20 research outputs found
Answering Ambiguous Questions via Iterative Prompting
In open-domain question answering, due to the ambiguity of questions,
multiple plausible answers may exist. To provide feasible answers to an
ambiguous question, one approach is to directly predict all valid answers, but
this can struggle with balancing relevance and diversity. An alternative is to
gather candidate answers and aggregate them, but this method can be
computationally costly and may neglect dependencies among answers. In this
paper, we present AmbigPrompt to address the imperfections of existing
approaches to answering ambiguous questions. Specifically, we integrate an
answering model with a prompting model in an iterative manner. The prompting
model adaptively tracks the reading process and progressively triggers the
answering model to compose distinct and relevant answers. Additionally, we
develop a task-specific post-pretraining approach for both the answering model
and the prompting model, which greatly improves the performance of our
framework. Empirical studies on two commonly-used open benchmarks show that
AmbigPrompt achieves state-of-the-art or competitive results while using less
memory and having a lower inference latency than competing approaches.
Additionally, AmbigPrompt also performs well in low-resource settings. The code
are available at: https://github.com/sunnweiwei/AmbigPrompt.Comment: To be published in ACL 202
Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation
Current state-of-the-art neural dialogue systems are mainly data-driven and
are trained on human-generated responses. However, due to the subjectivity and
open-ended nature of human conversations, the complexity of training dialogues
varies greatly. The noise and uneven complexity of query-response pairs impede
the learning efficiency and effects of the neural dialogue generation models.
What is more, so far, there are no unified dialogue complexity measurements,
and the dialogue complexity embodies multiple aspects of
attributes---specificity, repetitiveness, relevance, etc. Inspired by human
behaviors of learning to converse, where children learn from easy dialogues to
complex ones and dynamically adjust their learning progress, in this paper, we
first analyze five dialogue attributes to measure the dialogue complexity in
multiple perspectives on three publicly available corpora. Then, we propose an
adaptive multi-curricula learning framework to schedule a committee of the
organized curricula. The framework is established upon the reinforcement
learning paradigm, which automatically chooses different curricula at the
evolving learning process according to the learning status of the neural
dialogue generation model. Extensive experiments conducted on five
state-of-the-art models demonstrate its learning efficiency and effectiveness
with respect to 13 automatic evaluation metrics and human judgments.Comment: Accepted to AAAI 202
Towards Verifiable Text Generation with Evolving Memory and Self-Reflection
Despite the remarkable ability of large language models (LLMs) in language
comprehension and generation, they often suffer from producing factually
incorrect information, also known as hallucination. A promising solution to
this issue is verifiable text generation, which prompts LLMs to generate
content with citations for accuracy verification. However, verifiable text
generation is non-trivial due to the focus-shifting phenomenon, the intricate
reasoning needed to align the claim with correct citations, and the dilemma
between the precision and breadth of retrieved documents. In this paper, we
present VTG, an innovative framework for Verifiable Text Generation with
evolving memory and self-reflection. VTG introduces evolving long short-term
memory to retain both valuable documents and recent documents. A two-tier
verifier equipped with an evidence finder is proposed to rethink and reflect on
the relationship between the claim and citations. Furthermore, active retrieval
and diverse query generation are utilized to enhance both the precision and
breadth of the retrieved documents. We conduct extensive experiments on five
datasets across three knowledge-intensive tasks and the results reveal that VTG
significantly outperforms baselines
Explainability for Large Language Models: A Survey
Large language models (LLMs) have demonstrated impressive capabilities in
natural language processing. However, their internal mechanisms are still
unclear and this lack of transparency poses unwanted risks for downstream
applications. Therefore, understanding and explaining these models is crucial
for elucidating their behaviors, limitations, and social impacts. In this
paper, we introduce a taxonomy of explainability techniques and provide a
structured overview of methods for explaining Transformer-based language
models. We categorize techniques based on the training paradigms of LLMs:
traditional fine-tuning-based paradigm and prompting-based paradigm. For each
