We introduce “Generative Fusion Decoding” (GFD), a novel shallow fusion framework, utilized to integrate large language models(LLMs) into cross-modal text recognition systems inlculding automatic speech recognition (ASR) and optical character recognition (OCR). We derive the formulas necessary to enable GFD to operate across mismatched token spaces of different models by calculating likelihood at the byte level, thereby enabling seamless fusion and synchronous progression during the decoding process. GFD is plug-and-play bydesign, making it readily compatible with various auto-regressive models without the need for any re-training. GFD proves effective for general ASR and OCR tasks through intermediate and frequent interactions with LLMs, surpassing cascaded methods in English and Mandarin benchmarks. In addition, GFD transfers in-context learning abilities of LLMs and allows for adaptive ASR in instruction-aware andlong-context settings, yielding significant WER reductions of up to 17.7%.

Large language models (LLMs) have significantly advanced autonomous agents, particularly in zero-shot tool usage, also known as function calling. This research delves into enhancing the function-calling capabilities of LLMs by exploring different approaches, including prompt formats for integrating function descriptions, blending function-calling and instruction-following data, introducing a novel Decision Token for conditional prompts, leveraging chain-of-thought reasoning, and overcoming multilingual challenges with a translation pipeline. Our key findings and contributions are as follows: (1) Instruction-following data improves both function-calling accuracy and relevance detection. (2) The use of the newly proposed Decision Token, combined with synthetic non-function-call data, enhances relevance detection. (3) A tailored translation pipeline effectively overcomes multilingual limitations, demonstrating significant improvements in Traditional Chinese. These insights highlight the potential for improved function-calling capabilities and multilingual applications in LLMs.

An approach to improve question-answering performance is to retrieve accompanying information that contains factual evidence matching the question. These retrieved documents are then fed into a reader that generates an answer. A commonly applied retriever is dense passage retrieval. In this retriever, the output of a transformer neural network is used to query a knowledge database for matching documents. Inspired by the observation that different layers of a transformer network provide rich representations with different levels of abstraction, we hypothesize that useful queries can be generated not only at the output layer, but at every layer of a transformer network, and that the hidden representations of different layers may combine to improve the fetched documents for reader performance. Our novel approach integrates retrieval into each layer of a transformer network, exploiting the hierarchical representations of the input question. We show that our technique outperforms prior work on downstream tasks such as question answering, demonstrating the effectiveness of our approach.

In meta-learning, the knowledge learned from previous tasks is transferred to new ones, but this transfer only works if tasks are related. Sharing information between unrelated tasks might hurt performance, and it is unclear how to transfer knowledge across tasks that have a hierarchical structure. Our research extends a meta-learning model, MAML, by exploiting hierarchical task relationships. Our algorithm, TreeMAML, adapts the model to each task with a few gradient steps, but the adaptation follows the hierarchical tree structure: in each step, gradients are pooled across tasks clusters and subsequent steps follow down the tree. We also implement a clustering algorithm that generates the tasks tree without previous knowledge of the task structure, allowing us to make use of implicit relationships between the tasks. We show that TreeMAML successfully trains natural language processing models for cross-lingual Natural Language Inference by taking advantage of the language phylogenetic tree. This result is useful since most languages in the world are under-resourced and the improvement on cross-lingual transfer allows the internationalization of NLP models.

Natural conversations are filled with disfluencies. This study investigates if and how BERT understands disfluency with three experiments: (1) a behavioural study using a downstream task, (2) an analysis of sentence embeddings and (3) an analysis of the attention mechanism on disfluency. The behavioural study shows that without fine-tuning on disfluent data, BERT does not suffer significant performance loss when presented disfluent compared to fluent inputs (exp1). Analysis on sentence embeddings of disfluent and fluent sentence pairs reveals that the deeper the layer, the more similar their representation (exp2). This indicates that deep layers of BERT become relatively invariant to disfluency. We pinpoint attention as a potential mechanism that could explain this phenomenon (exp3). Overall, the study suggests that BERT has knowledge of disfluency structure. We emphasise the potential of using BERT to understand natural utterances without disfluency removal.