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Found 22 papers, 17 papers with code
Grounded Persuasive Language Generation for Automated Marketing
This paper develops an agentic framework that employs large language models (LLMs) to automate the generation of persuasive and grounded marketing content, using real estate listing descriptions as our focal application domain.
CryptoX : Compositional Reasoning Evaluation of Large Language Models
In this paper, we introduce CryptoX, an evaluation framework that, for the first time, combines existing benchmarks and cryptographic, to quantify the compositional reasoning capacity of LLMs.
Hello Again! LLM-powered Personalized Agent for Long-term Dialogue
Crucial to addressing this real-world need are event summary and persona management, which enable reasoning for appropriate long-term dialogue responses.
Equipping Transformer with Random-Access Reading for Long-Context Understanding
Long-context modeling presents a significant challenge for transformer-based large language models (LLMs) due to the quadratic complexity of the self-attention mechanism and issues with length extrapolation caused by pretraining exclusively on short inputs.
When Hindsight is Not 20/20: Testing Limits on Reflective Thinking in Large Language Models
In this paper, we set out to clarify these capabilities under a more stringent evaluation setting in which we disallow any kind of external feedback.
Identifying Self-Disclosures of Use, Misuse and Addiction in Community-based Social Media Posts
In the last decade, the United States has lost more than 500, 000 people from an overdose involving prescription and illicit opioids making it a national public health emergency (USDHHS, 2017).
Can You Follow Me? Testing Situational Understanding in ChatGPT
Understanding sentence meanings and updating information states appropriately across time -- what we call "situational understanding" (SU) -- is a critical ability for human-like AI agents.
Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints
The increasing capabilities of large language models (LLMs) raise opportunities for artificial general intelligence but concurrently amplify safety concerns, such as potential misuse of AI systems, necessitating effective AI alignment.
Efficient Shapley Values Estimation by Amortization for Text Classification
In practice, Shapley Values are often estimated with a small number of stochastic model evaluations.
ReCode: Robustness Evaluation of Code Generation Models
Most existing works on robustness in text or code tasks have focused on classification, while robustness in generation tasks is an uncharted area and to date there is no comprehensive benchmark for robustness in code generation.
Improving Stability of Fine-Tuning Pretrained Language Models via Component-Wise Gradient Norm Clipping
Despite its superior performance, such fine-tuning can be unstable, resulting in significant variance in performance and potential risks for practical applications.
The prediction of the quality of results in Logic Synthesis using Transformer and Graph Neural Networks
Furthermore, the Transformer and GNNs are adopted as a joint learning policy for the QoR prediction of the unseen circuit-optimization sequences.
Transformer Embeddings of Irregularly Spaced Events and Their Participants
The neural Hawkes process (Mei & Eisner, 2017) is a generative model of irregularly spaced sequences of discrete events.
Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study
Recent advancements in open-domain question answering (ODQA), i. e., finding answers from large open-domain corpus like Wikipedia, have led to human-level performance on many datasets.
Weakly-Supervised Methods for Suicide Risk Assessment: Role of Related Domains
Social media has become a valuable resource for the study of suicidal ideation and the assessment of suicide risk.
Frustratingly Hard Evidence Retrieval for QA Over Books
A lot of progress has been made to improve question answering (QA) in recent years, but the special problem of QA over narrative book stories has not been explored in-depth.
Enhancing Transformer with Sememe Knowledge
While large-scale pretraining has achieved great success in many NLP tasks, it has not been fully studied whether external linguistic knowledge can improve data-driven models.
Word-level Textual Adversarial Attacking as Combinatorial Optimization
Also, further experiments show our model has higher transferability and can bring more robustness enhancement to victim models by adversarial training.
Modeling Semantic Compositionality with Sememe Knowledge
In this paper, we verify the effectiveness of sememes, the minimum semantic units of human languages, in modeling SC by a confirmatory experiment.
multi-word expression embedding
multi-word expression sememe prediction
COS960: A Chinese Word Similarity Dataset of 960 Word Pairs
Word similarity computation is a widely recognized task in the field of lexical semantics.
OpenHowNet: An Open Sememe-based Lexical Knowledge Base
In this paper, we present an open sememe-based lexical knowledge base OpenHowNet.
Textual Relationship Modeling for Cross-Modal Information Retrieval
A joint neural model is proposed to learn feature representation individually in each modality.
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