8 research outputs found
Entity-Aspect-Opinion-Sentiment Quadruple Extraction for Fine-grained Sentiment Analysis
Product reviews often contain a large number of implicit aspects and
object-attribute co-existence cases. Unfortunately, many existing studies in
Aspect-Based Sentiment Analysis (ABSA) have overlooked this issue, which can
make it difficult to extract opinions comprehensively and fairly. In this
paper, we propose a new task called Entity-Aspect-Opinion-Sentiment Quadruple
Extraction (EASQE), which aims to hierarchically decompose aspect terms into
entities and aspects to avoid information loss, non-exclusive annotations, and
opinion misunderstandings in ABSA tasks. To facilitate research in this new
task, we have constructed four datasets (Res14-EASQE, Res15-EASQE, Res16-EASQE,
and Lap14-EASQE) based on the SemEval Restaurant and Laptop datasets. We have
also proposed a novel two-stage sequence-tagging based Trigger-Opinion
framework as the baseline for the EASQE task. Empirical evaluations show that
our Trigger-Opinion framework can generate satisfactory EASQE results and can
also be applied to other ABSA tasks, significantly outperforming
state-of-the-art methods. We have made the four datasets and source code of
Trigger-Opinion publicly available to facilitate further research in this area
A Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily
Large Language Models (LLMs), such as ChatGPT and GPT-4, are designed to
provide useful and safe responses. However, adversarial prompts known as
'jailbreaks' can circumvent safeguards, leading LLMs to generate harmful
content. Exploring jailbreak prompts can help to better reveal the weaknesses
of LLMs and further steer us to secure them. Unfortunately, existing jailbreak
methods either suffer from intricate manual design or require optimization on
another white-box model, compromising generalization or jailbreak efficiency.
In this paper, we generalize jailbreak prompt attacks into two aspects: (1)
Prompt Rewriting and (2) Scenario Nesting. Based on this, we propose ReNeLLM,
an automatic framework that leverages LLMs themselves to generate effective
jailbreak prompts. Extensive experiments demonstrate that ReNeLLM significantly
improves the attack success rate while greatly reducing the time cost compared
to existing baselines. Our study also reveals the inadequacy of current defense
methods in safeguarding LLMs. Finally, we offer detailed analysis and
discussion from the perspective of prompt execution priority on the failure of
LLMs' defense. We hope that our research can catalyze both the academic
community and LLMs vendors towards the provision of safer and more regulated
Large Language Models
FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented Dialogue
Pre-trained language models based on general text enable huge success in the
NLP scenario. But the intrinsical difference of linguistic patterns between
general text and task-oriented dialogues makes existing pre-trained language
models less useful in practice. Current dialogue pre-training methods rely on a
contrastive framework and face the challenges of both selecting true positives
and hard negatives. In this paper, we propose a novel dialogue pre-training
model, FutureTOD, which distills future knowledge to the representation of the
previous dialogue context using a self-training framework. Our intuition is
that a good dialogue representation both learns local context information and
predicts future information. Extensive experiments on diverse downstream
dialogue tasks demonstrate the effectiveness of our model, especially the
generalization, robustness, and learning discriminative dialogue
representations capabilities.Comment: ACL 2023 Main Conferenc
Decoupling Pseudo Label Disambiguation and Representation Learning for Generalized Intent Discovery
Generalized intent discovery aims to extend a closed-set in-domain intent
classifier to an open-world intent set including in-domain and out-of-domain
intents. The key challenges lie in pseudo label disambiguation and
representation learning. Previous methods suffer from a coupling of pseudo
label disambiguation and representation learning, that is, the reliability of
pseudo labels relies on representation learning, and representation learning is
restricted by pseudo labels in turn. In this paper, we propose a decoupled
prototype learning framework (DPL) to decouple pseudo label disambiguation and
representation learning. Specifically, we firstly introduce prototypical
contrastive representation learning (PCL) to get discriminative
representations. And then we adopt a prototype-based label disambiguation
method (PLD) to obtain pseudo labels. We theoretically prove that PCL and PLD
work in a collaborative fashion and facilitate pseudo label disambiguation.
