Precise question understanding is critical for temporal reading comprehension.
Besides a norm-grounding knowledge model, we present a novel norm-supported ethical judgment model in line with neural module networks to alleviate dilemma situations and improve norm-level explainability.
Large Language Models (LLMs) have ushered in a transformative era in the field of natural language processing, excelling in tasks related to text comprehension and generation.
Through extensive experiments on two mathematical reasoning benchmarks, namely GSM8k and MATH, we reveal the extraordinary capabilities of our model.
Large language models (LLMs) have shown remarkable capacity for in-context learning (ICL), where learning a new task from just a few training examples is done without being explicitly pre-trained.
Moreover, our model even outperforms the largest closed LLMs, Anthropic's Claude and Google's Bard, on HumanEval and HumanEval+.
Ranked #15 on Code Generation on HumanEval
Information retrieval (IR) plays a crucial role in locating relevant resources from vast amounts of data, and its applications have evolved from traditional knowledge bases to modern search engines (SEs).
We demonstrate that our PKG framework can enhance the performance of "black-box" LLMs on a range of domain knowledge-intensive tasks that require factual (+7. 9%), tabular (+11. 9%), medical (+3. 0%), and multimodal (+8. 1%) knowledge.
In this work, we propose a simple method that applies a large language model (LLM) to large-scale retrieval in zero-shot scenarios.
In this paper, we show an avenue for creating large amounts of instruction data with varying levels of complexity using LLM instead of humans.
In this paper, we conduct a thorough evaluation of the robustness of ChatGPT from the adversarial and out-of-distribution (OOD) perspective.
The conventional dense retrieval paradigm relies on encoding images and texts into dense representations using dual-stream encoders, however, it faces challenges with low retrieval speed in large-scale retrieval scenarios.
Weakly-Supervised Video Grounding (WSVG) aims to localize events of interest in untrimmed videos with only video-level annotations.
To address this issue, we propose a novel sparse retrieval paradigm for ITR that exploits sparse representations in the vocabulary space for images and texts.
Different from previous works that only rely on one positive and hard negatives as candidate passages, we create dark examples that all have moderate relevance to the query through mixing-up and masking in discrete space.
Long document retrieval aims to fetch query-relevant documents from a large-scale collection, where knowledge distillation has become de facto to improve a retriever by mimicking a heterogeneous yet powerful cross-encoder.
In large-scale retrieval, the lexicon-weighting paradigm, learning weighted sparse representations in vocabulary space, has shown promising results with high quality and low latency.
The alignment is achieved by weakened knowledge distillations to enlighten the retriever via two aspects -- 1) a lexicon-augmented contrastive objective to challenge the dense encoder and 2) a pair-wise rank-consistent regularization to make dense model's behavior incline to the other.
This paper focuses on the data augmentation for low-resource NLP tasks where the training set is limited.
A ranker plays an indispensable role in the de facto 'retrieval & rerank' pipeline, but its training still lags behind -- learning from moderate negatives or/and serving as an auxiliary module for a retriever.
Large-scale retrieval is to recall relevant documents from a huge collection given a query.
(2) How to cohere with context and preserve the knowledge when generating a stylized response.
To address these challenges, we present HeterMPC, a heterogeneous graph-based neural network for response generation in MPCs which models the semantics of utterances and interlocutors simultaneously with two types of nodes in a graph.
To address the problem, we propose augmenting TExt Generation via Task-specific and Open-world Knowledge (TegTok) in a unified framework.
Generating new events given context with correlated ones plays a crucial role in many event-centric reasoning tasks.
This paper focuses on the Data Augmentation for low-resource Natural Language Understanding (NLU) tasks.
A straightforward solution is resorting to more diverse positives from a multi-augmenting strategy, while an open question remains about how to unsupervisedly learn from the diverse positives but with uneven augmenting qualities in the text field.
In such a low-resource setting, we devise a novel conversational agent, Divter, in order to isolate parameters that depend on multimodal dialogues from the entire generation model.
Event correlation reasoning infers whether a natural language paragraph containing multiple events conforms to human common sense.
For this task, the adoption of pre-trained language models (such as BERT) has led to remarkable progress in a number of benchmarks.
Second, only the items mentioned in the training corpus have a chance to be recommended in the conversation.
Procedural text understanding aims at tracking the states (e. g., create, move, destroy) and locations of the entities mentioned in a given paragraph.
Recently, various neural models for multi-party conversation (MPC) have achieved impressive improvements on a variety of tasks such as addressee recognition, speaker identification and response prediction.
That is, we can only access training data in a high-resource language, while need to answer multilingual questions without any labeled data in target languages.
The retriever aims to retrieve a correlated image to the dialog from an image index, while the visual concept detector extracts rich visual knowledge from the image.
We notice that some real-world QA tasks are more complex, which cannot be solved by end-to-end neural networks or translated to any kind of formal representations.
We focus on the task of reasoning over paragraph effects in situation, which requires a model to understand the cause and effect described in a background paragraph, and apply the knowledge to a novel situation.
In this work, we focus on the task of procedural text understanding, which aims to comprehend such documents and track entities' states and locations during a process.
Recent studies on open-domain question answering have achieved prominent performance improvement using pre-trained language models such as BERT.
We consider the problem of conversational question answering over a large-scale knowledge base.
These two problems lead to a poorly-trained semantic parsing model.
To avoid these limitations, in this paper, we propose a new model, which is defined with a coset-permutation distance, and models the generation of a permutation as a stagewise process.