Jingwei Ni

When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP

2023 · SSRN Electronic Journal

Multi-task learning (MTL) aims at achieving a better model by leveraging data and knowledge from multiple tasks. However, MTL does not always work – sometimes negative transfer occurs between tasks, especially when aggregating loosely related …

Balancing Truthfulness and Informativeness with Uncertainty-Aware Instruction Fine-Tuning

2025 · arXiv (Cornell University)

Instruction fine-tuning (IFT) can increase the informativeness of large language models (LLMs), but may reduce their truthfulness. This trade-off arises because IFT steers LLMs to generate responses containing long-tail knowledge that was not well covered …

DIRAS: Efficient LLM Annotation of Document Relevance for Retrieval Augmented Generation

2025

Retrieval Augmented Generation (RAG) is widely employed to ground responses to queries on domain-specific documents. But do RAG implementations leave out important information when answering queries that need an integrated analysis of information (e.g., Tell …