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Explicit Inductive Inference using Large Language Models
Tianyang Liu, Tianyi Li, Liang Cheng +1
Large Language Models (LLMs) are reported to hold undesirable attestation bias on inference tasks: when asked to predict if a premise P entails a hypothesis H, instead of consideri…
A Usage-centric Take on Intent Understanding in E-Commerce
Wendi Zhou, Tianyi Li, Pavlos Vougiouklis +2
Identifying and understanding user intents is a pivotal task for E-Commerce. Despite its essential role in product recommendation and business user profiling analysis, intent under…
Sources of Hallucination by Large Language Models on Inference Tasks
Nick McKenna, Tianyi Li, Liang Cheng +3
Large Language Models (LLMs) are claimed to be capable of Natural Language Inference (NLI), necessary for applied tasks like question answering and summarization. We present a seri…
Language Models Are Poor Learners of Directional Inference
Tianyi Li, Mohammad Javad Hosseini, Sabine Weber +1
We examine LMs' competence of directional predicate entailments by supervised fine-tuning with prompts. Our analysis shows that contrary to their apparent success on standard NLI,…
Cross-lingual Inference with A Chinese Entailment Graph
Tianyi Li, Sabine Weber, Mohammad Javad Hosseini +2
Predicate entailment detection is a crucial task for question-answering from text, where previous work has explored unsupervised learning of entailment graphs from typed open relat…
Incorporating Textual Evidence in Visual Storytelling
Tianyi Li, Sujian Li
Previous work on visual storytelling mainly focused on exploring image sequence as evidence for storytelling and neglected textual evidence for guiding story generation. Motivated…