Mexico

Mixed initiative serves as one of the key factors in controlling conversation directions. For a speaker, responding passively or leading proactively would result in rather different responses. However, most dialogue systems focus on training a holistic response generation model without any distinction among different initiatives. It leads to the cross-contamination problem, where the model confuses different initiatives and generates inappropriate responses. Moreover, obtaining plenty of human annotations for initiative labels can be expensive. To address this issue, we propose a general mix-Initiative Dynamic Prefix Tuning framework (IDPT) to decouple different initiatives from the generation model, which learns initiative-aware prefixes in both supervised and unsupervised settings. Specifically, IDPT decouples initiative factors into different prefix parameters and uses the attention mechanism to adjust the selection of initiatives in guiding generation dynamically. The prefix parameters can be tuned towards accurate initiative prediction as well as mix-initiative response generation. Extensive experiments on two public dialogue datasets show that the proposed IDPT outperforms previous baselines on both automatic metrics and human evaluations. It also manages to generate appropriate responses with manipulated initiatives.

NAACL 2024

Mix-Initiative Response Generation with Dynamic Prefix Tuning

mix-initiative

prefix tuning

recommendation

dialogue system

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
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ACL 2023

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poster

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The University of Texas at Austin 
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2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

In this paper, we propose sequence-based pre-training methods to enhance procedural understanding in natural language processing. Procedural text, containing sequential instructions to accomplish a task, is difficult to understand due to the changing attributes of entities in the context. We focus on recipes as they are commonly represented as ordered instructions, and use this order as a supervision signal. Our work is one of the first to compare several `order-as-supervision' transformer pre-training methods, including Permutation Classification, Embedding Regression, and Skip-Clip, and show that these methods give improved results compared to baselines and SoTA LLMs on two downstream Entity-Tracking datasets: NPN-Cooking dataset in recipe domain and ProPara dataset in open domain. Our proposed methods address the non-trivial Entity Tracking Task that requires prediction of entity states across procedure steps, which requires understanding the order of steps. These methods show an improvement over the best baseline by $1.6$\% and $7$-$9$\% on NPN-Cooking and ProPara Datasets respectively across metrics.

Order-Based Pre-training Strategies for Procedural Text Understanding

In the realm of emerging multitasking abilities of Large language models (LLMs), methodologies like prompt tuning enable low-cost adaptation to downstream tasks without retraining the model. However, automatic input pre-processing when LLMs are unavailable is currently under-studied. This paper proposes ReLLM (Rephrasing for LLMs), a method that automatically paraphrases input content for better output generations. ReLLM replaces low-frequency lexical items with their high-frequency counterparts. This substitution is particularly beneficial for low-resource language tasks that lack sufficient training data and resources. 
ReLLM is user-friendly and requires no additional LLM training. Experimental results in cross-lingual summarization, and natural language inference demonstrate the effectiveness of  ReLLM.

Rephrasing Invokes Better Generations for Large Language Models

In this paper, we study the generalization ability of large language models (LLMs) with respect to compositional instructions, which are instructions that can be decomposed into several sub-instructions. We argue that the ability to generalize from simple instructions to more intricate compositional instructions represents a key aspect of the out-of-distribution generalization for LLMs. Since there are no specialized datasets for studying this phenomenon, we first construct a dataset with the help of ChatGPT, guided by the self-instruct technique. Then, we fine-tune and evaluate LLMs on these datasets. Interestingly, our experimental results indicate that training LLMs on higher-order compositional instructions enhances their performance on lower-order ones, but the reverse does not hold true.

Exploring Compositional Generalization of Large Language Models

Critical error detection (CED) in machine translation is a task that aims to detect errors that significantly distort the intended meaning. However, the existing study of CED lacks explainability due to the absence of content addressing the reasons for catastrophic errors. To address this limitation, we propose Explainable CED, a dataset that introduces the attributes of error explanation and correction regarding critical errors. Considering the advantage of reducing time costs and mitigating human annotation bias, we leverage a large language model in the data construction process. To improve the quality of the dataset and mitigate hallucination, we compare responses from the model and introduce an additional data filtering method through feedback scoring. The experiment demonstrates that the dataset appropriately reflects a consistent explanation and revision for errors, validating the reliability of the dataset.

Explainable CED: A Dataset for Explainable Critical Error Detection in Machine Translation

Exact nearest neighbor search is a computationally intensive process, and even its simpler sibling --- vector retrieval --- can be computationally complex. This is exacerbated when retrieving vectors which have high-dimension d relative to the number of vectors, N, in the database. Exact nearest neighbor retrieval has been generally acknowledged to be an O(Nd) problem with no sub-linear solutions. Attention has instead shifted towards Approximate Nearest-Neighbor (ANN) retrieval techniques, many of which have sub-linear or even logarithmic time complexities. However, if our intuition from binary search problems (e.g. d=1 vector retrieval) carries, there ought to be a way to retrieve an organized representation of vectors without brute-forcing our way to a solution. For low dimensions (e.g. d=2 or d=3 cases), kd-trees provide an O(d*log N) algorithm for retrieval. Unfortunately the algorithm deteriorates rapidly to an O(d*N) solution at high dimensions (e.g. k=128), in practice. We propose a novel algorithm for logarithmic Fast Exact Retrieval for Nearest-neighbor lookup (FERN), inspired by kd-trees. The algorithm achieves O(d*log N) look-up with 100% recall on 10 million d=128 uniformly randomly generated vectors. Code available at https://github.com/RichardZhu123/ferns

