Building cross-model intelligence that can understand charts and communicate the salient
information hidden behind them is an appealing challenge in the vision and language (V+ L) …

Recently, Large Language Models (LLMs) have drawn significant attention due to their
outstanding reasoning capabilities and extensive knowledge repository, positioning them as …

Retrieval Augmented Generation (RAG) improves large language models (LMs) by
incorporating non-parametric knowledge through evidence retrieval from external sources …

Information extraction tasks such as event extraction require an in-depth understanding of
the output structure and sub-task dependencies. They heavily rely on task-specific training …

Cross-document relation extraction (CodRED) task aims to infer the relation between two
entities mentioned in different documents within a reasoning path. Previous studies have …

Dialogue state tracking (DST) is an important step in dialogue management to keep track of
users' beliefs. Existing works fine-tune all language model (LM) parameters to tackle the …

Relation extraction (RE) extracts structured tuples of relationships (eg friend, enemy)
between entities (eg Sherlock Holmes, John Watson) from text, with exciting potential …

Document-level event extraction aims to extract structured event information from
unstructured text. However, a single document often contains limited event information and …

Generative Language Models rely on autoregressive decoding to produce the output
sequence token by token. Many tasks such as preference optimization, require the model to …

Abstract Information extraction tasks such as event extraction require an in-depth
understanding of the output structure and sub-task dependencies. They heavily rely on task …