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Jiayi Lin

7 accepted papers

2026

Towards Better Code Understanding in Decoder-Only Models with Contrastive Learning

AAAI 2026technical

Recent advances in large-scale code generation models have led to remarkable progress in producing high-quality code. These models are trained in a self-supervised manner on extensive unlabeled code corpora using a decoder-only architecture. However, despite their generative strength, decoder-only m

Cited by 0SourcePDFScholar
2025

Bold Claims or Self-Doubt? Factuality Hallucination Type Detection via Belief State

EMNLP 2025

Large language models are prone to generating hallucination that deviates from factual information. Existing studies mainly focus on detecting the presence of hallucinations but lack a systematic classification approach, which hinders deeper exploration of their characteristics. To address this, we

Cited by 0SourcePDFScholar
2025

Disentangle to Decay: Linear Attention with Trainable Decay Factor

COLING 2025main

Linear attention enhances inference efficiency of Transformer and has attracted research interests as an efficient backbone of language models. Existing linear attention based models usually exploit decay factor based positional encoding (PE), where attention scores decay exponentially with increasi…

2025

Rhetorical Device-Aware Sarcasm Detection with Counterfactual Data Augmentation

ACL 2025finding

Sarcasm is a complex form of sentiment expression widely used in human daily life. Previous work primarily defines sarcasm as a form of verbal irony, which covers only a subset of real-world sarcastic expressions. However, sarcasm serves multifaceted functions and manifests itself through various rh…

2024

Leveraging Hallucinations to Reduce Manual Prompt Dependency in Promptable Segmentation

NeurIPS 2024poster

Promptable segmentation typically requires instance-specific manual prompts to guide the segmentation of each desired object. To minimize such a need, task-generic promptable segmentation has been introduced, which employs a single task-generic prompt to segment various images of different objects i…

2024

Relax Image-Specific Prompt Requirement in SAM: A Single Generic Prompt for Segmenting Camouflaged Objects

AAAI 2024technical

Camouflaged object detection (COD) approaches heavily rely on pixel-level annotated datasets. Weakly-supervised COD (WSCOD) approaches use sparse annotations like scribbles or points to reduce annotation efforts, but this can lead to decreased accuracy. The Segment Anything Model (SAM) shows remar…