Experiments and analysis on three benchmark datasets show the effectiveness of
our method.Comment: Accepted at ACL2023 main conferenc
Exchanging-based Multimodal Fusion with Transformer
We study the problem of multimodal fusion in this paper. Recent
exchanging-based methods have been proposed for vision-vision fusion, which aim
to exchange embeddings learned from one modality to the other. However, most of
them project inputs of multimodalities into different low-dimensional spaces
and cannot be applied to the sequential input data. To solve these issues, in
this paper, we propose a novel exchanging-based multimodal fusion model MuSE
for text-vision fusion based on Transformer. We first use two encoders to
separately map multimodal inputs into different low-dimensional spaces. Then we
employ two decoders to regularize the embeddings and pull them into the same
space. The two decoders capture the correlations between texts and images with
the image captioning task and the text-to-image generation task, respectively.
Further, based on the regularized embeddings, we present CrossTransformer,
which uses two Transformer encoders with shared parameters as the backbone
model to exchange knowledge between multimodalities. Specifically,
CrossTransformer first learns the global contextual information of the inputs
in the shallow layers. After that, it performs inter-modal exchange by
selecting a proportion of tokens in one modality and replacing their embeddings
with the average of embeddings in the other modality. We conduct extensive
experiments to evaluate the performance of MuSE on the Multimodal Named Entity
Recognition task and the Multimodal Sentiment Analysis task. Our results show
the superiority of MuSE against other competitors. Our code and data are
provided at https://github.com/RecklessRonan/MuSE
APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection
Detecting out-of-domain (OOD) intents from user queries is essential for a
task-oriented dialogue system. Previous OOD detection studies generally work on
the assumption that plenty of labeled IND intents exist. In this paper, we
focus on a more practical few-shot OOD setting where there are only a few
labeled IND data and massive unlabeled mixed data that may belong to IND or
OOD. The new scenario carries two key challenges: learning discriminative
representations using limited IND data and leveraging unlabeled mixed data.
Therefore, we propose an adaptive prototypical pseudo-labeling (APP) method for
few-shot OOD detection, including a prototypical OOD detection framework
(ProtoOOD) to facilitate low-resource OOD detection using limited IND data, and
an adaptive pseudo-labeling method to produce high-quality pseudo OOD\&IND
labels. Extensive experiments and analysis demonstrate the effectiveness of our
method for few-shot OOD detection
PreQuant: A Task-agnostic Quantization Approach for Pre-trained Language Models
While transformer-based pre-trained language models (PLMs) have dominated a
number of NLP applications, these models are heavy to deploy and expensive to
use. Therefore, effectively compressing large-scale PLMs becomes an
increasingly important problem. Quantization, which represents high-precision
tensors with low-bit fix-point format, is a viable solution. However, most
existing quantization methods are task-specific, requiring customized training
and quantization with a large number of trainable parameters on each individual
task. Inspired by the observation that the over-parameterization nature of PLMs
makes it possible to freeze most of the parameters during the fine-tuning
stage, in this work, we propose a novel ``quantize before fine-tuning''
framework, PreQuant, that differs from both quantization-aware training and
post-training quantization. PreQuant is compatible with various quantization
strategies, with outlier-aware parameter-efficient fine-tuning incorporated to
correct the induced quantization error. We demonstrate the effectiveness of
PreQuant on the GLUE benchmark using BERT, RoBERTa, and T5. We also provide an
empirical investigation into the workflow of PreQuant, which sheds light on its
efficacy.Comment: Findings of ACL202
AutoFAS: Automatic Feature and Architecture Selection for Pre-Ranking System
Industrial search and recommendation systems mostly follow the classic
multi-stage information retrieval paradigm: matching, pre-ranking, ranking, and
re-ranking stages. To account for system efficiency, simple vector-product
based models are commonly deployed in the pre-ranking stage. Recent works
consider distilling the high knowledge of large ranking models to small
pre-ranking models for better effectiveness. However, two major challenges in
pre-ranking system still exist: (i) without explicitly modeling the performance
gain versus computation cost, the predefined latency constraint in the
pre-ranking stage inevitably leads to suboptimal solutions; (ii) transferring
the ranking teacher's knowledge to a pre-ranking student with a predetermined
handcrafted architecture still suffers from the loss of model performance. In
this work, a novel framework AutoFAS is proposed which jointly optimizes the
efficiency and effectiveness of the pre-ranking model: (i) AutoFAS for the
first time simultaneously selects the most valuable features and network
architectures using Neural Architecture Search (NAS) technique; (ii) equipped
with ranking model guided reward during NAS procedure, AutoFAS can select the
best pre-ranking architecture for a given ranking teacher without any
computation overhead. Experimental results in our real world search system show
AutoFAS consistently outperforms the previous state-of-the-art (SOTA)
approaches at a lower computing cost. Notably, our model has been adopted in
the pre-ranking module in the search system of Meituan, bringing significant
improvements