Fast Exact Retrieval for Nearest-neighbor Lookup (FERN)

Large language models (LLMs) are increasingly used for applications beyond text generation, ranging from text summarization to instruction following. One popular example of exploiting LLMs' zero- and few-shot capabilities is the task of text classification. This short paper compares two popular LLM-based classification pipelines (GPT-4 and LLAMA 2) to a popular pre-LLM-era classification pipeline on the task of news trustworthiness classification, focusing on performance, training, and deployment requirements. We find that, in this case, the pre-LLM-era ensemble pipeline outperforms the two popular LLM pipelines while being orders of magnitude smaller in parameter size.

When XGBoost Outperforms GPT-4 on Text Classification: A Case Study

Named Entity Recognition (NER) is a useful component in Natural Language Processing (NLP) applications. It is used in various tasks such as Machine Translation, Summarization, Information Retrieval, and Question-Answering systems. The research on NER is centered around English and some other major languages, whereas limited attention has been given to Indian languages. We analyze the challenges and propose techniques that can be tailored for Multilingual Named Entity Recognition for Indian Languages. We present a human annotated named entity corpora of ∼40K sentences for 4 Indian languages from two of the major Indian language families. Additionally, we show the transfer learning capabilities of pre-trained transformer models from a high resource language to multiple low resource languages through a series of experiments. We also present a multilingual model fine-tuned on our dataset, which achieves an F1 score of ∼0.80 on our dataset on average. We achieve comparable performance on completely unseen benchmark datasets for Indian languages which affirms the usability of our model.

Fine-tuning Pre-trained Named Entity Recognition Models For Indian Languages

Large Language Models (LLMs) have significantly impacted various fields requiring advanced linguistic understanding, yet concerns regarding their inherent biases and ethical considerations have also increased. Notably, LLMs have been critiqued for perpetuating stereotypes against diverse groups based on race, sexual orientation, and other attributes. However, most research analyzing these biases has predominantly focused on communities where English is the primary language, neglecting to consider the cultural and linguistic nuances of other societies. In this paper, we aim to explore the inherent biases and toxicity of LLMs, specifically within the social context of Korea. We devise a set of prompts that reflect major societal issues in Korea and assign varied personas to both ChatGPT and GPT-4 to assess the toxicity of the generated sentences. Our findings indicate that certain personas or prompt combinations consistently yield harmful content, highlighting the potential risks associated with specific persona-issue alignments within the Korean cultural framework. Furthermore, we discover that GPT-4 can produce more than twice the level of toxic content than ChatGPT under certain conditions.

Exploring Inherent Biases in LLMs within Korean Social Context: A Comparative Analysis of ChatGPT and GPT-4

The prediction for output token $n+1$ of Transformer architectures without one of the mechanisms of positional encodings and causal attention is invariant to permutations of input tokens $1, 2, .., n-1$. Usually, both mechanisms are employed and the symmetry with respect to the input tokens is broken. Recently, it has been shown that one can train Transformers without positional encodings. This must be enabled by the causal attention mechanism. In this paper, we elaborate on the argument that the causal connection mechanism must be responsible for the fact that Transformers are able to model input sequences where the order is important. Vertical ``slices" of transformers are all encouraged to represent the same location $k$ in the input sequence. We hypothesize that residual connections contribute to this; we do not find definitive evidence of this.

Breaking Symmetry When Training Transformers

Pretrained transformer-based language models have produced state-of-the-art performance in most natural language understanding tasks. These models undergo two stages of training: pretraining on a huge corpus of data and fine-tuning on a specific downstream task. The pretraining phase is extremely compute-intensive and requires several high-performance computing devices like GPUs and several days or even months of training, but it is crucial for the model to capture global knowledge and also has a significant impact on the fine-tuning task. This is a major roadblock for researchers without access to sophisticated computing resources. To overcome this challenge, we propose two novel hybrid architectures called HybridBERT (HBERT), which combine self-attention and additive attention mechanisms together with sub-layer normalization. We introduce a computing budget to the pretraining phase, limiting the training time and usage to a single GPU. We show that HBERT attains twice the pretraining accuracy of a vanilla-BERT baseline. We also evaluate our proposed models on two downstream tasks, where we outperform BERT-base while accelerating inference. Moreover, we study the effect of weight initialization with a limited pretraining budget. The code and models are publicly available at: www.github.com/gokulsg/HBERT/.

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Order-Based Pre-training Strategies for Procedural Text Understanding

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Knowledge-enhanced Mixed-initiative Dialogue System for Emotional Support Conversations

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