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Found 43 papers, 26 papers with code
Automatic True/False Question Generation for Educational Purpose
In field of teaching, true/false questioning is an important educational method for assessing students’ general understanding of learning materials.
KHANQ: A Dataset for Generating Deep Questions in Education
Designing in-depth educational questions is a time-consuming and cognitively demanding task.
Updating Language Models with Unstructured Facts: Towards Practical Knowledge Editing
In this paper, we propose a new benchmark, Unstructured Knowledge Editing (UKE).
A Survey on Data Selection for Language Models
A major factor in the recent success of large language models is the use of enormous and ever-growing text datasets for unsupervised pre-training.
Perils of Self-Feedback: Self-Bias Amplifies in Large Language Models
Recent studies show that self-feedback improves large language models (LLMs) on certain tasks while worsens other tasks.
SciAgent: Tool-augmented Language Models for Scientific Reasoning
To make this task more practical and solvable for LLMs, we introduce a new task setting named tool-augmented scientific reasoning.
Understanding the Reasoning Ability of Language Models From the Perspective of Reasoning Paths Aggregation
To understand how pre-training with a next-token prediction objective contributes to the emergence of such reasoning capability, we propose that we can view an LM as deriving new conclusions by aggregating indirect reasoning paths seen at pre-training time.
Tweets to Citations: Unveiling the Impact of Social Media Influencers on AI Research Visibility
As the number of accepted papers at AI and ML conferences reaches into the thousands, it has become unclear how researchers access and read research publications.
Efficient Online Data Mixing For Language Model Pre-Training
The data used to pretrain large language models has a decisive impact on a model's downstream performance, which has led to a large body of work on data selection methods that aim to automatically determine the most suitable data to use for pretraining.
Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers
The increased use of large language models (LLMs) across a variety of real-world applications calls for mechanisms to verify the factual accuracy of their outputs.
A Survey on Detection of LLMs-Generated Content
The burgeoning capabilities of advanced large language models (LLMs) such as ChatGPT have led to an increase in synthetic content generation with implications across a variety of sectors, including media, cybersecurity, public discourse, and education.
MAF: Multi-Aspect Feedback for Improving Reasoning in Large Language Models
Language Models (LMs) have shown impressive performance in various natural language tasks.
QACHECK: A Demonstration System for Question-Guided Multi-Hop Fact-Checking
Fact-checking real-world claims often requires complex, multi-step reasoning due to the absence of direct evidence to support or refute them.
Towards Verifiable Generation: A Benchmark for Knowledge-aware Language Model Attribution
Although achieving great success, Large Language Models (LLMs) usually suffer from unreliable hallucinations.
Investigating Zero- and Few-shot Generalization in Fact Verification
In this paper, we explore zero- and few-shot generalization for fact verification (FV), which aims to generalize the FV model trained on well-resourced domains (e. g., Wikipedia) to low-resourced domains that lack human annotations.
FOLLOWUPQG: Towards Information-Seeking Follow-up Question Generation
We introduce the task of real-world information-seeking follow-up question generation (FQG), which aims to generate follow-up questions seeking a more in-depth understanding of an initial question and answer.
Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies
Large language models (LLMs) have demonstrated remarkable performance across a wide array of NLP tasks.
INSTRUCTSCORE: Explainable Text Generation Evaluation with Finegrained Feedback
By harnessing both explicit human instruction and the implicit knowledge of GPT-4, we fine-tune a text evaluation metric based on LLaMA, producing both a score for generated text and a human readable diagnostic report.
On the Risk of Misinformation Pollution with Large Language Models
In this paper, we comprehensively investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation and its subsequent impact on information-intensive applications, particularly Open-Domain Question Answering (ODQA) systems.
The Knowledge Alignment Problem: Bridging Human and External Knowledge for Large Language Models
Large language models often necessitate grounding on external knowledge to generate faithful and reliable answers.
Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models
This paper investigates the capabilities of Large Language Models (LLMs) in the context of understanding their own knowledge and measuring their uncertainty.
SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tables
Current scientific fact-checking benchmarks exhibit several shortcomings, such as biases arising from crowd-sourced claims and an over-reliance on text-based evidence.
Fact-Checking Complex Claims with Program-Guided Reasoning
Fact-checking real-world claims often requires collecting multiple pieces of evidence and applying complex multi-step reasoning.
Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning
We also introduce a self-refinement module, which utilizes the symbolic solver's error messages to revise symbolic formalizations.
Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generation
In addition, we propose Conv-Distinct, a novel evaluation metric for CQG, to evaluate the diversity of the generated conversation from a context.
InfoCTM: A Mutual Information Maximization Perspective of Cross-Lingual Topic Modeling
Instead of the direct alignment in previous work, we propose a topic alignment with mutual information method.
Hashtag-Guided Low-Resource Tweet Classification
Social media classification tasks (e. g., tweet sentiment analysis, tweet stance detection) are challenging because social media posts are typically short, informal, and ambiguous.
Modeling and Leveraging Prerequisite Context in Recommendation
Prerequisites can play a crucial role in users' decision-making yet recommendation systems have not fully utilized such contextual background knowledge.
CoHS-CQG: Context and History Selection for Conversational Question Generation
While previous studies mainly focus on how to model the flow and alignment of the conversation, there has been no thorough study to date on which parts of the context and history are necessary for the model.
Attacking Open-domain Question Answering by Injecting Misinformation
We curate both human-written and model-generated false documents that we inject into the evidence corpus of QA models and assess the impact on the performance of these systems.
Interpreting the Robustness of Neural NLP Models to Textual Perturbations
In this work, we test the hypothesis that the extent to which a model is affected by an unseen textual perturbation (robustness) can be explained by the learnability of the perturbation (defined as how well the model learns to identify the perturbation with a small amount of evidence).
Zero-shot Fact Verification by Claim Generation
However, for each new domain that requires fact verification, creating a dataset by manually writing claims and linking them to their supporting evidence is expensive.
Exploring Question-Specific Rewards for Generating Deep Questions
Recent question generation (QG) approaches often utilize the sequence-to-sequence framework (Seq2Seq) to optimize the log-likelihood of ground-truth questions using teacher forcing.
Unsupervised Multi-hop Question Answering by Question Generation
Obtaining training data for multi-hop question answering (QA) is time-consuming and resource-intensive.
Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment
GNN-based EA methods present promising performances by modeling the KG structure defined by relation triples.
Multi-modal Cooking Workflow Construction for Food Recipes
Understanding food recipe requires anticipating the implicit causal effects of cooking actions, such that the recipe can be converted into a graph describing the temporal workflow of the recipe.
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base
To this end, we introduce KQA Pro, a dataset for Complex KBQA including ~120K diverse natural language questions.
Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen
The curse of knowledge can impede communication between experts and laymen.
Semantic Graphs for Generating Deep Questions
This paper proposes the problem of Deep Question Generation (DQG), which aims to generate complex questions that require reasoning over multiple pieces of information of the input passage.
Recent Advances in Neural Question Generation
Emerging research in Neural Question Generation (NQG) has started to integrate a larger variety of inputs, and generating questions requiring higher levels of cognition.
Resource Mention Extraction for MOOC Discussion Forums
We propose the novel problem of learning resource mention identification in MOOC forums.
Course Concept Extraction in MOOCs via Embedding-Based Graph Propagation
Massive Open Online Courses (MOOCs), offering a new way to study online, are revolutionizing education.
Prerequisite Relation Learning for Concepts in MOOCs
What prerequisite knowledge should students achieve a level of mastery before moving forward to learn subsequent coursewares?