paradigm, we summarize the goals and dominant approaches for generating local
explanations of individual predictions and global explanations of overall model
knowledge. We also discuss metrics for evaluating generated explanations, and
discuss how explanations can be leveraged to debug models and improve
performance. Lastly, we examine key challenges and emerging opportunities for
explanation techniques in the era of LLMs in comparison to conventional machine
learning models
Text-Video Retrieval via Variational Multi-Modal Hypergraph Networks
Text-video retrieval is a challenging task that aims to identify relevant
videos given textual queries. Compared to conventional textual retrieval, the
main obstacle for text-video retrieval is the semantic gap between the textual
nature of queries and the visual richness of video content. Previous works
primarily focus on aligning the query and the video by finely aggregating
word-frame matching signals. Inspired by the human cognitive process of
modularly judging the relevance between text and video, the judgment needs
high-order matching signal due to the consecutive and complex nature of video
contents. In this paper, we propose chunk-level text-video matching, where the
query chunks are extracted to describe a specific retrieval unit, and the video
chunks are segmented into distinct clips from videos. We formulate the
chunk-level matching as n-ary correlations modeling between words of the query
and frames of the video and introduce a multi-modal hypergraph for n-ary
correlation modeling. By representing textual units and video frames as nodes
and using hyperedges to depict their relationships, a multi-modal hypergraph is
constructed. In this way, the query and the video can be aligned in a
high-order semantic space. In addition, to enhance the model's generalization
ability, the extracted features are fed into a variational inference component
for computation, obtaining the variational representation under the Gaussian
distribution. The incorporation of hypergraphs and variational inference allows
our model to capture complex, n-ary interactions among textual and visual
contents. Experimental results demonstrate that our proposed method achieves
state-of-the-art performance on the text-video retrieval task
Estimating the Volume of the Solution Space of SMT(LIA) Constraints by a Flat Histogram Method
The satisfiability modulo theories (SMT) problem is to decide the satisfiability of a logical formula with respect to a given background theory. This work studies the counting version of SMT with respect to linear integer arithmetic (LIA), termed SMT(LIA). Specifically, the purpose of this paper is to count the number of solutions (volume) of a SMT(LIA) formula, which has many important applications and is computationally hard. To solve the counting problem, an approximate method that employs a recent Markov Chain Monte Carlo (MCMC) sampling strategy called “flat histogram” is proposed. Furthermore, two refinement strategies are proposed for the sampling process and result in two algorithms, MCMC-Flat1/2 and MCMC-Flat1/t, respectively. In MCMC-Flat1/t, a pseudo sampling strategy is introduced to evaluate the flatness of histograms. Experimental results show that our MCMC-Flat1/t method can achieve good accuracy on both structured and random instances, and our MCMC-Flat1/2 is scalable for instances of convex bodies with up to 7 variables
Insulin Resistance-Associated Interhemispheric Functional Connectivity Alterations in T2DM: A Resting-State fMRI Study
We aim to investigate whether decreased interhemispheric functional connectivity exists in patients with type 2 diabetes mellitus (T2DM) by using resting-state functional magnetic resonance imaging (rs-fMRI). In addition, we sought to determine whether interhemispheric functional connectivity deficits associated with cognition and insulin resistance (IR) among T2DM patients. We compared the interhemispheric resting state functional connectivity of 32 T2DM patients and 30 healthy controls using rs-fMRI. Partial correlation coefficients were used to detect the relationship between rs-fMRI information and cognitive or clinical data. Compared with healthy controls, T2DM patients showed bidirectional alteration of functional connectivity in several brain regions. Functional connectivity values in the middle temporal gyrus (MTG) and in the superior frontal gyrus were inversely correlated with Trail Making Test-B score of patients. Notably, insulin resistance (log homeostasis model assessment-IR) negatively correlated with functional connectivity in the MTG of patients. In conclusion, T2DM patients exhibit abnormal interhemispheric functional connectivity in several default mode network regions, particularly in the MTG, and such alteration is associated with IR. Alterations in interhemispheric functional connectivity might contribute to cognitive dysfunction in T2DM patients