Interchangeable Token Embeddings for Extendable Vocabulary and Alpha-Equivalence
- URL: http://arxiv.org/abs/2410.17161v1
- Date: Tue, 22 Oct 2024 16:34:36 GMT
- Title: Interchangeable Token Embeddings for Extendable Vocabulary and Alpha-Equivalence
- Authors: İlker Işık, Ramazan Gokberk Cinbis, Ebru Aydin Gol,
- Abstract summary: We propose a novel approach for learning interchangeable tokens in language models.
Our method is designed to address alpha-equivalence, the principle that renaming bound variables in a syntactic expression preserves semantics.
- Score: 6.991281327290525
- License:
- Abstract: We propose a novel approach for learning interchangeable tokens in language models to obtain an extendable vocabulary that can generalize to new tokens. Our method is designed to address alpha-equivalence, the principle that renaming bound variables in a syntactic expression preserves semantics. This property arises in many formal languages such as temporal logics, in which all proposition symbols represent the same concept but are distinguishable from each other. To handle such tokens, we develop a dual-part embedding approach. The first part is shared across all interchangeable tokens, thereby enforcing that they represent the same core concept. The second part is randomly generated for each token, which enables distinguishability. We evaluate our method in a Transformer encoder-decoder model on two tasks: solving linear temporal logic formulae and copying with extendable vocabulary. Our method demonstrates promising generalization capabilities in addition to introducing a favorable inductive bias for alpha-equivalence.
Related papers
- Tokenization as Finite-State Transduction [24.19959327497118]
We introduce a finite-state framework which can efficiently encode all possible tokenizations of a regular language.
We show that Byte-Pair.
Match (BPE) and MaxPiece (WordPiece) fit within this framework.
An application of this is to guided generation, where the outputs of a language model are constrained to match some pattern.
arXiv Detail & Related papers (2024-10-21T07:10:07Z) - SEP: Self-Enhanced Prompt Tuning for Visual-Language Model [68.68025991850115]
We introduce a novel approach named Self-Enhanced Prompt Tuning (SEP)
SEP explicitly incorporates discriminative prior knowledge to enhance both textual-level and visual-level embeddings.
Comprehensive evaluations across various benchmarks and tasks confirm SEP's efficacy in prompt tuning.
arXiv Detail & Related papers (2024-05-24T13:35:56Z) - Word Boundary Information Isn't Useful for Encoder Language Models [8.1305024841559]
We train transformer encoders across four different training scales, and investigate several alternative approaches to including word boundary information.
We find no substantial improvements from our alternative approaches, suggesting that modifying tokenisers to remove word boundary information isn't leading to a loss of useful information.
arXiv Detail & Related papers (2024-01-15T19:21:08Z) - Object Recognition as Next Token Prediction [99.40793702627396]
We present an approach to pose object recognition as next token prediction.
The idea is to apply a language decoder that auto-regressively predicts the text tokens from image embeddings to form labels.
arXiv Detail & Related papers (2023-12-04T18:58:40Z) - Sequential Integrated Gradients: a simple but effective method for
explaining language models [0.18459705687628122]
We propose a new method for explaining language models called Sequential Integrated Gradients ( SIG)
SIG computes the importance of each word in a sentence by keeping fixed every other words, only creatings between the baseline and word of interest.
We show on various models and datasets that SIG proves to be a very effective method for explaining language models.
arXiv Detail & Related papers (2023-05-25T08:44:11Z) - Lexinvariant Language Models [84.2829117441298]
Token embeddings, a mapping from discrete lexical symbols to continuous vectors, are at the heart of any language model (LM)
We study textitlexinvariantlanguage models that are invariant to lexical symbols and therefore do not need fixed token embeddings in practice.
We show that a lexinvariant LM can attain perplexity comparable to that of a standard language model, given a sufficiently long context.
arXiv Detail & Related papers (2023-05-24T19:10:46Z) - From Characters to Words: Hierarchical Pre-trained Language Model for
Open-vocabulary Language Understanding [22.390804161191635]
Current state-of-the-art models for natural language understanding require a preprocessing step to convert raw text into discrete tokens.
This process known as tokenization relies on a pre-built vocabulary of words or sub-word morphemes.
We introduce a novel open-vocabulary language model that adopts a hierarchical two-level approach.
arXiv Detail & Related papers (2023-05-23T23:22:20Z) - VECO 2.0: Cross-lingual Language Model Pre-training with
Multi-granularity Contrastive Learning [56.47303426167584]
We propose a cross-lingual pre-trained model VECO2.0 based on contrastive learning with multi-granularity alignments.
Specifically, the sequence-to-sequence alignment is induced to maximize the similarity of the parallel pairs and minimize the non-parallel pairs.
token-to-token alignment is integrated to bridge the gap between synonymous tokens excavated via the thesaurus dictionary from the other unpaired tokens in a bilingual instance.
arXiv Detail & Related papers (2023-04-17T12:23:41Z) - Fast End-to-End Speech Recognition via a Non-Autoregressive Model and
Cross-Modal Knowledge Transferring from BERT [72.93855288283059]
We propose a non-autoregressive speech recognition model called LASO (Listen Attentively, and Spell Once)
The model consists of an encoder, a decoder, and a position dependent summarizer (PDS)
arXiv Detail & Related papers (2021-02-15T15:18:59Z) - Neural Syntactic Preordering for Controlled Paraphrase Generation [57.5316011554622]
Our work uses syntactic transformations to softly "reorder'' the source sentence and guide our neural paraphrasing model.
First, given an input sentence, we derive a set of feasible syntactic rearrangements using an encoder-decoder model.
Next, we use each proposed rearrangement to produce a sequence of position embeddings, which encourages our final encoder-decoder paraphrase model to attend to the source words in a particular order.
arXiv Detail & Related papers (2020-05-05T09:02:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.