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Found 7 papers, 5 papers with code
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.
The FinBen: An Holistic Financial Benchmark for Large Language Models
This along with the rapid development of LLMs, highlights the urgent need for a systematic financial evaluation benchmark for LLMs.
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.
