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Found 30 papers, 22 papers with code
SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages
Large Language Models (LLMs) have shown remarkable abilities across various tasks, yet their development has predominantly centered on high-resource languages like English and Chinese, leaving low-resource languages underserved.
Unlocking Varied Perspectives: A Persona-Based Multi-Agent Framework with Debate-Driven Text Planning for Argument Generation
The results show that our framework can generate more diverse and persuasive arguments through both automatic and human evaluations.
From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models
This survey paper serves as a comprehensive resource for researchers and practitioners in the fields of natural language processing, computer vision, and data analysis, providing valuable insights and directions for future research in chart understanding leveraging large foundation models.
Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement
The increasing demand for personalized interactions with large language models (LLMs) calls for methodologies capable of accurately and efficiently identifying user opinions and preferences.
Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate
Fact-checking research has extensively explored verification but less so the generation of natural-language explanations, crucial for user trust.
Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning
This work inaugurates a new domain in factual error correction for chart captions, presenting a novel evaluation mechanism, and demonstrating an effective approach to ensuring the factuality of generated chart captions.
Factual Inconsistency Detection in Chart Captioning
Image Captioning
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AMERICANO: Argument Generation with Discourse-driven Decomposition and Agent Interaction
Argument generation is a challenging task in natural language processing, which requires rigorous reasoning and proper content organization.
Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting
Automatic response forecasting for news media plays a crucial role in enabling content producers to efficiently predict the impact of news releases and prevent unexpected negative outcomes such as social conflict and moral injury.
Measuring the Effect of Influential Messages on Varying Personas
Our analysis shows that the best-performing models are capable of predicting responses that are consistent with the personas, and as a byproduct, the task formulation also enables many interesting applications in the analysis of social network groups and their opinions, such as the discovery of extreme opinion groups.
Scientific Opinion Summarization: Paper Meta-review Generation Dataset, Methods, and Evaluation
To address this gap, we propose the task of scientific opinion summarization, where research paper reviews are synthesized into meta-reviews.
Interpretable Automatic Fine-grained Inconsistency Detection in Text Summarization
Motivated by how humans inspect factual inconsistency in summaries, we propose an interpretable fine-grained inconsistency detection model, FineGrainFact, which explicitly represents the facts in the documents and summaries with semantic frames extracted by semantic role labeling, and highlights the related semantic frames to predict inconsistency.
ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media
We present a novel task, identifying manipulation of news on social media, which aims to detect manipulation in social media posts and identify manipulated or inserted information.
Zero-shot Faithful Factual Error Correction
Faithfully correcting factual errors is critical for maintaining the integrity of textual knowledge bases and preventing hallucinations in sequence-to-sequence models.
Can LMs Generalize to Future Data? An Empirical Analysis on Text Summarization
Moreover, the knowledge memorized by PLMs may quickly become outdated, which affects the generalization performance of PLMs on future data.
PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets Stream
Summarizing text-rich documents has been long studied in the literature, but most of the existing efforts have been made to summarize a static and predefined multi-document set.
SumREN: Summarizing Reported Speech about Events in News
A primary objective of news articles is to establish the factual record for an event, frequently achieved by conveying both the details of the specified event (i. e., the 5 Ws; Who, What, Where, When and Why regarding the event) and how people reacted to it (i. e., reported statements).
MOCHA: A Multi-Task Training Approach for Coherent Text Generation from Cognitive Perspective
Teaching neural models to generate narrative coherent texts is a critical problem.
Multimedia Generative Script Learning for Task Planning
Goal-oriented generative script learning aims to generate subsequent steps to reach a particular goal, which is an essential task to assist robots or humans in performing stereotypical activities.
PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation
Despite recent progress of pre-trained language models on generating fluent text, existing methods still suffer from incoherence problems in long-form text generation tasks that require proper content control and planning to form a coherent high-level logical flow.
Grounding Commands for Autonomous Vehicles via Layer Fusion with Region-specific Dynamic Layer Attention
In this work, we study the problem of language grounding for autonomous vehicles, which aims to localize a region in a visual scene according to a natural language command from a passenger.
Controllable Summarization with Constrained Markov Decision Process
We study controllable text summarization which allows users to gain control on a particular attribute (e. g., length limit) of the generated summaries.
Dialogue Summarization with Supporting Utterance Flow Modeling and Fact Regularization
The supporting utterance flow modeling helps to generate a coherent summary by smoothly shifting the focus from the former utterances to the later ones.
A Condense-then-Select Strategy for Text Summarization
This framework first selects salient sentences and then independently condenses each of the selected sentences into a concise version.
A Unified Dual-view Model for Review Summarization and Sentiment Classification with Inconsistency Loss
Review summarization aims at generating a concise summary that describes the key opinions and sentiment of a review, while sentiment classification aims to predict a sentiment label indicating the sentiment attitude of a review.
Exclusive Hierarchical Decoding for Deep Keyphrase Generation
A new setting is recently introduced into this problem, in which, given a document, the model needs to predict a set of keyphrases and simultaneously determine the appropriate number of keyphrases to produce.
Topic-Aware Neural Keyphrase Generation for Social Media Language
Further discussions show that our model learns meaningful topics, which interprets its superiority in social media keyphrase generation.
Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards
To address this problem, we propose a reinforcement learning (RL) approach for keyphrase generation, with an adaptive reward function that encourages a model to generate both sufficient and accurate keyphrases.
An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction
For further exploiting the power of extraction and retrieval, we propose a neural-based merging module to combine and re-rank the predicted keyphrases from the enhanced generative model, the extractive model, and the retrieved keyphrases.
Thread Popularity Prediction and Tracking with a Permutation-invariant Model
This task has been formulated as a reinforcement learning problem, in which the reward of the agent is the sum of positive responses received by the recommended comments.
