most citedInvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning

24 citations · 29 across the 5 of their papers we have counts for

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5 papers

cs.CL2024

LLM-Measure: Generating Valid, Consistent, and Reproducible Text-Based Measures for Social Science Research

Yi Yang, Hanyu Duan, Jiaxin Liu +1

The increasing use of text as data in social science research necessitates the development of valid, consistent, reproducible, and efficient methods for generating text-based conce…

cs.CL2024

Beyond Surface Similarity: Detecting Subtle Semantic Shifts in Financial Narratives

Jiaxin Liu, Yi Yang, Kar Yan Tam

In this paper, we introduce the Financial-STS task, a financial domain-specific NLP task designed to measure the nuanced semantic similarity between pairs of financial narratives.…

cs.CL20244 cited

Do LLMs Know about Hallucination? An Empirical Investigation of LLM's Hidden States

Hanyu Duan, Yi Yang, Kar Yan Tam

Large Language Models (LLMs) can make up answers that are not real, and this is known as hallucination. This research aims to see if, how, and to what extent LLMs are aware of hall…

cs.CL20231 cited

Exploring the Relationship between In-Context Learning and Instruction Tuning

Hanyu Duan, Yixuan Tang, Yi Yang +2

In-Context Learning (ICL) and Instruction Tuning (IT) are two primary paradigms of adopting Large Language Models (LLMs) to downstream applications. However, they are significantly…

q-fin.GN202324 cited

InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning

Yi Yang, Yixuan Tang, Kar Yan Tam

We present a new financial domain large language model, InvestLM, tuned on LLaMA-65B (Touvron et al., 2023), using a carefully curated instruction dataset related to financial inve…