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Found 13 papers, 6 papers with code
Cross-model Transferability among Large Language Models on the Platonic Representations of Concepts
Understanding the inner workings of Large Language Models (LLMs) is a critical research frontier.
A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors
To investigate this, we conduct a study on the impact of overall loss on existing time series methods with sequence decomposition.
Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications
We begin with FinLLaMA, pre-trained on a 52 billion token financial corpus, incorporating text, tables, and time-series data to embed comprehensive financial knowledge.
HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection
In this paper, we take a step forward to explore LLMs for tabular data synthesis and privacy protection, by introducing a new framework HARMONIC for tabular data generation and evaluation.
Legend: Leveraging Representation Engineering to Annotate Safety Margin for Preference Datasets
By leveraging this safety direction, Legend can then leverage the semantic distances of paired responses along this direction to annotate margins automatically.
Dishonesty in Helpful and Harmless Alignment
We find that this also induces dishonesty in helpful and harmless alignment where LLMs tell lies in generating harmless responses.
Towards Understanding the Influence of Reward Margin on Preference Model Performance
Reinforcement Learning from Human Feedback (RLHF) is a widely used framework for the training of language models.
Towards Analyzing and Understanding the Limitations of DPO: A Theoretical Perspective
Direct Preference Optimization (DPO), which derives reward signals directly from pairwise preference data, has shown its effectiveness on aligning Large Language Models (LLMs) with human preferences.
FinBen: A Holistic Financial Benchmark for Large Language Models
Our evaluation of 15 representative LLMs, including GPT-4, ChatGPT, and the latest Gemini, reveals several key findings: While LLMs excel in IE and textual analysis, they struggle with advanced reasoning and complex tasks like text generation and forecasting.
Dólares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs Between Spanish and English
We evaluate our model and existing LLMs using FLARE-ES, the first comprehensive bilingual evaluation benchmark with 21 datasets covering 9 tasks.
DREditor: An Time-efficient Approach for Building a Domain-specific Dense Retrieval Model
Motivated by this, we develop a time-efficient approach called DREditor to edit the matching rule of an off-the-shelf dense retrieval model to suit a specific domain.
LAiW: A Chinese Legal Large Language Models Benchmark
Through automated evaluation of current general and legal domain LLMs on our benchmark, we indicate that these LLMs may not align with the logic of legal practice.
Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models
We then propose the first Credit and Risk Assessment Large Language Model (CALM) by instruction tuning, tailored to the nuanced demands of various financial risk assessment tasks.
