Adapting Language Models via Token Translation

by Zhili Feng, ... at arxiv.org 11-04-2024

https://arxiv.org/pdf/2411.00593.pdf

Deeper Inquiries

How might S2T2 be applied to adapt LLMs for tasks beyond language modeling, such as machine translation or text summarization?

S2T2's core principle of adapting tokenizers and translating between token spaces holds exciting potential for tasks beyond language modeling. Here's how it can be applied to machine translation and text summarization:
Machine Translation:

Specialized Tokenizers: Instead of using a single tokenizer for both source and target languages, S2T2 can train tailored tokenizers for each. This allows for better capturing of language-specific nuances and potentially improves translation quality, especially for low-resource languages.
Direct Token Translation: The learned sparse translation matrix (P) can be used to directly map tokens between the source and target languages. This can be incorporated into existing neural machine translation architectures, potentially reducing the need for large parallel corpora during training.
Cross-Lingual Transfer Learning: S2T2's ability to transfer token translations from smaller to larger models can be leveraged for cross-lingual transfer learning. A translation model trained on a high-resource language pair can be adapted to a low-resource language pair by transferring the token translation knowledge.
Text Summarization:

Abstractive Summarization: S2T2 can be used to learn a translation between the vocabulary of the input text and a more concise vocabulary suitable for summaries. This can guide the model to generate more abstractive and concise summaries.
Domain-Specific Summarization: For specialized domains like scientific papers or legal documents, S2T2 can train a tokenizer specific to the domain's terminology. This can improve the model's understanding of the domain and lead to more accurate summaries.
Multi-Lingual Summarization: Similar to machine translation, S2T2 can facilitate multi-lingual summarization by learning token translations between different languages. This allows for summarizing text from one language into another, expanding the reach and applicability of summarization models.

Could the reliance on a pre-trained LLM and its inherent biases limit the effectiveness of S2T2 in certain domains or for specific languages?

Yes, the reliance on a pre-trained LLM and its inherent biases can indeed pose limitations to S2T2's effectiveness, particularly in the following scenarios:

Domain Mismatch: If the pre-trained LLM is primarily trained on a domain significantly different from the target domain, the learned token translations might not be as effective. For example, an LLM trained on news articles might not translate well to the medical domain.
Bias Amplification: LLMs are known to inherit biases present in their training data. S2T2, by relying on these models, risks amplifying these biases in the target domain. This is particularly concerning for sensitive domains like social justice or healthcare, where biased translations can have real-world consequences.
Low-Resource Languages: LLMs are often trained on vast amounts of data, which are predominantly available for high-resource languages. This can lead to under-representation and potential biases against low-resource languages, making S2T2 less effective for these languages.
Mitigation Strategies:

Domain-Specific Pre-Training: Pre-training LLMs on data relevant to the target domain can help mitigate domain mismatch issues.
Bias Mitigation Techniques: Incorporating bias mitigation techniques during pre-training and fine-tuning can help reduce bias amplification.
Data Augmentation and Representation: Increasing data diversity and representation for low-resource languages during pre-training can improve S2T2's performance for these languages.

If we consider language as a form of compression, how might the principles of S2T2 be applied to other domains where efficient information encoding is crucial, such as data compression or communication protocols?

The principles of S2T2, rooted in adapting compression schemes (tokenizers) and translating between encoded representations, hold intriguing possibilities for domains beyond language where efficient information encoding is paramount:
Data Compression:

Adaptive Compression Algorithms: Similar to how S2T2 adapts tokenizers for different language domains, we can envision compression algorithms that dynamically adjust their encoding schemes based on the type of data being compressed. This could lead to higher compression ratios for specific data types like images, audio, or scientific datasets.
Cross-Modal Compression: Inspired by token translation, we can explore methods to translate between different data representations. For instance, compressing an image by first translating it into a frequency domain representation and then applying a compression algorithm optimized for that domain.
Lossy Compression with Semantic Preservation: S2T2's focus on semantic alignment can be applied to lossy compression. Instead of simply discarding information, algorithms can be designed to prioritize preserving semantically relevant information, leading to less perceptual loss.
Communication Protocols:

Efficient Data Transmission: S2T2's principles can be applied to optimize data transmission in communication protocols. By adapting encoding schemes based on network conditions or the type of data being transmitted, we can achieve higher bandwidth efficiency and lower latency.
Cross-Platform Communication: Translating between different data formats and protocols is crucial for seamless communication between diverse devices and systems. S2T2's token translation concept can inspire methods to efficiently translate between these formats, enabling smoother interoperability.
Secure Communication: Efficient encoding is vital for secure communication. S2T2's ideas can be explored to develop adaptive encryption schemes that optimize the trade-off between security and data overhead, leading to more secure and efficient communication channels.